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EAI Endorsed Transactions on Scalable Information Systems

EAI Endorsed Transactions on Scalable Information Systems is an open access, peer-reviewed scholarly journal focused on scalable distributed information system, scalable, data mining, grid information systems and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency. Authors are not charged for article submission and processing.

Editor(s)-in-Chief: Hua Wang, Xiaohua Jia and Manik Sharma

As the data volumes continue to increase and the ways of information dispersion across the globe continue to diversify, new scalable methods and structures are needed for efficiently processing those distributed and autonomous data. Grid computing, P2P technology, distributed information retrieval technology and networking technology all must be merged to address the scalability concern.

The scope of the journal includes:

  • Scalable distributed information systems
  • Scalable grid information systems
  • Parallel information processing and systems
  • Web information searching and retrieval
  • Data mining
  • Content delivery networks (CDN)
  • VLDB
  • P2P systems
  • Scalable mobile and wireless database systems
  • Large scale sensor network systems
  • Index compression methods
  • Architectures for scalability
  • Scalable information system applications
  • Evaluation metrics for scalability
  • Information security
  • Ajay Kattepur (Tata Consultancy Services)
  • Aniello Castiglione (University of Salerno)
  • Chang Choi (Chosun University)
  • Cho-Li Wang (University of Hong Kong)
  • Daniel S. Katz (University of Chicago)
  • Fabrizio Silvestri (ISTI – CNR, Italy)
  • Giancarlo Fortino (University of Calabria)
  • Hamed Taherdoost (Hamta Business Solution Snd)
  • Heng Tao Shen (University of Queensland)
  • Houbing Song (Embry-Riddle Aeronautical University)
  • José Manuel Machado (University of Minho, Portugal)
  • Jose Merseguer (Universidad de Zaragoza)
  • Jie Li (University of Tsukuba)
  • Lin Yun (Harbin Engineering University)
  • Phan Cong Vinh (Nguyen Tat Thanh University)
  • Raj Gururajan (University of Southern Queensland)
  • Sherman Chow (Chinese University of Hong Kong)
  • Silva Fábio (University of Minho, Portugal)
  • Steve Beitzel (Telcordia)
  • Tuan Phung-Duc (University of Tsukuba, JP)
  • Tzung-Pei Hong (National University of Kaohsiung, Kaohsing City, Taiwan)
  • Wang-Chien Lee (The Pennsylvania State University)
  • Weili Wu (The University of Texas at Dallas)
  • Xueyan Tang (Nanyang Technological University)
  • Vijayakumar Ponnusamy (SRM University, India)
  • J Amudhavel (KL University, India)
  • Yingshu Li (Georgia State University)
  • Jerry Chun-Wei Lin (Western Norway University of Applied Sciences, Norway)
  • Karolj Skala (Ruđer Bošković Institute, Croatia)
  • Xiao-Zhi Gao (University of Eastern Finland, Finland)
  • Thaier Hayajneh (Fordham University, USA)
  • Chin-Ling Chen (Chaoyang University of Technology, Taiwan)
  • Nuno M. Garcia (Universidade da Beira Interior, Portugal)
  • Arianna D'Ulizia (Consiglio Nazionale delle Ricerche (CNR), Italy)
  • Robertas Damaševičius (Kaunas University of Technology (KTU), Lithuania)
  • Hiep Xuan Huynh (Can Tho University, VietNam)
  • Dongming Li (Nanjing University of Posts and Telecommunications (NUPT), China)
  • Vincent A. Cicirello (Stockton University, USA)
  • Ji Zhang (University of Southern Queensland, Australia)
  • Xiaohui Tao (University of Southern Queensland, Australia)
  • Ye Wang (National University of Defense Technology, China)

Hua Wang1

Hua Wang is now a full-time Professor at Victoria University. He was a professor at the University of Southern Queensland before he joined Victoria University in 2014. Hua has more than ten years teaching and working experience in Applied Informatics at both enterprise and university. He has expertise in Big Data, Health Informatics, Cloud Computing, Cyber Security, Artificial Intelligence. As a Chief Investigator, Hua has successfully received four Australian Research Council (ARC) Discovery grants and two Linkage grants since 2006. He is also a successor for international grants such as two Japan – Australian grants, one German – Australian grant, one Norway Government grant and grants from Hong Kong Chinese University, Hong Kong City University. Hua has published 258 refereed scholar papers in data security, access control, data mining, database, privacy preserving and Web services. Representative publications are on ACM Transactions on Internet Technology, ACM Transactions on Information Systems, ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Automation Science and Engineering and World Wide Web; and Proceedings of ACL, AAAI, CIKM, ICDE, ICDM and PAKDD.

Xiaohua Jia2

Xiaohua Jia received his BSc (1984) and MEng (1987) from University of Science and Technology of China, and DSc (1991) in Information Science from University of Tokyo. He is currently Chair Professor with Dept of Computer Science at City University of Hong Kong. His research interests include cloud computing and distributed systems, data security and privacy, computer networks and mobile computing. Prof. Jia is an editor of IEEE Trans. on Parallel and Distributed Systems (2006-2009), IEEE Internet of Things (2013-2018), Journal of World Wide Web, Journal of Combinatorial Optimization, etc. He is the General Chair of ACM MobiHoc 2008, TPC Co-Chair of IEEE GlobeCom 2010 – Ad Hoc and Sensor Networking Symp, Area-Chair of IEEE INFOCOM 2015-2017, Track-Chair of IEEE ICDCS 2019, and General Chair of ACM ICN 2019. He is a Fellow of IEEE (Computer Society) and Distinguished Member of ACM.

Manik Sharma3

Dr. Manik Sharma is an MCA, UGC-NET qualified and has received his Ph.D. degree in “Distributed Query Optimization”. He has more than 12 years of teaching experience. He is working in the area of Data Mining, Machine Learning and Soft Computing with specialization in the diagnosis of diseases and modeling of DSS query optimizer. The thrust area is to improve the predictive accuracy in early diagnosis of several fatal human disorders. He has published 3 books related to Computer Network, Internet Applications, Management Information System and Database Theory. In addition, he has also published more than 25 research papers in various journals of International repute including Scopus and Thomson Reuters. He has also presented more than ten research articles in various national and international UGC and IEEE conferences. Moreover, he is an active member of International Program Committees, Technical Program Committees, and Advisory Committees of several academics conferences (United Kingdom, Singapore, Mauritius, Thailand, Vietnam, USA, Hong Kong, Canada, UAE, and India).


1: Victoria University, Australia
2: City University of Hong Kong
3: DAV University, India

  • A Game Theoretical Model for Anticipating Email Spear-Phishing Strategies

    Appears in:
    sis 18: e32

    Authors:
    Franklin Tchakounte, Virgile Simé Nyassi, Duplex Elvis Houpa Danga, Kalum Priyanath Udagepola, Marcellin Atemkeng

    Published:
    18th Sep 2020

    Abstract:
    A solution to help victims against phishing is anticipating and leveraging impacts related to phisher actions.In this regard, this work reshapes game theoretical logic between Intrusion Detection System (IDS) agents andinsiders to email spear-phishing interactions. The email spear-phishing attack i…A solution to help victims against phishing is anticipating and leveraging impacts related to phisher actions.In this regard, this work reshapes game theoretical logic between Intrusion Detection System (IDS) agents andinsiders to email spear-phishing interactions. The email spear-phishing attack is designed as a non-cooperativeand repeated game between opponents. Additionally, this work relies on Quantal Response Equilibrium (QRE)to build a game theoretical approach to predict the phisher’s future intent based on past actions of bothplayers. This approach is coupled with a recommendation strategy of appropriate allocation of resources toinvest to strengthen user protection. Simulations on spear-phishing scenarios demonstrate the ability of thefinal system to intuitively guess the most likely phisher decisions. This work provides intelligence to spear-phishing detectors and humans such that they can anticipate next phisher actions.
     more »

  • Challenges of Complying with Data Protection and Privacy Regulations

    Appears in:
    sis 18: e31

    Authors:
    A. M. Lonzetta, T. Hayajneh

    Published:
    18th Sep 2020

    Abstract:
    As we move into a more digitized society, the collection and use of data continues to increase. This influx in data, partnered with challenges complying with data protection and privacy regulations and the absence of a comprehensive global data protection and privacy strategy, ha…As we move into a more digitized society, the collection and use of data continues to increase. This influx in data, partnered with challenges complying with data protection and privacy regulations and the absence of a comprehensive global data protection and privacy strategy, has contributed to data breaches and data misusage. In order to reduce these incidents, updates must be made to existing regulations and included in future regulations. A global agency should also be created to identify the main data protection and privacy objectives to develop a comprehensive strategy and oversee data protectionand privacy. Our paper presents an overview of existing data protection and privacy regulations, the challenges of complying with the regulations, and recommendations to achieve long-term data protect and privacy.
     more »

  • Scaffolding computer programming languages learning with tailored English vocabulary based on learners' performance states

    Appears in:
    sis 18: e30

    Authors:
    Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq, Arsalan Ali Raza

    Published:
    18th Sep 2020

    Abstract:
    Due to the ubiquitous nature of mobile devices, they are now considered as an emerging platform for facilitating both teaching and learning experiences. In this paper, we presented a tailored mobile learning system, namely the Integrated English and Programming Language Learning System (IEPLS), whi…Due to the ubiquitous nature of mobile devices, they are now considered as an emerging platform for facilitating both teaching and learning experiences. In this paper, we presented a tailored mobile learning system, namely the Integrated English and Programming Language Learning System (IEPLS), which aims at learning English vocabulary before studying programming language concepts. The IEPLS supports programming language learning in three ways; (a) Recommending to learn specific English vocabulary used in programming language concepts (b) Adaptation to the learning flow of the learner and (c) Motivating and encouraging learners to learn items based on individual learner's performance. The IEPLS was used by one hundred and fifty undergraduate students for six months. Evaluation results revealed the attainment of IEPLS in supporting learners in learning programming languages backed by precise English vocabulary.
     more »

  • Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

    Appears in:
    sis 18: e29

    Authors:
    Sandeep U. Mane, Amol C. Adamuthe, Rajshree R. Omane

    Published:
    9th Sep 2020

    Abstract:
    INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance …INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance and system utilization for CEC2006 single-objective benchmark functions. METHODS: The proposed approach implemented using OpenMP and CUDA C, a hybrid programming approach to enhance the utilization of the system’s computational resources. The device utilization and performance of the proposed approach evaluated using CEC2006 benchmark functions. RESULTS: The proposed approach obtains best results in significantly reduced time for CEC2006 benchmark functions. The maximum speed-up achieved is 30.14X. The average GPGPU utilization is 90% and the average utilization of logical processors is more than 90%. CONCLUSION: The master-slave TLBO algorithm improves the utilization of computational resources significantly and obtains the best results for CEC2006 benchmark functions.
     more »

  • Cross-domain sentiment classification initiated with Polarity Detection Task

    Appears in:
    sis 18: e28

    Authors:
    Nancy Kansal, Lipika Goel, Sonam Gupta

    Published:
    11th Aug 2020

    Abstract:
    INTRODUCTION: The requirement of the labeled dataset in the source domain makes the Cross Domain Sentiment Classification (CDSC) task complicate in the situation when the dataset is labeled manually. OBJECTIVES: To overcome the dependency of CDSC tasks on manual labeling of the dataset by proposi…INTRODUCTION: The requirement of the labeled dataset in the source domain makes the Cross Domain Sentiment Classification (CDSC) task complicate in the situation when the dataset is labeled manually. OBJECTIVES: To overcome the dependency of CDSC tasks on manual labeling of the dataset by proposing a polarity detection task. METHODS: We have proposed the CDSC-PDT method that is the polarity Detection Task (PDT) followed by the CDSC task. The proposed PDT task extracts the polarity of reviews from the source domain using the contextual and relevancy information of words in documents and this automatic labeled dataset is further used to train classifiers to make the further classification. RESULTS: Proposed method is comparable to the traditional learning method giving the highest precision 85.7%. CONCLUSION: The proposed method does not need to manually label the documents in either of the domain (source or target), hence it overcomes the human intervention and is also time saving and cheap process, unlike traditional CDSC tasks.
     more »

  • Effects of data protection laws on data brokerage businesses

    Appears in:
    sis 18: e27

    Authors:
    G. Birckan, M. L. Dutra, D. D. J. de Macedo, A. F. G. Viera

    Published:
    23rd Jul 2020

    Abstract:
    For years now, players known as data brokers have operated on an unregulated market, by building businesses from the aggregation and processing of data, away from public scrutiny. Due to the genesis of laws that ended up exposing their model, along with debates on data privacy, a new generation o…For years now, players known as data brokers have operated on an unregulated market, by building businesses from the
    aggregation and processing of data, away from public scrutiny. Due to the genesis of laws that ended up exposing their
    model, along with debates on data privacy, a new generation of data protection acts, much stricter, are being enacted
    worldwide. The shift away from the data commoditization paradigm might, at first, indicate the extinction of such businesses.
    In this paper, we present related concepts and a brief history that led to that current scenario, along with some collaborativeoriented proposals for the adequacy of the data brokerage industry to a transparent perspective.
     more »

  • An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews

    Appears in:
    sis 18: e26

    Authors:
    A. K. Yadav, D. Yadav, A. Jain

    Published:
    22nd Jul 2020

    Abstract:
    In today’s society, sentiment analysis has gained due importance as it provides useful information about products that are used by variety of users. It gives a sneak peek of users’ reactions towards the products that are available in the market at an early stage. It thus intimates users’ percepti…In today’s society, sentiment analysis has gained due importance as it provides useful information about products that are
    used by variety of users. It gives a sneak peek of users’ reactions towards the products that are available in the market at an
    early stage. It thus intimates users’ perception and charts out a path that is beneficial for the market to grow as a whole.
    Although a lot of research is done to exploit the product based sentiment analysis but due to increase demand of the
    detailed components based products and their associated features, a novel method is desired to meet these criteria. So far,
    no such method is explored that analyses the product’s components and their features simultaneously, on the basis of
    sentiments of the users. This paper proposes an improvised Feature Based Algorithm (FBA) for the sentiment analysis of
    product reviews while formulating a tree structure of product, components, and associated features. In addition, evaluation
    of double negative sentences, detecting questions and emotions from the review sentences are measured which increases
    efficiency of the FBA method. The comparison of product’s components reviews is done with other existing algorithmsTF, TF-IDF and Naïve Bayes to demonstrate that the proposed FBA is coherent and auspicious.
     more »

  • ANFIS based hybrid approach identifying correlation between decision making and online social networks

    Appears in:
    sis 18: e25

    Authors:
    Areeba Rahman, Nageen Saleem, Aysha Shabbir, Maryam Shabbir, Muhammad Rizwan, Shahid Naseem, Fahad Ahmad

    Published:
    22nd Jul 2020

    Abstract:
    The fast-growing use of online social networks (OSNs) has prompted stakeholders to change their market strategies and hence have raised several questions on the users' decision making (DM). OSNs, as being regularly used, have resulted in playing a significant role in supporting consumer’s rationa…The fast-growing use of online social networks (OSNs) has prompted stakeholders to change their market strategies and hence
    have raised several questions on the users' decision making (DM). OSNs, as being regularly used, have resulted in playing a
    significant role in supporting consumer’s rational DM. We use a hybrid approach i.e. an online survey and fuzzy model
    development to indicate how OSNs have resulted in giving an impact on the decision making. The research depicts that OSNs
    support and empower users in the DM process specifically in Rationality, Design, & Choice and the model predicts the DM
    strategy of each user according to their present decisions using fuzzy logic. Our results also reveal that different types of users
    (observers, seekers, and advisers) have significantly different participation styles, which in turn have an impact on the efficacy
    of the DM process. We discussed the implications for OSN designers and developers based on the findings from the research.
     more »

  • Revealing the Demonstration of Blockchain and Implementing Scope in COVID-19 Outbreak

    Appears in:
    sis 18: e24

    Authors:
    Mansi Gupta, Vimal Kumar

    Published:
    15th Jul 2020

    Abstract:
    Blockchain is gaining attention since its development due to its features like immutability, transparency, decentralization. It can also be used as decentralized storage. Blockchain offers a wide range of digital services both in financial and nonfinancial sectors. In this paper, we discuss the ba…Blockchain is gaining attention since its development due to its features like immutability, transparency, decentralization.
    It can also be used as decentralized storage. Blockchain offers a wide range of digital services both in financial and nonfinancial sectors. In this paper, we discuss the basic concepts and terminologies associated with blockchain and discuss the
    application areas both in financial and non- financial sectors such as Digital voting, Digital identity, Education, etc. and
    discuss the existing framework in these areas, also the progress and challenges of global acquisition. The main highlight of
    this paper is COVID-19 and we discussed existing solutions to fight the COVID-19 pandemic by using such technology.
    This survey paper will be helpful for a data scientist as well as researchers to explore in the area COVID-19.
     more »

  • A lightweight technique for detection and prevention of wormhole attack in MANET

    Appears in:
    sis 18: e23

    Authors:
    Zulfiqar Ali Zardari, Kamran Ali Memon, Reehan Ali Shah, Sanaullah Dehraj, Iftikhar Ahmed

    Published:
    8th Jul 2020

    Abstract:
    A mobile ad-hoc network (MANET) is an ordinary and self-orbiting communication network that is capable of managing mobile nodes. Many proposed protocols on MANET address its vulnerability against different threats and attacks. The malicious node exploits these vulnerabilities to lunch attacks, es…A mobile ad-hoc network (MANET) is an ordinary and self-orbiting communication network that is capable of managing
    mobile nodes. Many proposed protocols on MANET address its vulnerability against different threats and attacks. The
    malicious node exploits these vulnerabilities to lunch attacks, especially when nodes have mobility and network do not have
    constant topology, like wormhole attack. This paper presents a lightweight technique that detects the wormhole attacks in
    MANET. In the proposed technique, the source node calculates the average sequence number of the reply (RREP) packets.
    If the sequence number of the corresponding node exceeds the calculated average value of the sequence number, then all
    traffic is discarded, and the node is marked as malicious. The proposed technique is less complex, power-efficient, and
    enhances network lifetime as more data packets are delivered to the destination node. This technique is validated through
    comprehensive simulations results in NS2.
     more »

  • Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods

    Appears in:
    sis 18: e22

    Authors:
    Shiva Shankar Reddy, Nilambar Sethi, R. Rajender

    Published:
    1st Jul 2020

    Abstract:
    Diabetic retinopathy is a diabetes complication that effects eyes. It disrupts the vasculature of the sensitive tissue present at the back of the eye. If this complication is untreated it may lead to blindness. The aim of this work is to train a model that efficiently predicts diabetic retinopath…Diabetic retinopathy is a diabetes complication that effects eyes. It disrupts the vasculature of the sensitive tissue present at
    the back of the eye. If this complication is untreated it may lead to blindness. The aim of this work is to train a model that
    efficiently predicts diabetic retinopathy. Machine learning techniques like Decision tree, Random forest, Adaptive
    boosting and Bagging are used as primary algorithms to train predictive models. An algorithm namely ‘Support Vector
    Machine using Gaussian kernel for retinopathy prediction’ is proposed in this work. The proposed algorithm is compared
    with the primary algorithms based on five evaluation metrics namely accuracy, Youden’s J index, concordance, Somers’ D
    statistic and balanced accuracy. From the results obtained the proposed algorithm obtained better values for all considered
    evaluation metrics. Thus the use of SVM with Gaussian kernel is proposed to be used for prediction of diabetic
    retinopathy.
     more »

  • Enhancing Security of Android Operating System Based Phones using Quantum Key Distribution

    Appears in:
    sis 18: e21

    Authors:
    Nageen Saleem, Areeba Rahman, Muhammad Rizwan, Shahid Naseem, Fahad Ahmad

    Published:
    15th Jun 2020

    Abstract:
    The Android-based devices are gaining popularity now a day. With the widespread use of smartphones both in private and work-related areas, securing these devices has become of paramount importance. These devices are prone to various security issues of malicious attacks and performance problems. O…The Android-based devices are gaining popularity now a day. With the widespread use of smartphones both in private and
    work-related areas, securing these devices has become of paramount importance. These devices are prone to various
    security issues of malicious attacks and performance problems. Owners use their smartphones to perform tasks ranging
    from everyday communication with friends and family to the management of banking accounts and accessing sensitive
    work-related data. These factors, combined with limitations in administrative device control through owners and securitycritical applications, make Android-based smartphones a very attractive target for attackers and malware authors of any
    kind and motivation. Applications keep and manage diverse intrinsic data as well as sensitive private information such as
    address books. Smartphones enable swift and easy data exchange via 3G, 4G, and Wi-Fi. Thus, personal information
    stored on smartphones is prone to leakage. Up until recently, the Android Operating System’s security model has
    succeeded in preventing any significant attacks by malware. This can be attributed to a lack of attack vectors which could
    be used for self-spreading infections and low sophistication of malicious applications. The research provides a distinctive
    solution to the security threats being found in the Android operating system. This paper presents a data security and quality
    enhancement method based on amalgamating quantum attributes into the Android operating system that could effectively
    solve the issue raised. The paper provides a proposed architecture of Quantum Key distribution being embedded within the
    Android OS to improve efficiency. However, QKD is a new technology. The research unleashes the possible ways in
    which quantum could be effectively embedded in smartphones to resolve certain data security problems. Quantum key
    distribution implements the Android to guard and use in the case of a run-time kernel compromise. That is, even with a
    fully compromised kernel, an attacker cannot read key material stored in Quantum key.
     more »

  • An Automatic Conversion of Punjabi Text to Indian Sign Language

    Appears in:
    sis 18: e20

    Authors:
    Amandeep Singh Dhanjal, Williamjeet Singh

    Published:
    12th Jun 2020

    Abstract:
    An escalating focus worldwide on the development of persons with disabilities results in the disability-inclusive 2030 UN agenda for sustainable development. This paper discusses the development of Punjabi text to Indian Sign Language (ISL) conversion system aiming for the better communication an…An escalating focus worldwide on the development of persons with disabilities results in the disability-inclusive 2030 UN
    agenda for sustainable development. This paper discusses the development of Punjabi text to Indian Sign Language (ISL)
    conversion system aiming for the better communication and education of hearing-impaired people. The developed system
    accepts Punjabi text as input and deliver output as 3D animation. The currently created corpus involves more than 100
    commonly used Punjabi words under various categories. This system has been tested on all the prepared words. Results of
    the developed system are very encouraging and the work is still going on with full enthusiasm. Since there is no universal
    or written form of sign language available, so generating synthetic sign animation is the best solution. In near future it can
    be implemented as a tutoring system to promote sign language education among common people of Punjab.
     more »

  • Performance Evaluation of Symmetrical Encryption Algorithms with Wavelet Based Compression Technique

    Appears in:
    sis 18: e19

    Authors:
    Neetu Gupta, Ritu Vijay, Hemant Kumar Gupta

    Published:
    28th May 2020

    Abstract:
    To overcome the different issues connected with security and limited bandwidth during transmission of images, compression and encryption play an important role. To measure the effect on the performance of compression technique followed by encryption techniques is a challenging task. In this paper, …To overcome the different issues connected with security and limited bandwidth during transmission of images, compression and encryption play an important role. To measure the effect on the performance of compression technique followed by encryption techniques is a challenging task. In this paper, biorthogonal discrete wavelet transform (DWT) technique is proposed for compression which is followed by advanced encryption standard (AES) and data encryption standard (DES) technologies to achieve secure transmission of data. The performances of DWT-AES and DWT-DES Compression-encryption (CE) algorithms are analyzed based on statistical and differential parameters. We illustrate the authentication of results by applying the proposed CE algorithms over five standard test images and also by comparing with different state of the art methods. The results show that combination of DWT-DES CE methodology provides high quality of reconstructed image with robustness against different attacks.
     more »

  • A Visualization Based Analysis on Dynamic Bandwidth Allocation Algorithms for Optical Networks

    Appears in:
    sis 18: e18

    Authors:
    Noor ul Ain, Kamran A. Memon, Muhammad Abbas, Nazish Nawaz Hussaini, Zulifqar Ali Bhutto, Kamlesh Kumar Soothar

    Published:
    21st May 2020

    Abstract:
    This paper presents the bibliometric analysis of Dynamic Bandwidth Allocation (DBA) algorithms in the telecommunication field using CiteSpace and Web of Science (WoS) analysis for the period of decade (2009-2018). The publication count and citation counts in the last five years have significantly i…This paper presents the bibliometric analysis of Dynamic Bandwidth Allocation (DBA) algorithms in the telecommunication field using CiteSpace and Web of Science (WoS) analysis for the period of decade (2009-2018). The publication count and citation counts in the last five years have significantly increased reflecting this as active research. The visualization results identified the most influential DBA algorithms centered on 1) EPON technology addressing DBAs meeting QoS requirement (68 counts), 2) DBAs for Elastic Optical Networks (58 counts) and (3 DBAs for long reach PON (54 counts). Assi CM and Kramer G are the leading contributors and Plos One and Journal of optical communication and networking are proffered Journals. The correlation between the research contribution and expenditures countries do is 0.945. Wherein, China in countries and Beijing university of Posts and Telecommunications among research institutes contribute the most in the DBA PON research across the world.
     more »

  • The Design of Wireless Portable Electrocardiograph Monitoring System Based on ZigBee

    Appears in:
    sis 18: e17

    Authors:
    Saleemullah Memon, Kamlesh Kumar Soothar, Kamran Ali Memon, Arif Hussain Magsi, Asif Ali Laghari, Muhammad Abbas, Noor ul Ain

    Published:
    14th May 2020

    Abstract:
    The emerging trend in wireless hospitals requires the development of bio-signal acquisition devices to be easily integrated into a single clinical routine. The wired devices used in hospitals and clinics, which makes setup very inconvenient, costly, and difficult to manage patients. It is crucial t…The emerging trend in wireless hospitals requires the development of bio-signal acquisition devices to be easily integrated into a single clinical routine. The wired devices used in hospitals and clinics, which makes setup very inconvenient, costly, and difficult to manage patients. It is crucial to develop and design a clinical diagnostic system that takes bio-signal from the body of the patient and transmits wirelessly to the physicians/hospital. In this paper, we have designed a novel portable system for ECG signal acquisition, processing, and wireless transmission using a ZigBee module. Our portable design enhances mobility, small in size, and very cheap for long term monitoring of cardiac patients. The results show that the ECG wave successfully transmitted to the physician, and heartbeat values are visible to the patient. Furthermore, the results of the ECG wave are unclear when the position of electrodes is dislocated.
     more »

  • Maiden Application of Meta-Heuristic Techniques with Optimized Integral minus Tilt-Derivative Controller for AGC of Multi-area Multi-Source System

    Appears in:
    sis 18: e16

    Authors:
    Sanjeev Kumar Bhagat, Naladi Ram Babu, Lalit Chandra Sakia, Dhenuvakonda Koteswara Raju

    Published:
    14th May 2020

    Abstract:
    This article presents a new meta-heuristic algorithm optimized secondary controller called Integral minus Tilt-Derivative (I-TD) for automatic generation control of three area multi-source system. Area-1 comprises of thermal and solar thermal units, area-2 comprises of two thermal units and area-3 …This article presents a new meta-heuristic algorithm optimized secondary controller called Integral minus Tilt-Derivative (I-TD) for automatic generation control of three area multi-source system. Area-1 comprises of thermal and solar thermal units, area-2 comprises of two thermal units and area-3 comprises of thermal and wind systems. Comparison of system responses using proposed I-TD controller and some other commonly used controller revels better dynamics characteristics of the proposed one. Dynamic responses of the system corresponding to various meta heuristic optimization technique like firefly algorithm (FA), grey-wolf optimization (GWO), grass-hopper algorithm (GHA) explore that GHA provides slightly better dynamics than the other and also converges faster. Further, sensitivity analysis suggests that system dynamics with GHA optimized I-TD controller at various loading conditions are robust and are not reset again.
     more »

  • Spatial Ambiguities Optimization in GIR

    Appears in:
    sis 18: e15

    Authors:
    Arun Kumar Yadav, Jay Kant Pratap Singh Yadav, Divakar Yadav

    Published:
    14th May 2020

    Abstract:
    INTRODUCTION: Huge amount of geographically referenced information is available on World Wide Web and has become an excellent source for retrieval of desired information. Extraction of relevant information from such a huge unstructured source is not an easy task. OBJECTIVES: In this paper, we p…INTRODUCTION: Huge amount of geographically referenced information is available on World Wide Web and has become an excellent source for retrieval of desired information. Extraction of relevant information from such a huge unstructured source is not an easy task.

    OBJECTIVES: In this paper, we propose a location based search engine, designing two indexes namely spatial index and inverted index. Using these indexes, we are able to solve toponym ambiguity and overlapping of documents.

    METHODS: To handle toponym ambiguity problem, we designed and implemented architecture for directional web document search. The architecture poses spatial followed by textual indexing for toponym resolution and to reduce overlapping of documents in geographical locations.

    RESULTS: The proposed architecture was implemented and tested on spatial and textural data sets.

    CONCLUSION: The performance is measured in terms of precision and false positive parameters and found that the proposed architecture performs better for geo/geo and geo/non-geo search queries.
     more »

  • Analytical Method to Compute the Cloud Computing Data Security Issues by Using Encryption Algorithms

    Appears in:
    sis 18: e14

    Authors:
    Abdul Hannan Khan, Syed Anwer Hasnain, Shahan Yamin Siddiqui, Muhammad Sohail Irshad, Muhammad Sajid, Shahid Iqbal

    Published:
    13th May 2020

    Abstract:
    All the associations are genuine thoughts to receive the cloud computing administrations, seeing its advantages as far as cost, openness, accessibility, adaptability and profoundly computerized procedure of upgrading. Cloud computing upgrade the present capacities progressively without further spec…All the associations are genuine thoughts to receive the cloud computing administrations, seeing its advantages as far as cost, openness, accessibility, adaptability and profoundly computerized procedure of upgrading. Cloud computing upgrade the present capacities progressively without further speculation. Cloud computing is a band of assets, applications, and administrations. With the execution of cloud computing, associations have solid worries about the security of their information. The research focuses on the security issues of cloud services. The AES and RSA algorithms will be verified on five parameters key generation time, encryption execution time, memory usage, file uploading time and file downloading time. The CloudSim simulator will be used to find results that the RSA is performing much better than AES in terms of encryption execution time.
     more »

  • Secure new node ID assignment for internet integrated wireless body area networks

    Appears in:
    sis 18: e13

    Authors:
    Amit Kumar Gautam, Rakesh Kumar

    Published:
    13th May 2020

    Abstract:
    Internet integrated sensor networks have gained much importance and exponential growth in wireless body area networks (WBAN) over the last few years. These networks are used in health services to remotely monitor the health of patients and send/receive sensitive, time critical medical data. Because…Internet integrated sensor networks have gained much importance and exponential growth in wireless body area networks (WBAN) over the last few years. These networks are used in health services to remotely monitor the health of patients and send/receive sensitive, time critical medical data. Because of the wireless and broadcast nature of WBAN, it is easy for an adversary to get, inject, or update the information transmitted in the medium or launch many security attacks. To protect the sensitive data of the patient, we propose a secure communication strategy for different sensors to form a WBAN. Here, a new node ID is assigned for sensors by using public key cryptography to communicate in the network. An energy utilization and communication cost analysis show that our approach incurs less communication overhead as compared with the recently proposed secure solutions in WBAN. The complexity analysis show that the proposed model works efficiently for secure communication in WBAN.
     more »

  • Eurasian Wolves-Cuckoo Search Optimizer for Localization of Mobile Sensor Nodes using Single Beacon Node in WSN

    Appears in:
    sis 18: e12

    Authors:
    Ravi Sharma, Shiva Prakash

    Published:
    13th May 2020

    Abstract:
    Wireless Sensor Networks (WSNs) is a widely used technology for remote area monitoring in collaboration with the Internet of Things (IoT). The fundamental research challenge of mobile sensor nodes for the WSN community is localization. The sensor node localization of the WSN is related to the NP-ha…Wireless Sensor Networks (WSNs) is a widely used technology for remote area monitoring in collaboration with the Internet of Things (IoT). The fundamental research challenge of mobile sensor nodes for the WSN community is localization. The sensor node localization of the WSN is related to the NP-hard problem, and because of this, determining the actual coordinate of the sensor node is quite complex. The computational intelligence approach is assisted in obtaining an optimal solution to the given NP-hard problem. Most researchers today are more concerned about three beacon-based localization approaches, but the fewest researchers are concerned about two or single beacon-based localization approaches. This paper provides a single beacon-based localization approach using the hybrid approach of the Eurasian Wolves Optimizer (EWO) and the Cuckoo Search Optimizer (CSO) algorithm called the EW-CSO computational intelligence algorithm for randomly deployed mobile sensor nodes. The simulation results of the computational intelligence algorithms show that the proposed work using EW-CSO performs better in terms of mean localization error, computational cost, and number of localized nodes from the EWO and EW- Particle Swarm Optimization (EW-PSO) algorithms. It also reduced the line of sight problem for mobile sensor nodes with efficient use of network resources.
     more »

  • Finding Frequent Subgraphs and Subpaths through Static and Dynamic Window Filtering Techniques

    Appears in:
    sis 18: e11

    Authors:
    Bhargavi B., K. Swarupa Rani

    Published:
    16th Apr 2020

    Abstract:
    Big data era has large volumes of data generated at high velocity from different data sources. Finding frequent subgraphs from the graph streams can be a challenging task as streams are non-uniformly distributed and continuously processed. Its applications include finding strongly interacting gro…Big data era has large volumes of data generated at high velocity from different data sources. Finding frequent
    subgraphs from the graph streams can be a challenging task as streams are non-uniformly distributed and
    continuously processed. Its applications include finding strongly interacting groups in social networks and
    sensor networks. To find frequent subgraphs, we proposed static single-window technique and dynamic
    sliding window techniques. We also proposed enhancements by extending proposed static approach with its
    variations and extending dynamic approach in variations of incremental strategy to find frequent subgraphs.
    We also solved the sub problem to extract frequent subpaths from sequence of paths. Its applications include
    finding congested sections in traffic analysis. We applied our proposed static and dynamic techniques to
    extract the frequent subpaths from sequence of paths. We experimented the proposed dynamic and static
    approaches with real and benchmark datasets.
     more »

  • Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model

    Appears in:
    sis 18: e10

    Authors:
    Akshay Aggarwal, Aniruddha Chauhan, Deepika Kumar, Mamta Mittal, Sharad Verma

    Published:
    9th Apr 2020

    Abstract:
    With the ever-increasing rate of information dissemination and absorption, “Fake News” has become a real menace. People these days often fall prey to fake news that is in line with their perception. Checking the authenticity of news articles manually is a time-consuming and laborious task, thus, …With the ever-increasing rate of information dissemination and absorption, “Fake News” has become a real menace. People
    these days often fall prey to fake news that is in line with their perception. Checking the authenticity of news articles
    manually is a time-consuming and laborious task, thus, giving rise to the requirement for automated computational tools that
    can provide insights about degree of fake ness for news articles. In this paper, a Natural Language Processing (NLP) based
    mechanism is proposed to combat this challenge of classifying news articles as either fake or real. Transfer learning on the
    Bidirectional Encoder Representations from Transformers (BERT) language model has been applied for this task. This paper
    demonstrates how even with minimal text pre-processing, the fine-tuned BERT model is robust enough to perform
    significantly well on the downstream task of classification of news articles. In addition, LSTM and Gradient Boosted Tree
    models have been built to perform the task and comparative results are provided for all three models. Fine-tuned BERT
    model could achieve an accuracy of 97.021% on NewsFN data and is able to outperform the other two models by
    approximately eight percent.
     more »

  • Big Computing: Where are we heading?

    Appears in:
    sis 18: e9

    Authors:
    Sabuzima Nayak, Ripon Patgiri, Thoudam Doren Singh

    Published:
    9th Apr 2020

    Abstract:
    This paper presents the overview of the current trends of Big data against the computing scenario from different aspects. Some of the important aspect includes the Exascale, the computing power and the kind of applications which offer the Big data. This starts with the current computing hardware c…This paper presents the overview of the current trends of Big data against the computing scenario from
    different aspects. Some of the important aspect includes the Exascale, the computing power and the kind of applications which offer the Big data. This starts with the current computing hardware constraint against
    the need of the rising Big data applications. We highlight the issues and challenges of energy requirement, software complexity, hardware failure, fault tolerant computing, and communication. As the complexity of computation is going to rise in the future. The paper also highlights the future direction of Big computing systems for Bioinformatics, social media, hardware and software requirements, data intensive computation and then towards GPU era.
     more »

  • Enhanced Bitcoin Protocol with Effective Block Creation and Verification by Trusted Miners

    Appears in:
    sis 18: e8

    Authors:
    R. Bala, R. Manoharan

    Published:
    19th Mar 2020

    Abstract:
    The Distributed nature of Bitcoin introduces security issues that necessitate security-specific enhancements in Bitcoin protocol. Therefore, proposing a method of incorporating criteria check and verification process for miners to participate in the mining process and join the mining pool respectiv…The Distributed nature of Bitcoin introduces security issues that necessitate security-specific enhancements in Bitcoin protocol. Therefore, proposing a method of incorporating criteria check and verification process for miners to participate in the mining process and join the mining pool respectively. The proposed idea mitigates double spending, block withholding, and 51 percentage attacks. In addition, an increase in the rate of Bitcoin users necessitates performance improvement. Hence,
    proposing an effective approach of refining the existing block creation and verification strategy for improving transaction rate without compromising security.
     more »

  • A Novel & Efficient Fusion Based Image Retrieval Model for Speedy Image Recovery

    Appears in:
    sis 18: e7

    Authors:
    Shefali Dhingra, Poonam Bansal

    Published:
    17th Mar 2020

    Abstract:
    An efficient and novel image retrieval system is framed here, which retrieve images from massive datasets to overcome the constraints of efficiency and retrieval time. Thus to address this issue, an effective indexing technique is proposed on the hybrid system constituted by low level features of t…An efficient and novel image retrieval system is framed here, which retrieve images from massive datasets to overcome the constraints of efficiency and retrieval time. Thus to address this issue, an effective indexing technique is proposed on the hybrid system constituted by low level features of the image. Firstly, features are extracted from the combination color moment, LBP and segmentation to form a hybrid feature space. To reduce its dimensional space, principle component analysis is exercised which provide lesser and good quality features. On this space, two expedient indexing techniques are proposed: cluster based and similarity based. The approach that is proposed here is an innovative design of a hybrid content based image retrieval system, as in this framework all the skilled techniques are merged to form a competent and dynamic image retrieval system. Five touchstone datasets are used to test the performance of the system. Extensive experiments are carried out which shows that the system with cluster based indexing technique provides highlighted results as compared to similarity based technique and also surpasses the other latest state of art techniques in terms of precision and retrieval time.
     more »

  • Learning Model for Phishing Website Detection

    Appears in:
    sis 18: e6

    Authors:
    A. Suryan, C. Kumar, M. Mehta, R. Juneja, A. Sinha

    Published:
    13th Mar 2020

    Abstract:
    Website portal empowered with information technology are of great importance in present scenario. With access to data all around the world, securing our information becomes an issue of topmost priority. Over the decade there have been numerous attacks by phishing websites and people have lost hug…Website portal empowered with information technology are of great importance in present scenario. With access to data all
    around the world, securing our information becomes an issue of topmost priority. Over the decade there have been
    numerous attacks by phishing websites and people have lost huge resources. Such malicious websites, also known as
    phishing website, steal information of authenticate users and carry out illegal transactions by misusing the personal
    information. Phishing website links and associated e-mails are sent to billions of users daily, thereby becoming a big
    concern for cyber security. In this paper, we address the phishing problem using machine learning approach applied on our
    proposed model, which uses 30 distinct features for phishing detection. We extracted multiple features from the website
    link and applied appropriate algorithms to classify the link as legitimate or phishing links.
     more »

  • FEECA: Fuzzy based Energy Efficient Clustering Approach in Wireless Sensor Network

    Appears in:
    sis 18: e5

    Authors:
    Anshu Kumar Dwivedi, A. K. Sharma

    Published:
    12th Mar 2020

    Abstract:
    With huge advancements in Microelectronics, Wireless Sensor Network (WSN) is conquering its domain with low powered tiny sensors. These sensors are commonly used in monitoring and surveillance of remote and urban areas. Since the power supply to these tiny sensors is a battery, for the maximal effi…With huge advancements in Microelectronics, Wireless Sensor Network (WSN) is conquering its domain with low powered tiny sensors. These sensors are commonly used in monitoring and surveillance of remote and urban areas. Since the power supply to these tiny sensors is a battery, for the maximal efficiency of WSN, there arises a need for a maximal
    lifetime of these tiny sensors wherever they are deployed. Clustering is a promising solution but there is uncertainty in the selection of cluster heads. Fuzzy logic, however, can contribute to the selection of optimal candidates to play the role of such cluster heads. To muddle through the issue, we have proposed a fuzzy-based energy-efficient clustering approach
    (FEECA) in wireless sensor networks and also designed a fuzzy inference system that defines influential parameters for selecting optimal candidates for cluster head (CH) role. To reduce the expenditure borne by communication, master nodes are selected among the chosen cluster heads so that their communication distance to the sink can be reduced. In the proposed scenario the area is divided into the diagonal form to reduce the load of the network. Each part consists of a Sensor Node (SN), cluster head, and master node. In the diagonal form, the network is divided so that the distance travelled by the data packet through the diagonal path is always smaller than the distance in which the horizontal path exists, based on the triangular inequality theorem. Simulation experiments were conducted and experimental results
    unveiled better performance of proposed work in terms of stability period, more packet delivery to sink and extended lifetime.
     more »

  • Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations

    Appears in:
    sis 18: e4

    Authors:
    Virendra P. Vishwakarma, Sahil Dalal

    Published:
    10th Mar 2020

    Abstract:
    The multifaceted light varying environment severely degrades the performance of person recognition using facial images. Here, the authors present a novel person identification method using hybridization of artificial neural network (ANN) and fuzzy logic concepts. An efficient illumination normali…The multifaceted light varying environment severely degrades the performance of person recognition using facial images.
    Here, the authors present a novel person identification method using hybridization of artificial neural network (ANN) and
    fuzzy logic concepts. An efficient illumination normalization method is presented with the help of a new modified S
    membership function. The proposed method of illumination normalization retains the large scale facial features as well as
    suppresses the variations related to change in light variations. Kernel extreme learning machine (KELM) which is a nonlinear and non-iterative learning algorithm of ANN is used for classification. Various kernel types and parameters are
    experimented to find the best choice for robust classification. To assess the performance of proposed hybridization, Yale
    and extended Yale B face databases have been used. Very promising results have been achieved which establish the worth
    of the proposed method.
     more »

  • Social Networks Mining and Analysis of Specific Groups (Political and Regional) by using API

    Appears in:
    sis 18: e3

    Authors:
    Farhan Khan, Saqib Saqib, Pireh Soomro, Asif Ali Laghari, Kamran Ali Memon

    Published:
    10th Mar 2020

    Abstract:
    People have informal conversations on social media sites (Twitter, Facebook, etc) that shed light on the current issues around the world – opinions, and concerns about the learning process. Data from such environments can provide valuable information to predict the future to make effective decisi…People have informal conversations on social media sites (Twitter, Facebook, etc) that shed light on the current issues
    around the world – opinions, and concerns about the learning process. Data from such environments can provide valuable
    information to predict the future to make effective decision making, however, the usage of such data is challenging. The
    complexity of such data from social media content requires the use of human understanding as well as social network
    analysis to make predictions. We focused on political groups around the world to discover linkages behind the Terrorism
    around the World to make predictions depend upon the data available. In this research paper, we focused on tweets related
    to the politicians and communities based on hashtags like #Syria #Weapons #Terrorist, etc. to identify the linkages of the
    user who are talking about these issues depending upon the HashTag we used for the content analysis of social network by
    using Gephi to make a prediction.
     more »

  • Performance analysis of compression algorithms for information security: A Review

    Appears in:
    sis 18: e2

    Authors:
    Neha Sharma, Usha Batra

    Published:
    6th Mar 2020

    Abstract:
    Data compression is a vital part of information security, since compressed data is much more secure and convenient to handle. Effective data compression technique creates an effective, secure, easy communicable & redundant data. There are two types of compression algorithmic techniques: - lossy a…Data compression is a vital part of information security, since compressed data is much more secure and convenient to
    handle. Effective data compression technique creates an effective, secure, easy communicable & redundant data. There are
    two types of compression algorithmic techniques: - lossy and lossless. These techniques can be applied to any data format
    like text, audio, video or image file. The primary objective of this study was to analyse data compression techniques used
    for information security techniques like steganography, cryptography etc. Four each, lossy and lossless techniques are
    implemented and evaluated on parameters like- file size, saving percentage, time, compression ratio and speed. Detailed
    analysis shows that the lossy techniques performs better quantitatively whereas lossless is better qualitatively. However,
    lossless techniques are more effective as there is no data loss during the process. Among all, Huffman encoding outperforms
    other algorithms.
     more »

  • An Improved Weighted Base Classification for Optimum Weighted Nearest Neighbor Classifiers

    Appears in:
    sis 18: e1

    Authors:
    Muhammad Abbas, Kamran Ali Memon, Noor ul Ain, Ekang Francis Ajebesone, Muhammad Usaid, Zulfiqar Ali Bhutto

    Published:
    27th Feb 2020

    Abstract:
    Existing classification studies use two non-parametric classifiers- k-nearest neighbours (kNN) and decision trees, and one parametric classifier-logistic regression, generating high accuracies. Previous research work has compared the results of these classifiers with training patterns of differen…Existing classification studies use two non-parametric classifiers- k-nearest neighbours (kNN) and decision trees, and one
    parametric classifier-logistic regression, generating high accuracies. Previous research work has compared the results of
    these classifiers with training patterns of different sizes to study alcohol tests. In this paper, the Improved Version of the
    kNN (IVkNN) algorithm is presented which overcomes the limitation of the conventional kNN algorithm to classify wine
    quality. The proposed method typically identifies the same number of nearest neighbours for each test example. Results
    indicate a higher Overall Accuracy (OA) that oscillates between 67% and 76%. Among the three classifiers, the least
    sensitive to the training sample size was the kNN and produced the unrivalled OA, followed by sequential decision trees and
    logistic regression. Based on the sample size, the proposed IVkNN model presented 80% accuracy and 0.375 root mean
    square error (RMSE).
     more »

  • Simplification of Punjabi Sentences: Converting Complex Participial Sentences into Simple Sentences

    Appears in:
    sis 20(26): e10

    Authors:
    R. M. Jindal, V. Rana, S. K. Sharma

    Published:
    27th Feb 2020

    Abstract:
    INTRODUCTION: In this world of internet and artificial intelligence, Natural Language Processing has emerged as most demanding research area. Under Natural Language Processing, sentence simplification is one of the research area that deals with simplification or conversion of complex sentences in…INTRODUCTION: In this world of internet and artificial intelligence, Natural Language Processing has emerged as most
    demanding research area. Under Natural Language Processing, sentence simplification is one of the research area that
    deals with simplification or conversion of complex sentences in to simple sentences.

    OBJECTIVES: In this research
    article, author has proposed a novel approach for conversion (simplification) of complex sentences (participial type) of
    Punjabi language into easily understandable simple sentences.

    METHODS: Author performed lexical and morphological
    simplification by using morphological features of the language. Morphological features are used to identify the participial
    type complex sentences.

    RESULTS: on testing the proposed algorithm, a precision of 96.39%, recall of 91.37% and Fmeasure as 93.79% was reported.

    CONCLUSION: the developed system can be helpful for Aphasic and Dyslexia readers
    and can be used as subpart for machine translation system, summarization system and other Natural Language Processing
    applications.
     more »

  • An Automated Recommender System for Educational Institute in India

    Appears in:
    sis 20(26): e9

    Authors:
    Mamata Garanayak, Sipra Sahoo, Sachi Nandan Mohanty, Alok Kumar Jagadev

    Published:
    14th Feb 2020

    Abstract:
    This study aims to suggest a recommender System for undergraduate students who desire to seek admission into engineering courses in different Indian Institute of Technology (IITs) in India. Initially, the focus is to purpose a recommender system for admission into the top 10 IIT on a pilot basis …This study aims to suggest a recommender System for undergraduate students who desire to seek admission into
    engineering courses in different Indian Institute of Technology (IITs) in India. Initially, the focus is to purpose a
    recommender system for admission into the top 10 IIT on a pilot basis in four common branches such as Electrical
    Engineering, Computer Science and Engineering, Mechanical Engineering, Civil Engineering. Data were collected from
    different authentic sources from 2016 to 2018. A model was built to predict the ranks for 2019 for each branch of every
    IITs. This paper illustrates prediction using Time Series Forecasting and recommendation algorithm using classification
    techniques. A comparative study of Random Forest Classification and K-Nearest Neighbor classification has been done.
    Finally, the recommendation algorithm shown reliable results with high accuracy in prediction model. It can be diversify
    and implement other streams as part of future work.
     more »

  • A Swarm Intelligence Based Coverage Hole Healing Approach for Wireless Sensor Networks

    Appears in:
    sis 20(26): e8

    Authors:
    Shalu Mehta, Amita Malik

    Published:
    11th Feb 2020

    Abstract:
    In the WSN network, nodes are always deprived of battery and can’t be in operation for a long time. Some nodes may die sooner than others, creating a void in the area. Our work in this article covered this hole area efficiently with the introduction of a novel hybrid optimization algorithm. Our a…In the WSN network, nodes are always deprived of battery and can’t be in operation for a long time. Some nodes may die sooner than
    others, creating a void in the area. Our work in this article covered this hole area efficiently with the introduction of a novel hybrid
    optimization algorithm. Our algorithm makes the locally converging particle swarm optimization to a global optimization algorithm by
    ensembling it with the Gravitational search algorithm. The perturbation in this algorithm is also reduced by logistic chaotic mapping.
    The current solution has detected the holes and healed them with additional redundant nodes. The Delaunay triangulation method
    detected the number of holes with the improved algebraic approach. The results are validated by extensive experimentation with
    different sensing range and node's density. Our method seems to perform well than state of the art schemes. We have also shown that
    increasing the redundant nodes, more coverage can be achieved.
     more »

  • Semantic N-Gram Topic Modeling

    Appears in:
    sis 20(26): e7

    Authors:
    Pooja Kherwa, Poonam Bansal

    Published:
    11th Feb 2020

    Abstract:
    In this paper a novel approach for effective topic modeling is presented. The approach is different from traditional vector space model-based topic modeling, where the Bag of Words (BOW) approach is followed. The novelty of our approach is that in phrase-based vector space, where critical measure…In this paper a novel approach for effective topic modeling is presented. The approach is different from
    traditional vector space model-based topic modeling, where the Bag of Words (BOW) approach is followed.
    The novelty of our approach is that in phrase-based vector space, where critical measure like point wise
    mutual information (PMI) and log frequency based mutual dependency (LGMD)is applied and phrase’s
    suitability for particular topic are calculated and best considerable semantic N-Gram phrases and terms are
    considered for further topic modeling. In this experiment the proposed semantic N-Gram topic modeling is
    compared with collocation Latent Dirichlet allocation(coll-LDA) and most appropriate state of the art topic
    modeling technique latent Dirichlet allocation (LDA). Results are evaluated and it was found that perplexity is
    drastically improved and found significant improvement in coherence score specifically for short text data set
    like movie reviews and political blogs.
     more »

  • Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals

    Appears in:
    sis 20(26): e6

    Authors:
    Ritu Singh, Navin Rajpal, Rajesh Mehta

    Published:
    6th Feb 2020

    Abstract:
    Electrocardiogram (ECG) monitoring is continuously required to detect cardiac ailments. At times it is challenging to interpret the differences in the P- QRS-T curve. The proposed approach aims to show the excellence of kernel capabilities of Kernel Principal Component Analysis (KPCA) and Kernel …Electrocardiogram (ECG) monitoring is continuously required to detect cardiac ailments. At times it is challenging to
    interpret the differences in the P- QRS-T curve. The proposed approach aims to show the excellence of kernel capabilities
    of Kernel Principal Component Analysis (KPCA) and Kernel Independent Component Analysis (KICA) in the wavelet
    domain. In this work, experiments are performed using five different categories of cardiac beats. The supervised
    classifiers like feed-forward neural network (FNN), backpropagation neural network (BPNN), and K nearest neighbor
    (KNN) statistically evaluates the impact of discrete wavelet with KPCA and KICA on extracted beats. The performance
    evaluation also compares the outcomes with existing techniques. The obtained results justify the supremacy of the
    combination of wavelet, kernel, and KNN approach, yielding a 99.7 % classification success rate. The five-fold crossvalidation scheme is used for measuring the efficacy of classifiers.
     more »

  • An Experimental Analysis of Secure-Energy Trade-Off using Optimized Routing Protocol In modern-secure-WSN

    Appears in:
    sis 20(26): e5

    Authors:
    S. Venkataramana, B.V.D.S. Sekhar, Bh. V.S. Ramakrishnam Raju, V.V.S.S.S. Chakravarthy, G. Srinivas

    Published:
    6th Feb 2020

    Abstract:
    In modern secure Wireless Sensor Networks (WSN), the sensor-nodes need extra energy owing to secure transmission of perceived information. So the energy-utilization of sensor-node should calculate while transfer the sensed-attributes securely to network. In this experimentation, we are proposing …In modern secure Wireless Sensor Networks (WSN), the sensor-nodes need extra energy owing to secure transmission of
    perceived information. So the energy-utilization of sensor-node should calculate while transfer the sensed-attributes securely
    to network. In this experimentation, we are proposing a revised Low Energy Adaptive Clustering Hierarchy (LEACH)
    protocol as LEATCH along secure information transmission (privacy and node authentication) in various levels using Quality
    of Protection Modeling Language (QoPML), which balance the Security-Energy trade-offs. This research experimentally
    analyzes the impact of data privacy, authentication operations on energy-utilization at sensor-node level while applying a
    LEACH & LEATCH. The obtained outcomes indicate the optimized LEATCH is outperforming correlated to the basic Leach
    with respect to minimal energy-utilization, time efficiency and expands life-time of modern-secure-WSNs.
     more »

  • K-Nearest Neighbor Learning based Diabetes Mellitus Prediction and Analysis for eHealth Services

    Appears in:
    sis 20(26): e4

    Authors:
    Iqbal H. Sarker, Md. Faisal Faruque, Hamed Alqahtani, Asra Kalim

    Published:
    15th Jan 2020

    Abstract:
    Nowadays, eHealth service has become a booming area, which refers to computer-based health care and information delivery to improve health service locally, regionally and worldwide. An effective disease risk prediction model by analyzing electronic health data benefits not only to care a patient …Nowadays, eHealth service has become a booming area, which refers to computer-based health care and
    information delivery to improve health service locally, regionally and worldwide. An effective disease risk
    prediction model by analyzing electronic health data benefits not only to care a patient but also to provide
    services through the corresponding data-driven eHealth systems. In this paper, we particularly focus on
    predicting and analysing diabetes mellitus, an increasingly prevalent chronic disease that refers to a group
    of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. K-Nearest
    Neighbor (KNN) is one of the most popular and simplest machine learning techniques to build such a disease
    risk prediction model utilizing relevant health data. In order to achieve our goal, we present an optimal KNearest Neighbor (Opt-KNN) learning based prediction model based on patient’s habitual attributes in various
    dimensions. This approach determines the optimal number of neighbors with low error rate for providing
    better prediction outcome in the resultant model. The effectiveness of this machine learning eHealth model
    is examined by conducting experiments on the real-world diabetes mellitus data collected from medical
    hospitals.
     more »

  • Diagnosis Heart Disease Using Mamdani Fuzzy Inference Expert System

    Appears in:
    sis 20(26): e3

    Authors:
    Iftikhar Naseer, Bilal Shoaib Khan, Shazia Saqib, Syed Nadeem Tahir, Sheraz Tariq, Muhammad Saleem Akhter

    Published:
    15th Jan 2020

    Abstract:
    The death ratio caused by heart diseases is threating around the world. Efficient and accurate diagnosis through information technology can turn over this picture. This article proposed Diagnosis Heart Disease using Mamdani Fuzzy Inference (DHD-MFI) based expert system which intelligently diagnos…The death ratio caused by heart diseases is threating around the world. Efficient and accurate diagnosis through information
    technology can turn over this picture. This article proposed Diagnosis Heart Disease using Mamdani Fuzzy Inference
    (DHD-MFI) based expert system which intelligently diagnoses heart disease. In an explorative pattern, the current research
    has taken six conducive variables for the purpose of fuzzy logic technical enhancement in the diagnosis of heart disease. The
    input fields comprise of age, chest pain, electrocardiography, blood pressure systolic, diabetic and cholesterol are
    transmitted with the help of Fuzzy rules which are framed in the light of low, normal, high and very high intensity among
    the input variations. The single output is obtained as a clinical decision support system for the heart diagnosis by using the
    Mamdani Inference method. The proposed DHD-MFI based expert system gives 94% overall accuracy.
     more »

  • Diagnosis of Arthritis Using Adaptive Hierarchical Mamdani Fuzzy Type-1 Expert System

    Appears in:
    sis 20(26): e2

    Authors:
    Shahan Yamin Siddiqui, Syed Anwar Hussnain, Abdul Hannan Siddiqui, Rimsha Ghufran, Muhammad Saleem Khan, Muhammad Sohail Irshad, Abdul Hannan Khan

    Published:
    18th Nov 2019

    Abstract:
    The adroit system is frequently used in artificial intelligence in medicine (AIM). They comprise medical information about a dedicated task and prone to purpose with data from case studies to produce lucid results. Though there are many irregularities, the information with an adroit network is deri…The adroit system is frequently used in artificial intelligence in medicine (AIM). They comprise medical information about a dedicated task and prone to purpose with data from case studies to produce lucid results. Though there are many irregularities, the information with an adroit network is derived with a set of expert rules to produce accurate results. Arthritis is the stiffness of one or more joints and about three fourth of the victims are suffering from it. Late detection of that chronic disease may cause the severity of the sickness at greater risk. So the idea is to contemplate a mechanism for the detection of arthritis using an adaptive hierarchical Mamdani fuzzy expert system (DA-AH-MFES). It is a befitting source to process ambiguity and inaccuracy. Physical and some medical parameters with the expertise of doctors can be
    mapped using MFES. The ability of MFES completely depends on the rules which are finalized by a discussion with an expert. The expert system has eight input variables at layer-I and four input variables at layer-II. At layer-I input variables are rest pain, morning stiffness, body pain, joint infection, swelling, redness, past injury and age that detects output condition of arthritis to be normal, infection and/or other problem. The further input variables of layer-II are RF, ANA,
    HLA-B27, ANTI-CCP that determine the output condition of arthritis. The performance of proposed Diagnose arthritis disease using an adaptive hierarchical mamdani fuzzy expert system is evaluated with expert observations of Cavan General Hospital Lisdaran, Cavan, Ireland and Jinnah Hospital Lahore, Pakistan. The accuracy of the expert system (DAAH-MFES) is 95.6%.
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  • Efficient and Flexible Role-Based Access Control (EFRBAC) Mechanism for Cloud

    Appears in:
    sis 20(26): e1

    Authors:
    Shilpi Harnal, R.K. Chauhan

    Published:
    18th Nov 2019

    Abstract:
    The cloud reduces the user’s burden to many folds. But cloud providers and cloud users with dynamic relationship, are in distinct security domains. Amongst various challenges with cloud, the crucial one is to detect and protect the user’s data from unauthorized accesses. In cloud, users are not leg…The cloud reduces the user’s burden to many folds. But cloud providers and cloud users with dynamic relationship, are in distinct security domains. Amongst various challenges with cloud, the crucial one is to detect and protect the user’s data from unauthorized accesses. In cloud, users are not legendary by their predefined identities. Instead, they are providing
    accesses based on their characteristics and attributes. This work is focusing on available access control mechanisms and one that applicable for cloud environment. The paper also proposes an Efficient and Flexible Role-Based Access Control (EF-RBAC) mechanism for the cloud computing environment to achieve confidentiality and security. RBAC limits the
    accesses for resources within an organization to authorized users only and also guarantees that a user can solely access specific information they are authorized for by the organization policy. The proposed scheme adds flexibility to the RBAC for better cloud user’s experience.
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  • The impact of using social media and internet on academic performance case study Bahrain Universities

    Appears in:
    sis 17(13): e2

    Author:
    Abdulla Jaafar Desmal

    Downloads:
    4906

    Abstract:
    The internet and social media provide students with a range of academic benefits and opportunities to enhance their learning process. The main goal of this research is to examine the impact of using the social media on the academic performance. The new social networks, such as Instagram, Facebook, …The internet and social media provide students with a range of academic benefits and opportunities to enhance their learning process. The main goal of this research is to examine the impact of using the social media on the academic performance. The new social networks, such as Instagram, Facebook, Twitter, etc., can affect the behavior and academic performance of the universities' students; therefore the selected universities were Ahlia University, Applied Science University and University of Bahrain. The sample was (150) students distributed equally among the three universities. The research questions will answer (1) what is the evolution of ICTs and the Internet in the World; (2) what is the impact caused by ICT in education; (3) what are the effects of social media on the academic performance of students at Bahrain Universities; (4) what are the social networking sites that are more popular among students at Bahrain Universities. The results show that the social media has a positive impact on academic performance and 57% of students prefer the mobile application WhatsApp as a social media for their academic purpose.
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  • A Narrative Literature Review and E-Commerce Website Research

    Appears in:
    sis 18(17): e1

    Author:
    K.M. Rahman

    Downloads:
    1856

    Abstract:
    In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To…In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To compete in the global e-commerce marketplace, local businesses need to focus on designing culturally friendly e-commerce websites. To the best of my knowledge, there has been insignificant research conducted on correlations between culture and e-commerce website design. The research shows that there are correlations between e-commerce, culture, and website design. The result of the study indicates that cultural aspects influence e-commerce website design. This study aims to deliver a reference source for information systems and information technology researchers interested in culture and e-commerce website design, and will show less-focused research areas in addition to future directions.
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  • A Parking Management System based on Background Difference Detecting Algorithm

    Appears in:
    sis 15(7): e3

    Authors:
    Xiaoling Wu, Yanwen Wang, Hainan Chen, Kangkang Liang, Lei Shu

    Downloads:
    1801

    Abstract:
    The number of vehicles in cities has increased dramatically due to rapid economic development. However, the infrastructure for accommodating these vehicles has grown relatively slow. Alleviating the pressure on the urban transport system and solving the ‘parking difficulty’ problem have thus becom…The number of vehicles in cities has increased dramatically due to rapid economic development. However, the infrastructure for accommodating these vehicles has grown
    relatively slow. Alleviating the pressure on the urban transport system and solving the ‘parking difficulty’ problem have thus become hot topics recently. In this paper, an intelligent parking system based on geomagnetic field variations is presented to solve
    this problem. An algorithm which detects the presence of vehicles in parking spaces in a parking lot is designed and field test results are presented. Our results show that this system has an acceptably high accuracy with low cost, high feasibility, high efficiency and hence is recommended for wide use.
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  • Preventing DDoS using Bloom Filter: A Survey

    Appears in:
    sis 18(19): e3

    Authors:
    Ripon Patgiri, Sabuzima Nayak, Samir Kumar Borgohain

    Downloads:
    1623

    Abstract:
    Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, causes loss of business re…Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, causes loss of business revenue. Therefore, DDoS is a grand challenge to defeat. There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending the DDoS attack. The Bloom Filter is a probabilistic data structure for membership query that returns either true or false. Bloom Filter uses tiny memory to store information of large data. Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS. This paper presents a survey on DDoS defending technique using Bloom Filter.
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  • A Hybrid Approach for Breast Cancer Classification and Diagnosis

    Appears in:
    sis 19(20): e2

    Authors:
    Bibhuprasad Sahu, Sachi Nandan Mohanty, Saroj Kumar Rout

    Downloads:
    1612

    Abstract:
    Feature selection in breast cancer disease important and risky task for further analysis. Breast cancer is the second leading reason for death among the women. Cancer starts from breast and spread to other part of the body. People are unable to identify their disease before it become dangerous. It …Feature selection in breast cancer disease important and risky task for further analysis. Breast cancer is the second leading reason for death among the women. Cancer starts from breast and spread to other part of the body. People are unable to identify their disease before it become dangerous. It can be cured if the disease identified at early stage. Accurate classification of benign tumours can avoid patients undergoing unnecessary treatments. Data Analytics and machine learning methods provides framework for prognostic studies by errorless classification of data instances into relevant based on the cancer severity. In this study we have purposed a prediction model by combining artificial intelligent based learning technique with multivariate statistical method. For automation of the diagnosis process data mining plays an significant role. The data sets available in different repositories are noisy in nature. This study suggests a hybrid feature selection method to be used with PCA (Principal Component Analysis) and Artificial Neural Network (ANN). Preprocessing of data and extracting the most relevant features done by PCA. The proposed algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases. In classification phase 10 fold cross validation was used. The suggested algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved better accuracy with sensitivity and F measure comparison with others and by enhancing this concept we can provide a future scope to produce sophisticated learning models for diagnosis.
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  • An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

    Appears in:
    sis 18(18): e5

    Authors:
    M. Sharma, G. Singh, R. Singh

    Downloads:
    1471

    Abstract:
    The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solu…The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.
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  • Stock Price Prediction using Artificial Neural Model: An Application of Big Data

    Appears in:
    sis 19(20): e1

    Authors:
    Malav Shastri, Sudipta Roy, Mamta Mittal

    Downloads:
    1402

    Abstract:
    In recent time, stock price prediction is an area of profound interest in the realm of fiscal market. To predict the stock prices, authors have proposed a technique by first calculating the sentiment scores through Naïve Bayes classifier and after that neural network is applied on both sentiment sc…In recent time, stock price prediction is an area of profound interest in the realm of fiscal market. To predict the stock prices, authors have proposed a technique by first calculating the sentiment scores through Naïve Bayes classifier and after that neural network is applied on both sentiment scores and historical stock dataset. They have also addressed the issue of data cleaning using a Hive ecosystem. This ecosystem is being used for pre-processing part and a neural network model with inputs from sentiment analysis and historic data is used to predict the prices. It has been observed from the experiments that the accuracy level reaches above 90% in maximum cases, as well as it also provides the solid base that model will be more accurate if it trained with recent data. The intended combination of sentiment analysis and Neural networks is used to establish a statistical relationship between historic numerical data records of a particular stock and other sentimental factors which can affects the stock prices.
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  • Machine Learning and Predictive Analysis of Fossil Fuels Consumption in Mid-Term

    Appears in:
    sis 17(15): e4

    Authors:
    Mahmood Amerion, Mohammadmehdi Hosseini, Abdorreza Alavi Gharahbagh, Mohsen Amerion

    Downloads:
    1385

    Abstract:
    In economies that are dependent on fossil fuel revenues, Realization of long-term plans, mid-term and annual budgeting requires a fairly accurate estimation of the amount of consumption and its price fluctuations. Accordingly, the present study is using machine learning techniques to predict the us…In economies that are dependent on fossil fuel revenues, Realization of long-term plans, mid-term and annual budgeting requires a fairly accurate estimation of the amount of consumption and its price fluctuations. Accordingly, the present study is using machine learning techniques to predict the usage of fossil fuels (Diesel, Black oil, Heating oil, and Petrol) in mid-term. Exponential Smoothing, a model of time series and the Neural Network model have been applied on the actual usage data obtained from Shahroud area from 2010 to 2015. For estimation of predictive value by Neural Network method, the training and testing samples, the highest and lowest errors with a range of 41% -0.89% and 88% -3% for the Mean Absolute Percent Deviation are the most appropriate predictions for Petrol consumption. And in the Single Exponential Smoothing, the forecast rate for each product is estimated on a
    quarterly as well as monthly basis.
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  • Auto recalls and software quality in the automotive sector

    Appears in:
    sis 18(17): e3

    Authors:
    J. Pancik, A. Vemola, R. Kledus, M. Semela, A. Bradac

    Downloads:
    1056

    Abstract:
    Software for automobiles is one from innovative factors in the automotive industry. Automobile is represented as an amount of embedded systems (embedded systems) and it is a very complex computing system. It is currently estimated that the average car has built-in software in the range of 100 milli…Software for automobiles is one from innovative factors in the automotive industry. Automobile is represented as an amount of embedded systems (embedded systems) and it is a very complex computing system. It is currently estimated that the average car has built-in software in the range of 100 million lines and in 2020 is already expected 300 million lines of code. The contribution is devoted to the management of safety and reliability of the software development for embedded systems designed for electromechanical (mechatronic) systems through quality assurance of embedded software. The contribution defines the term software quality assurance strategy, explains the role of standards such as ISO 26262 (Road vehicles - Functional safety), ISO 15504 (Automotive SPICE 3.0)
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  • Single and Multiple UAV Cyber-Attack Simulation and Performance Evaluation

    Appears in:
    sis 15(4): e4

    Authors:
    Ahmad Y. Javaid, Weiqing Sun, Mansoor Alam

    Downloads:
    1035

    Abstract:
    Usage of ground, air and underwater unmanned vehicles (UGV, UAV and UUV) has increased exponentially in the recent past with industries producing thousands of these unmanned vehicles every year.With the ongoing discussion of integration of UAVs in the US National Airspace, the need of a cost-effect…Usage of ground, air and underwater unmanned vehicles (UGV, UAV and UUV) has increased exponentially in the recent past with industries producing thousands of these unmanned vehicles every year.With the ongoing discussion of integration of UAVs in the US National Airspace, the need of a cost-effective way to verify the security and resilience of a group of communicating UAVs under attack has become very important. The answer to this need is a simulation testbed which can be used to simulate the UAV Network (UAVNet). One of these attempts is - UAVSim (Unmanned Aerial Vehicle Simulation testbed) developed at the University of Toledo. It has the capability of simulating large UAV networks as well as small UAV networks with large number of attack nodes. In this paper, we analyse the performance of the simulation testbed for two
    attacks, targeting single and multiple UAVs. Traditional and generic computing resource available in a regular computer laboratory was used. Various evaluation results have been presented and analysed which suggest the suitability of UAVSim for UAVNet attack and swarm simulation applications.
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Publisher
EAI
ISSN
2032-9407
Number of Volumes
7
Last Published
2020-05-26