EAI Endorsed Transactions on Scalable Information Systems

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sis/index

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

Aims & Scope
Indexing
Editorial Board
Special Issues

EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2024, the journal started to publish twelve issues per year. Authors are not charged for article submission and processing.

INDEXING: ESCI-WoS (IF: 1.3), Scopus (CiteScore 2022: 2.6), Compendex, DOAJ, ProQuest, EBSCO

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

Call for Papers: Special issue on: Real-time image information processing with deep neural networks and data mining technologies (Manuscript submission deadline: 2022-02-28; Notification of acceptance: 2022-04-15; Submission of final revised paper: 2022-05-15; Publication of special issue (tentative): 2022-06-15)

Guest Editor: Dr. Prof. Hang Li (Northeastern University, China) Guest Editor: Dr. Prof. Jochen Schiewe (HafenCity Universität Hamburg, Germany)

Editors-in-Chief
Hua Wang, Victoria University, Australia
Xiaohua Jia, City University of Hong Kong
Editorial board
Manik Sharma, DAV University, India
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)
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)
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 (Faculty of Sciences, University of Lisbon, 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)
Ji Zhang (University of Southern Queensland, Australia)
Xiaohui Tao (University of Southern Queensland, Australia)
Ye Wang (National University of Defense Technology, China)
Nageswara Rao Moparthi (KL University, India)
Shuai Liu (Hunan Normal University, China)
Prof Xiaoming Fu (Georg-August-University of Goettingen, Germany)
Prof Zhisheng Huang (Vrije University of Amsterdam)
Prof Rose Quan (Northumbria University, UK)
Prof Shi Dong (Zhoukou Normal University, China)
Dr Limei Peng (Kyungpook National University, South Korea)
Prof Hui Ma( Victoria University of Wellington, New Zealand)
Dr. Venkatesan Subramanian (Indian Institute of Information Technology – Allahabad, India)
Dr Pon Harshavardhanan (VIT Bhopal University, India)
Dr. Manish Kumar (The Indian Institute of Information Technology, Allahabad, India)
Muzammil Hussain, University of Management and Technology, Lahore, Pakistan
Michael Bewong, Charles Sturt University, Australia
Shabir Ahmad, Gachon University, Korea
Vu Nguyen, University of Science, Vietnam
Xiaodi Huang, Charles Sturt University, Australia
Jianming Yong, University of Southern Queensland, Australia
Yogeshwar Vijayakumar Navandar; National Institute of Technology, Indian.
Zhengyi Chai, Tiangong University in China, China
Chuanlong Wang, Taiyuan Normal University, China
Chin-Feng Lee, Chaoyang University of Technology, Taiwan
Hsing-Chung Chen (Jack Chen), Asia University, Taiwan
Wen-Yang Lin, National University of Kaohsiung, Taiwan
Chun-Hao Chen, National Kaohsiung University of Science and Technology, Taiwan
Mudasir Mohd, University of Kashmir, India.
BalaAnand Muthu, INTI International University, Malaysia.
Md Rafiqul Islam, Australian Institute of Higher Education, Australia.
Jin Wang, Institute of Applied Physics and Computational Mathematics, China.
Chandu Thota, University of Nicosia, Cyprus.
Haris M. Khalid, University of Dubai, UAE.
Dr. G. Reza Nasiri, Alzahra University, Tehran, Iran.
Siuly Siuly, Victoria University, Australia
Bishnu Prasad Gautam, Kanazawa Gakuin University, Japan
Sivaparthipan C B, Bharathiar University, India
Ting-Chia Hsu, National Taiwan Normal University, Taiwan
Punitha Palanisamy, Tagore IET, India
Lakshmana Kumar R, Tagore IET, India
Weiwei Jiang, Beijing University of Posts and Telecommunications, Taiwan

Recently Published
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Design and Application of Evaluation Method for Civics Classroom Based on CRITIC Fuzzy Algorithm
Appears in:
sis 23(1):

Author:
Zhanyu Chang

Published:
24th Oct 2024

Abstract:
INTRODUCTION: Through in-depth study of Civics classroom evaluation, it can provide teachers with scientific evaluation indexes and methods, improve teaching quality and effect, promote the overall development of students, and also promote the professional growth of teachers. OBJECTIVES: Based on …INTRODUCTION: Through in-depth study of Civics classroom evaluation, it can provide teachers with scientific evaluation indexes and methods, improve teaching quality and effect, promote the overall development of students, and also promote the professional growth of teachers.
OBJECTIVES: Based on critical and fuzzy comprehensive evaluation methods, this study aims to study the effectiveness and accuracy of the evaluation methods in Civics and Politics classrooms. Based on the CRITIC method, the fuzzy comprehensive evaluation method was introduced to solve the problem of subjectivity and uncertainty in the evaluation process.
METHODS: Various research methods were used, including observation, interviews, and questionnaires. By comprehensively analyzing the students' performance, feedback, and assessment, and the teachers' pre-course preparation, the advantages and improvement directions of Civics classroom teaching can be accurately evaluated using the CRITIC and fuzzy comprehensive evaluation methods.
RESULTS: The study results show that the CRITIC and fuzzy comprehensive evaluation methods can provide a more comprehensive, accurate, and objective evaluation of the Civics classroom. The undefined complete evaluation method plays an essential role in dealing with ambiguity and uncertainty in the evaluation process, making the evaluation results more objective and reliable.
CONCLUSION: The Civics classroom evaluation method based on the CRITIC and fuzzy comprehensive evaluation methods is adequate and accurate. These findings strongly support improving the quality of Civics classroom teaching and enhancing students' learning outcomes.
more »
A New Hybrid COA-OOA Based Task Scheduling and Fuzzy Logic Approach to Increase Fault Tolerance in Cloud Computing
Appears in:
sis 23(6):

Authors:
Manoj Kumar Malik, Vineet Goel, Abhishek Swaroop

Published:
27th Jun 2024

Abstract:
INTRODUCTION: Technology is made available to customers worldwide through a distributed computing architecture called cloud computing. In the cloud paradigm, there is a risk of single-point failures, in order to prevent errors and gain confidence from consumers in their cloud services, one problem …INTRODUCTION: Technology is made available to customers worldwide through a distributed computing architecture called cloud computing. In the cloud paradigm, there is a risk of single-point failures, in order to prevent errors and gain confidence from consumers in their cloud services, one problem facing cloud providers is efficiently scheduling tasks.
OBJECTIVES: High availability and fault tolerance must be offered to clients by these services. Fuzzy logic and hybrid COA-OOA are used in this study proposed fault-tolerant work scheduling algorithm. Jobs given by users and virtual machines are considered as input for this proposed approach.
METHODS: The given tasks are initially scheduled utilizing the FIFO order. Then, it is rescheduled utilizing the Hybrid Coati Optimization Algorithm (COA) - Osprey Optimization Algorithm (OOA) for scheduling the task based on priority.
RESULTS: This scheduled job is assigned to the VM for further execution. If the jobs are not executed successfully, then fault tolerant mechanism is carried out. Faults are recognized by employing fuzzy logic in this proposed approach. CONCLUSION: This proposed approach attains 62 sec response time, 61 sec of makespan and 98% success rate. Thus, this proposed approach is the best choice for efficient task scheduling with fault tolerant mechanism.
more »
A hybrid intrusion detection system with K-means and CNN+LSTM
Appears in:
sis 23(6):

Authors:
Haifeng Lv, Yong Ding

Published:
27th Jun 2024

Abstract:
Intrusion detection system (IDS) plays an important role as it provides an efficient mechanism to prevent or mitigate cyberattacks. With the recent advancement of artificial intelligence (AI), there have been many deep learning methods for intrusion anomaly detection to improve network security. In…Intrusion detection system (IDS) plays an important role as it provides an efficient mechanism to prevent or mitigate cyberattacks. With the recent advancement of artificial intelligence (AI), there have been many deep learning methods for intrusion anomaly detection to improve network security. In this research, we present a novel hybrid framework called KCLSTM, combining the K-means clustering algorithm with convolutional neural network (CNN) and long short-term memory (LSTM) architecture for the binary classification of intrusion detection systems. Extensive experiments are conducted to evaluate the performance of the proposed model on the well-known NSL-KDD dataset in terms of accuracy, precision, recall, F1-score, detection rate (DR), and false alarm rate (FAR). The results are compared with traditional machine learning approaches and deep learning methods. The proposed model demonstrates superior performance in terms of accuracy, DR, and F1-score, showcasing its effectiveness in identifying network intrusions accurately while minimizing false positives.
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Comprehensive Review of Advanced Machine Learning Techniques for Detecting and Mitigating Zero-Day Exploits
Appears in:
sis 23(6):

Authors:
Nachaat Mohamed, Hamed Taherdoost, Mitra Madanchian

Published:
26th Jun 2024

Abstract:
This paper provides an in-depth examination of the latest machine learning (ML) methodologies applied to the detection and mitigation of zero-day exploits, which represent a critical vulnerability in cybersecurity. We discuss the evolution of machine learning techniques from basic statistical model…This paper provides an in-depth examination of the latest machine learning (ML) methodologies applied to the detection and mitigation of zero-day exploits, which represent a critical vulnerability in cybersecurity. We discuss the evolution of machine learning techniques from basic statistical models to sophisticated deep learning frameworks and evaluate their effectiveness in identifying and addressing zero-day threats. The integration of ML with other cybersecurity mechanisms to develop adaptive, robust defense systems is also explored, alongside challenges such as data scarcity, false positives, and the constant arms race against cyber attackers. Special attention is given to innovative strategies that enhance real-time response and prediction capabilities. This review aims to synthesize current trends and anticipate future developments in machine learning technologies to better equip researchers, cybersecurity professionals, and policymakers in their ongoing battle against zero-day exploits.
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Sentinel Shield: Leveraging ConvLSTM and Elephant Herd Optimization for Advanced Network Intrusion Detection
Appears in:
sis 23(6):

Authors:
Aparna Tiwari, Dinesh Kumar

Published:
26th Jun 2024

Abstract:
Given the escalating intricacy of network environments and the rising level of sophistication in cyber threats, there is an urgent requirement for resilient and effective network intrusion detection systems (NIDS). This document presents an innovative NIDS approach that utilizes Convolutional Long …Given the escalating intricacy of network environments and the rising level of sophistication in cyber threats, there is an urgent requirement for resilient and effective network intrusion detection systems (NIDS). This document presents an innovative NIDS approach that utilizes Convolutional Long Short-Term Memory (ConvLSTM) networks and Elephant Herd Optimization (EHO) to achieve precise and timely intrusion detection. Our proposed model combines the strengths of ConvLSTM, which can effectively capture spatiotemporal dependencies in network traffic data, and EHO, which allow the model to focus on relevant information while filtering out noise. To achieve this, we first preprocess network traffic data into sequential form and use ConvLSTM layers to learn both spatial and temporal features. Subsequently, we introduce Elephant Herd Optimization that dynamically assigns different weights to different parts of the input data, emphasizing the regions most likely to contain malicious activity. To evaluate the effectiveness of our approach, we conducted extensive experiments on publicly available network intrusion CICIDS2017 Dataset. The experimental results demonstrate the efficacy of the proposed approach (Accuracy = 99.98%), underscoring its potential to revolutionize modern network intrusion detection and proactively safeguard digital assets.
more »
Improved Convolutional Neural Network Algorithm for Student Behavior Detection in the Classroom
Appears in:
sis 24(5):

Authors:
Yihua Liu, Weirong Wang

Published:
3rd May 2024

Abstract:
The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to impr…The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to improve the accuracy of student classroom behavior detection algorithm, this paper proposes a student classroom behavior detection method based on improved convolutional neural network algorithm. Firstly, the student behavior detection dataset is constructed, and the student classroom behavior detection technology scheme is designed; secondly, in order to improve the detection accuracy, the features are extracted by using the new jumping bi-directional paths, and the attention mechanism module is added at different positions to improve the path aggregation network; weekly, the embedding positions of the attention mechanism strategy are determined by analyzing multiple sets of experiments, and the proposed student classroom behavior detection algorithm's effectiveness and superiority.
more »
Application Big Data and Intelligent Optimization Algorithms on Teaching Evaluation Method for Higher Vocational Institutions
Appears in:
sis 24(5):

Author:
Meijuan Huang

Published:
3rd May 2024

Abstract:
INTRODUCTION: The optimization of the teaching evaluation system, as an essential part of teaching reform in higher vocational colleges and universities, is conducive to the development of higher vocational colleges and universities' disciplines, making the existing teaching more standardized. OBJ…INTRODUCTION: The optimization of the teaching evaluation system, as an essential part of teaching reform in higher vocational colleges and universities, is conducive to the development of higher vocational colleges and universities' disciplines, making the existing teaching more standardized.
OBJECTIVES: Aiming at the problems of inefficiency, incomplete index system, and low assessment accuracy in evaluation methods of higher vocational colleges and universities.
METHODS: Proposes a teaching evaluation method for higher vocational colleges and universities with a big data mining algorithm and an intelligent optimization algorithm. Firstly, the teaching evaluation index system of higher vocational colleges and universities is downgraded and analyzed by using principal component analysis; then, the random forest hyperparameters are optimized by the grey wolf optimization algorithm, and the teaching evaluation model of higher vocational colleges and universities is constructed; finally, the validity and stability of the proposed method is verified by simulation experimental analysis.
RESULTS: The results show that the proposed method improves the accuracy of the evaluation model.
CONCLUSION: Solves the problems of low evaluation accuracy, incomplete system, and low efficiency of teaching evaluation methods in higher vocational colleges.
more »
Design of Intelligent Political Test Paper Generation Method Based on Improved Intelligent Optimization Algorithm
Appears in:
sis 24(5):

Author:
Qing Wan

Published:
3rd May 2024

Abstract:
With the development of artificial intelligence, computer intelligent grouping, as a research hotspot of political ideology examination paper proposition, can greatly shorten the time of generating examination papers, reduce the human cost, reduce the human factor, and improve the quality of politi…With the development of artificial intelligence, computer intelligent grouping, as a research hotspot of political ideology examination paper proposition, can greatly shorten the time of generating examination papers, reduce the human cost, reduce the human factor, and improve the quality of political ideology teaching evaluation. Aiming at the problem that the current political ideology examination paper-grouping strategy method easily falls into the local optimum, a kind of intelligent paper-grouping method for political ideology examination based on the improved stock market trading optimisation algorithm is proposed. Firstly, by analyzing the traditional steps of political thought grouping, according to the index genus of the grouping problem and the condition constraints, we construct the grouping model of political thought test questions; then, combining the segmented real number coding method and the fitness function, we use the securities market trading optimization algorithm based on the Circle chaotic mapping initialization strategy and adaptive t-distribution variability strategy to solve the grouping problem of the political thought test. The experimental results show that the method can effectively find the optimal strategy of political thought exam grouping, and the test questions have higher knowledge point coverage, moderate difficulty, and more stable performance.
more »
IoT Product Design for User Experience and Technological Innovation in Virtual Reality Environments
Appears in:
sis 24(5):

Author:
Hao Zhang

Published:
3rd May 2024

Abstract:
INTRODUCTION: The rapid development of virtual reality technology and the Internet of Things (IoT) has provided new possibilities for user experience, and a variety of new products have emerged, especially in the field of painting, where the combination of these two provides a new platform for inno…INTRODUCTION: The rapid development of virtual reality technology and the Internet of Things (IoT) has provided new possibilities for user experience, and a variety of new products have emerged, especially in the field of painting, where the combination of these two provides a new platform for innovative artistic expression.
OBJECTIVES: This study takes IoT products in the art field as an example to analyze the user experience in virtual reality environments and the impact of technological innovations on IoT products, as well as to explore the potentials and limitations of this emerging form of products and forms of painting.
METHODS: In this study, the author constructed a virtual reality painting environment, utilized IoT technology to collect data from the user's painting process, and combined quantitative and qualitative analysis methods to assess user experience and technological innovation comprehensively.
RESULTS: In the virtual reality environment, the user experience was significantly improved, and the users were more immersed in the painting process and felt more robust creativity and expression. Meanwhile, the application of Internet of Things (IoT) technology also provides more possibilities for drawing; for example, using smartpens makes the drawing process more smooth and natural.
CONCLUSION: IoT painting with user experience and technological innovation in a virtual reality environment can provide a new creative platform for artists and bring a richer artistic experience to the audience, showing the feasibility and broad prospect of IoT products based on a virtual reality environment.

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Research and Design of Encryption Standards Based on IoT Network Layer Information Security of Data
Appears in:
sis 24(5):

Author:
Jia Wang

Published:
3rd May 2024

Abstract:
INTRODUCTION: With the rapid development of the economy, more and more devices and sensors are connected to the Internet, and a large amount of data is transmitted in the network. However, this large-scale data transmission involves the problem of information security, especially in the transport l…INTRODUCTION: With the rapid development of the economy, more and more devices and sensors are connected to the Internet, and a large amount of data is transmitted in the network. However, this large-scale data transmission involves the problem of information security, especially in the transport layer. Therefore, there is an urgent need to study and design an information security data enhancement security strategy for the transport layer of ubiquitous networks (i.e., IoT). OBJECTIVES: This thesis aims to research and create a data enhancement security strategy for the transport layer of the Ubiquitous Web to ensure the confidentiality and integrity of data transmitted in the Ubiquitous Web. Specific objectives include evaluating the advantages and disadvantages of current ubiquitous network transport layer lifting security techniques, proposing a new lifting security strategy applicable to the transport layer of ubiquitous networks, and verifying the feasibility and security of the proposed standard.
METHODS: First, a detailed study and evaluation of the current Ubiquitous Network Transport Layer Elevated Security Techniques is conducted, including analyzing and comparing the existing elevated security algorithms and protocols. Then, based on the obtained research results, a new lifting security strategy applicable to the transport layer of ubiquitous networks is proposed. The design process takes into account the characteristics and requirements of ubiquitous networks, such as resource constraints, dynamics of network topology, and cooperative communication of multiple devices. Subsequently, the feasibility and security of the proposed standard are verified through simulations and experiments. In the experiments, real ubiquitous network devices and network environments are used to evaluate the performance and attack resistance of the enhanced security algorithms.
RESULTS: Through the research and analysis of ubiquitous network transport layer lifting security techniques, some limitations of the existing lifting security algorithms are identified, such as high resource consumption, insufficient security, and limited ability to adapt to the characteristics of ubiquitous networks. Therefore, this thesis proposes a new lifting security strategy applicable to the transport layer of ubiquitous networks. The experimental results show that the standard can guarantee data confidentiality and integrity while possessing high efficiency and attack resistance. In addition, the proposed standard meets the needs of resource-constrained devices in ubiquitous networks and can operate properly under multiple network topologies and cooperative device communications.
CONCLUSION: This thesis proposes a new elevated security strategy applicable to ubiquitous networks through the study and design of transport layer elevated security techniques for ubiquitous networks. This standard can effectively protect the confidentiality and integrity of data transmitted in ubiquitous networks with high efficiency and attack resistance. The proposed standard is expected to provide a feasible solution for the information security of ubiquitous networks and a more reliable guarantee for developing and applying ubiquitous networks. Future work can further improve and optimize this enhanced security strategy and validate and apply it in a wider range of ubiquitous network environments.

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Topic Modeling: A Comprehensive Review
Appears in:
sis 20(24): e2

Authors:
Pooja Kherwa, Poonam Bansal

Downloads:
13378

Abstract:
Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented i…Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in this paper. It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its
applications in different areas of technology including Scientific Literature, Bioinformatics, Software Engineering and analysing social network is presented. Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling. At the end paper is concluded with detailed discussion on challenges
of topic modelling, which will definitely give researchers an insight for good research.
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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:
10467

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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Preventing DDoS using Bloom Filter: A Survey
Appears in:
sis 18(19): e3

Authors:
Ripon Patgiri, Sabuzima Nayak, Samir Kumar Borgohain

Downloads:
6579

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 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:
6202

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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A Narrative Literature Review and E-Commerce Website Research
Appears in:
sis 18(17): e1

Author:
K.M. Rahman

Downloads:
5886

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 Hybrid Approach for Breast Cancer Classification and Diagnosis
Appears in:
sis 19(20): e2

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

Downloads:
5787

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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Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM
Appears in:
sis 20(25): e1

Authors:
Satyam Gangwar, Vikram Bali, Ajay Kumar

Downloads:
5351

Abstract:
The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The…The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The availability of wind energy in ample amount makes it quite
comfortable to be utilized for various functionalities. In this research work the main aim is to forecast speed using LSTM including certain parameters and then comparative analysis is done using SVM. Both are machine learning approaches but have different functionalities in comparison to each other. This comparison is done to obtain the better technique which can be further applied on larger datasets to design a better, accurate, efficient forecasting model for speed of wind. The survey and implementation of both the techniques gave a clear idea about the utilisation of long short term memory for the better and enhanced wind speed forecasting. The forecasting is based on various atmospheric variables, and the data set is
taken from the kaggle datsets which have numerous attributes but we have considered few of them only for the prediction purpose.
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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:
5208

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.
more »
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:
5203

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:
4748

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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Publisher: EAI
ISSN: 2032-9407
Number of Volumes: 11

Last Published: 2023-10-05