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

Aims & Scope
Indexing
Editors in Chief
Editorial Board

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)
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)
Nageswara Rao Moparthi (KL University, India)

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

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A Fuzzy Logic Model for the Analysis of Social Corporate Responsibility
Appears in:
sis 18: e14

Authors:
A. C. Golzio, M. Puerta-Díaz, D. Martínez-Ávila

Published:
23rd Jul 2021

Abstract:
INTRODUCTION: Critical public opinion, based on information that is made available to the public through different systems, has led companies that operate in the environment to continually improve their social, environmental, and ethical performance. OBJECTIVES: This paper ai…INTRODUCTION: Critical public opinion, based on information that is made available to the public through
different systems, has led companies that operate in the environment to continually improve their social,
environmental, and ethical performance.

OBJECTIVES: This paper aims to propose a fuzzy-logic-based model for the analysis of social corporate
responsibility in cases of environmental accidents.

METHODS: Our study employs techniques derived from social network analysis. The data was collected from the
online database of The New York Times for the timespan from March 24, 1989, to September 1, 2017.

RESULTS: The results show that the proposed model can be replicated, after some adjustments.

CONCLUSION: We conclude that, despite the complexity of an analysis of this kind in which the model is applied
considering isolated words in the text and not the semantic aspects, the proposed model based on fuzzy logic is
adequate for the analysis of social corporate responsibility.
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Predicting the least air polluted path using the neural network approach
Appears in:
sis 18: e13

Authors:
K. Krishna Rani Samal, Korra Sathya Babu, Santos Kumar Das

Published:
29th Jun 2021

Abstract:
Air pollution exposure during daily transportation is becoming a critical issue worldwide due to its adverse effect on human health. Predicting the least air polluted healthier path is the best alternative way to mitigate personal air pollution exposure risk. Computing the least polluted path for t…Air pollution exposure during daily transportation is becoming a critical issue worldwide due to its adverse effect on human health. Predicting the least air polluted healthier path is the best alternative way to mitigate personal air pollution exposure risk. Computing the least polluted path for the current time might not be helpful for real-time applications. Therefore, we develop a routing algorithm based on a neural network-based CNN-LSTM-EBK (CLE), a temporal-spatial interpolation model. The proposed model predicts pollution levels at high temporal granularity. This paper introduces a weight function to compute air pollution concentration at the road network. It also predicts the least air polluted path among all possible paths from a source to a destination at different time granularity. The results show that the predicted path may be longer than the shortest route but minimize pollution exposure risk all the time, which proves its effectiveness during daily transportation.
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Experimental Comparison of Classification Methods under Class Imbalance
Appears in:
sis 18: e12

Authors:
Hui Chen, Mengru Ji

Published:
11th Jun 2021

Abstract:
The class imbalance problem is prevalent in many domains including medical, natural language processing, image recognition, economic and geographic areas etc. We perform a systematic experimental comparison of different imbalance classification algorithms — ranging from sampling, distance …The class imbalance problem is prevalent in many domains including medical, natural language processing, image recognition, economic and geographic areas etc. We perform a systematic experimental comparison of different imbalance classification algorithms — ranging from sampling, distance metric learning, cost-sensitive learning to ensemble learning approaches — on several datasets from UCI, KEEL and OpenML. The algorithms included DDAE, MWMOTE, SMOTE, RUSBoost, AdaBoost, cost-sensitive decision tree (csDCT), self-paced Ensemble Classifier, MetaCost, CAdaMEC and Iterative Metric Learning (IML). As the substantial bias potentially caused by imbalance classification can be harmful for underrepresented classes which are of critical social and economic values and even lives, the main objective of our study is thus to understand the impact of imbalance ratio and the size of the utilized datasets on the performance of the above-mentioned algorithms. Our experiments show that 1) Sampling methods perform the worst and cannot be used directly for imbalanced classification, since they lack of consideration of neighborhoods based on distance. However, some classifiers can be improved after the balance of class distribution. 2) Cost-sensitive learning models should be utilized when the dataset is less imbalanced, because it is difficult to set an appropriate cost matrix for a specific dataset, which can cause performance fluctuations. 3) IML consistently shows good performance (in terms of F1 and AUCPRC), is resilient to different imbalance ratios but sensitive to the data distribution of the dataset. 4) Ensemble learning techniques generally perform better over other approaches due to theircombined intelligence of multiple basic classifiers. 5) In terms of system performance, self-paced Ensemble Classifier performs fairly well with regards to learning time, while IML and DDAE yield the longest learningtime; AdaBoost and self-paced Ensemble Classifier are two algorithms require lowest memory usage. Wealso provide our empirical recommendation for algorithm selection under different requirements and usagescenarios based on our analysis.
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An Improved Approach for Stress Detection Using Physiological Signals
Appears in:
sis 18: e11

Authors:
Kushagra Nigam, Kirti Godani, Deepshi Sharma, Shikha Jain

Published:
14th May 2021

Abstract:
Stress is a major problem in society. Prolonged stress can lead to ill-health and a decrease in self-confidence. It is necessary to detect stress at an early stage to prevent its adverse effects on our physical and psychological health. The paper presents a stress detection model using physiologica…Stress is a major problem in society. Prolonged stress can lead to ill-health and a decrease in self-confidence. It is necessary to detect stress at an early stage to prevent its adverse effects on our physical and psychological health. The paper presents a stress detection model using physiological signals. In this paper, WESAD (Wearable and Stress Affect Detection) dataset is used which consists of physiological data recorded from both the chest and wrist. Further, a Long Short-Term Memory (LSTM) based model is used to detect stress. The simulation results indicate that, indeed, Electrocardiograph (ECG), Electromyogram (EMG), and Respiration (RESP) signals may not be necessary for identifying stress. A three-way validation is carried out with an accuracy of 98%. The novelty of the paper is the way time-series data is handled to make it closer to real-time data captured from sensors. The work can be used widely in clinical practices to detect stress at an early stage.
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Word Embedding and String-Matching Techniques for Automobile Entity Name Identification from Web Reviews
Appears in:
sis 18: e10

Authors:
Satanu Maity, Nilanjana Das, Mukta Majumder, Dibya Ranjan Dasadhikary

Published:
14th May 2021

Abstract:
With the huge popularity of Internet, various types of information on a wide range of domains are floating over different social media platforms. To extract this information for using in diverse natural language processing applications, identifying the names is prerequisite. A study …With the huge popularity of Internet, various types of information on a wide range of domains are floating over different social media platforms. To extract this information for using in diverse natural language processing applications, identifying the names is prerequisite. A study is presented here, to identify automobile names from noisy web reviews by exploring two widely used machine learning algorithms, Conditional Random Field and Support Vector Machine. The accuracy of machine learning classifiers radically rely on size and quality of training data which has been prepared manually by extracting discussion forum corpus; the task is time consuming and laborious; hence to leverage this word embedding is adopted. Though it enhances the system’s performance but is unable to spot noisy names which occur in web reviews. Next, a gazetteer based string matching technique is proposed, it recognizes a new set of noisy automobile entities, resulting considerable improvement in accuracy.
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Risk Assessment of Type 2 Diabetes Mellitus Prediction using an Improved Combination of NELM-PSO
Appears in:
sis 18: e9

Authors:
Shiva Shankar Reddy, Gadiraju Mahesh

Published:
3rd May 2021

Abstract:
Risk Assessment of Diabetes Type-II is crucial in preventing it and reducing the risk of various comorbidities. There are many existing machine learning models for predicting iType-II diabetics in short term future or in unspecified future. But obtaining a model having …Risk Assessment of Diabetes Type-II is crucial in preventing it and reducing the risk of various comorbidities. There are many existing machine learning models for predicting iType-II diabetics in short term future or in unspecified future. But obtaining a model having optimal performance and predicting diabetes risk in long term future are the main problems. These problems are handled in this work by proposing a stacking based integrated KELM imodel to predict the risk of diabetes Type-II for a person within five years after assessment. The Pima Indian Diabetic Dataset (PIDD) and a Diabetic Research Center dataset are used in this study. A Min-Max normalization is used to pre-process the noisy datasets. The HAFPSO algorithm used in this work explores the best combination of Base learners by increasing the Classification Accuracy (CA) and decreasing the kernel complexity of the optimal learners. Finally, the model is integrated by utilizing the KELM as a meta-classifier that combines the predictions of the twenty Base Learners. The proposed method is assessed with different measures such as accuracy, sensitivity, specificity, Mathews Correlation Coefficient, and Kappa Statistics. The proposed KELM-HAFPSO approach has got better values of the considered metrics confirming its effectiveness in identifying type-II diabetes. The proposed method helps the clinicians to predict the patients who are at a high risk of Type-II diabetes in the future with the highest accuracy of 98.5%. The results obtained show that the KELM-HAFPSO approach is a promising new tool for identifying type-II diabetes.
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Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms
Appears in:
sis 18: e8

Authors:
R. Regin, S. Suman Rajest, Bhopendra Singh

Published:
3rd May 2021

Abstract:
This paper is about Fault detection over a wireless sensor network in a fully distributed manner. First, we proposed the Convex hull algorithm to calculate a set of extreme points with the neighbouring nodes and the duration of the message remains restricted …This paper is about Fault detection over a wireless sensor network in a fully distributed manner. First, we proposed the Convex hull algorithm to calculate a set of extreme points with the neighbouring nodes and the duration of the message remains restricted as the number of nodes increases. Second, we proposed a Naïve Bayes classifier and convolution neural network (CNN) to improve the convergence performance and find the node faults. Finally, we analyze convex hull, Naïve bayes and CNN algorithms using real-world datasets to identify and organize the faults. Simulation and experimental outcomes retain feasibility and efficiency and show that the CNN algorithm has better-identified faults than the convex hull algorithm based on performance metrics.
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A Framework for Grammatical Error Detection and Correction System for Punjabi Language Using Stochastic Approach
Appears in:
sis 18: e7

Authors:
L. Jindal, H. Singh, S. K. Sharma

Published:
27th Apr 2021

Abstract:
INTRODUCTION: In this modern era of internet and technology natural language processing task has emerged as one of the major research area in computer science. Grammatical error detection and correction system assists to detect and correct syntactic errors present in written text. OBJECTIVES: In th…INTRODUCTION: In this modern era of internet and technology natural language processing task has emerged as one of the major research area in computer science. Grammatical error detection and correction system assists to detect and correct syntactic errors present in written text. OBJECTIVES: In this research article, author investigate the applicability of stochastic approach for the development of grammatical error detection and correction system for Punjabi language. METHOD: Author used corpus based stochastic approach to developed the system. The corpus used was taken from Indian language corpora initiative. RESULTS: On testing, the developed system shows a precision as 82.5%, recall as 89% .and f-measure as 85%. The results of the proposed system outperform the existing rule based system that shows precision of 76.79%, recall of 87.08%, and F-measure of 81.61%. CONCLUSION: author concluded that for syntax analysis stochastic approach can perform better than rule based approach.
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Prediction Based Dynamic Controller Placement in SDN
Appears in:
sis 18: e6

Authors:
Ramya G, Manoharan R

Published:
27th Apr 2021

Abstract:
The current technologies such as IoT, 5G networks and Fog computing creates a challenge in the efficient management of devices in dynamic conditions. Software-Defined Network (SDN) has been defined as a promising solution for providing efficient network management by decoupling the data and …The current technologies such as IoT, 5G networks and Fog computing creates a challenge in the efficient management of devices in dynamic conditions. Software-Defined Network (SDN) has been defined as a promising solution for providing efficient network management by decoupling the data and control planes from the network devices and enables programmability of network devices. The major challenge in SDN is identification of number of controllers to be placed and its optimal placements in the network. To address this issue, this work proposes a Traffic Engineering mechanism that leverages the performance of Machine Learning to predict controller numbers by analysing and predicting the controller’s traffic. The optimal locations of controllers are identified by using the K-Means++ algorithm. The proposed method is simulated using Mininet and the results depict that the proposed methodology outperforms the existing methodologies in terms of performance parameters.
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Performance Analysis of Fractional Earthworm Optimization Algorithm for Optimal Routing in Wireless Sensor Networks
Appears in:
sis 18: e5

Authors:
Venkat Rao Pasupuleti, Ch. Balaswamy

Published:
21st Apr 2021

Abstract:
In Wireless Sensor Networks (WSNs), the data transmission from the sensing node to the sink node consumes a lot of energy as the number of communications increases, so the battery life of nodes is limited, and the network also has a limited lifetime. Recent stud…In Wireless Sensor Networks (WSNs), the data transmission from the sensing node to the sink node consumes a lot of energy as the number of communications increases, so the battery life of nodes is limited, and the network also has a limited lifetime. Recent studies show that the bio-inspired meta-heuristic algorithms for solving engineering problems such as energy reduction in autonomous networks in the multidisciplinary areas of WSN, Internet of Things (IoT) and Machine learning models. Hence to increase Network lifetime, optimized clustering and energy-efficient routing techniques are required. In all applications of WSN, not only energy-efficient but also delay and throughput of the network are important for efficient transmission of data to the destination. This paper analyses optimized clustering by selecting cluster heads based on fractional calculus earthworm optimization algorithm (FEWA). The route from cluster heads to sink node is selected based on the fit factor. This paper's main intention is to provide an extensive comparative study of the FEWA with all standard optimization-based clustering and routing techniques. This method's performance is compared with existing optimized clustering methods like GA, PSO, ACO, DE and EWO in terms of the number of energy, delay, and throughput. At the end of 1000 iterations, the analysis shows that the FEWA outperforms existing methods with maximum average remaining energy of the nodes as 0.216J, the minimum average delay of 0.208 sec and maximum average throughput of 88.57% for 100 nodes.
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Machine Learning in Computer Vision: A Review
Appears in:
sis 18: e4

Authors:
Abdullah Ayub Khan, Asif Ali Laghari, Shafique Ahmed Awan

Published:
21st Apr 2021

Abstract:
INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human. The s…INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human. The scenario of manual feeding of the machine is changed in the modern era, it will learn automatically. Supervised and unsupervised ML techniques are used as a distinct purpose like feature extraction, pattern recognition, object detection, and classification.

OBJECTIVES: In Computer Vision (CV), ML performs a significant role to extract crucial information from images. CV successfully contributes to multiple domains, surveillance system, optical character recognition, robotics, suspect detection, and many more. The direction of CV research is going toward healthcare realm, medical imaging (MI) is the emerging technology, play a vital role to enhance image quality and recognized critical features of binary medical image, covert original image into grayscale and set the threshold values for segmentation.

CONTRIBUTION: This paper will address the importance of machine learning, state-of-the-art, and how ML is utilized in computer vision and image processing. This survey will provide details about the type of tools and applications, datasets, and techniques. Limitations of previous work and challenges of future work also discussed. Further, we identify and discuss a set of open issues yet to be addressed, for efficiently applying of ML in Computer vision and image process.

METHODS, RESULTS, AND CONCLUSION: In this review paper, we have discussed the techniques and various types of supervised and unsupervised algorithms of ML, general overview of image processing and the results based on the impact; neural network enabled models, limitations, tools and application of CV, moreover, highlight the critical open research areas of ML in CV.
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Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques
Appears in:
sis 18: e3

Authors:
Vikas Malhotra, Mandeep Kaur Sandhu

Published:
6th Apr 2021

Abstract:
INTRODUCTION: ECG have emerged as the most acceptable and widely used technique to infer mental health status using cardiac signals thereby resolving major challenge of Mental Health Assessment protocols. OBJECTIVES: Authors mainly aimed at identification of stressed signal…INTRODUCTION: ECG have emerged as the most acceptable and widely used technique to infer mental health status using cardiac signals thereby resolving major challenge of Mental Health Assessment protocols.

OBJECTIVES: Authors mainly aimed at identification of stressed signals to distinguish subjects exhibiting stress ECG signals.

METHODS: Authors have taken advantage of three optimization techniques namely, Genetic Algorithm (GA), Artificial Bee Colony (ABC) and improved Particle Swarm Optimization (PSO) that further improves the classification accuracy of Multi-kernel SVM.

RESULTS: The simulation analysis confer that the proposed work outperforms the existing works while demonstrating an average accuracy, precision, recall and specificity of 98.93%, 96.83%, 96.83% and 96.72%, respectively when evaluated against dataset comprising of 1000 ECG samples.

CONCLUSION: It is observed that the proposed stress prediction based on improved VMD and Improved SVM outperformed the existing work that comprised of traditional VMD and SVM.
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g-Sum: A Graph Summarization Approach for a Single Large Social Network
Appears in:
sis 18: e2

Authors:
Saif ur Rehman, Asif Nawaz, Tariq Ali, Naveed Amin

Published:
23rd Mar 2021

Abstract:
With the advances in computing resources, the processing of huge amount of data becomes possible. However, the human ability to identify patterns from such data has not scaled accordingly. Consequently, efficient computational approaches for condensing and simplifying data are b…With the advances in computing resources, the processing of huge amount of data becomes possible. However, the human ability to identify patterns from such data has not scaled accordingly. Consequently, efficient computational approaches for condensing and simplifying data are becoming essential for uncovering actionable insights, especially the big social networks. Many large datasets analyzed today are represented with the help of graphs. In many real-world applications, summarizing large graphs is beneficial to achieve a number of advantages, including 1) significant speedup for graph algorithms, 2) graph storage space reduction, 3) faster network transmission, 4) improved data privacy, 5) more effective graph visualization, etc. All these benefits can be obtained from the summarized graph, if it is summarized accurately. The quality of the summary graph is mostly measured using the Reconstruction Error (RE). In the existing literature, RE is a very challenging task. Most of the approaches investigated so farending up with high value of RE. Hence, the summarized graph does not express the original graph completely due to the high value of the RE. Therefore, in this work we have examined how can we summarize a big graph structure into a compact summary by reducing its RE and ensuring its usefulness. For this task, we have proposed a novel graph summarization approach, called g-Sum, to calculate the graph structure summaries while minimizing RE and compare to the existing work in the domain. The functionality of the g-Sum is decomposed into three different, but interlinked phases; (1)- graph dataset pre-processing; (2)- graph partitioning; and (3)- graph summarization. We have performed an extensive series of experiments on different real-world social graph dataset, including Ego-Facebook, Enron Email and Web-Stanford graph datasets. The computed results show the effectiveness of the g-Sum and also compared with the state-of-the-art graph summarization approaches, S2L and GraSS.
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Big Data Management of Hospital Data using Deep Learning and Block-chain Technology: A Systematic Review
Appears in:
sis 18: e1

Authors:
Nawaz Ejaz, Raza Ramzan, Tooba Maryam, Shazia Saqib

Published:
23rd Mar 2021

Abstract:
The main recompenses of remote and healthcare are sensor-based medical information gathering and remote access to medical data for real-time advice. The large volume of data coming from sensors requires to be handled by implementing deep learning and machine learning algorithms to i…The main recompenses of remote and healthcare are sensor-based medical information gathering and remote access to medical data for real-time advice. The large volume of data coming from sensors requires to be handled by implementing deep learning and machine learning algorithms to improve an intelligent knowledge base for providing suitable solutions as and when needed. Electronic medical records (EMR) are mostly stored in a client-server database and are supported by enabling technologies like Internet of Things (IoT), Sensors, cloud, big data, Deep Learning, etc. It is accessed by several users involved like doctors, hospitals, labs, insurance providers, patients, etc. Therefore, data security from illegal access is crucial especially to manage the integrity of data. In this paper, we describe all the basic concepts involved in management and security of such data and proposed a novel system to securely manage the hospital’s big data using Deep Learning and Block-Chain technology.
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Sequence Classification of Tweets with Transfer Learning via BERT in the Field of Disaster Management
Appears in:
sis 21(31): e8

Authors:
Sumera Naaz, Zain Ul Abedin, Danish Raza Rizvi

Published:
23rd Mar 2021

Abstract:
Twitter is extensively used as an information-sharing platform during any kind of emergency like disasters etc. People tweet useful information about disaster-related events such as evacuations, volunteer need, help, warnings etc. This data is sometimes very useful for rescue …Twitter is extensively used as an information-sharing platform during any kind of emergency like disasters etc. People tweet useful information about disaster-related events such as evacuations, volunteer need, help, warnings etc. This data is sometimes very useful for rescue teams, NGOs, military and various other government and private organisations who are tasked with responsibilities to save lives and provide volunteers. This data can also be used to analyze disaster behaviour. In this paper, we have collected labelled tweets from crisisLexT26 and crisisNLP and classified them into seven labels on the basis of information provided by them. The data was heavily skewed. So to improve the accuracy of classifiers, we have applied various techniques as a result of which we have created two datasets (Imbalanced and Balanced). We have compared the performance of various BERT-based models on these two datasets. For sequence classification, a balanced dataset performs better than an imbalanced dataset. We can improve accuracy of classifiers to great extent by adopting good data preprocessing and data splitting techniques.
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An astute LVQ approach using neural network for the prediction of conditional branches in pipeline processor
Appears in:
sis 21(31): e7

Authors:
Sweety Nain, Prachi Chaudhary

Published:
4th Mar 2021

Abstract:
Nowadays, microprocessors use the deep pipeline to execute multiple instructions per cycle. The frequency and behavior of conditional instructions mainly affect the performance of instruction-level parallelism. However, recent processors still have problems with the correct prediction of conditio…Nowadays, microprocessors use the deep pipeline to execute multiple instructions per cycle. The frequency and behavior of conditional instructions mainly affect the performance of instruction-level parallelism. However, recent processors still have problems with the correct prediction of conditional branches. Firstly, the perceptron neural network and global-based perceptron prediction has been exploited and implemented. Further, a new approach, linear vector quantization (LVQ) neural network, is explored and implemented to see its possibility and potentiality as a branch predictor in terms of accuracy rate. Simulation is performed by varying the parameter of hardware budget and the length of history register using different trace files for identification of the best branch predictor technique. The proposed LVQ perceptron branch predictor achieves an 85.56% accuracy rate using a hardware budget and an 86.36% accuracy rate in terms of history length by comparing the simulation results.
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An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network
Appears in:
sis 21(31): e6

Authors:
Navpreet Kaur, Inderdeep Kaur Aulakh

Published:
25th Feb 2021

Abstract:
Clustering is known to conserve energy and enhance the network lifetime of Wireless Sensor Network (WSN). Although, the topic of energy efficiency has been well researched in conventional WSN, but it has not been extensively studied. In theresearch, Reinforcement Learning (RL) ba…Clustering is known to conserve energy and enhance the network lifetime of Wireless Sensor Network (WSN). Although, the topic of energy efficiency has been well researched in conventional WSN, but it has not been extensively studied. In theresearch, Reinforcement Learning (RL) based energy-aware clustering algorithm is proposed by which the neighboring nodes in the cluster selects an appropriate Cluster Head ( CH) by observing the environmental conditions like as energy consumptionand coverage that is computed as distance from the CH to the Base Station (BS). An optimal cluster is selected by each neighboring node, which minimized the energy consumption and network lifetime. The problem of selecting an optimal CH is resolved using the RL approach. Using the RL approach, the CH having the highest reward point is selected for data communication. The results show that energy saving of 7.41%, 3.27%, 4.03%, and 2.79 % is achieved for 100, 200, 300, and 400 deployed nodes, respectively.
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HHO-LPWSN: Harris Hawks Optimization Algorithm for Sensor Nodes Localization Problem in Wireless Sensor Networks
Appears in:
sis 21(31): e5

Authors:
Ravi Sharma, Shiva Prakash

Published:
25th Feb 2021

Abstract:
Wireless sensor network (WSN) is a prominent technology for remote area monitoring with the assimilation of the Internet of Things (IoT). Over the past decades, sensor node localization has become an essential challenge of WSNs. The sensor indicates localization challenges related to NP-hard …Wireless sensor network (WSN) is a prominent technology for remote area monitoring with the assimilation of the Internet of Things (IoT). Over the past decades, sensor node localization has become an essential challenge of WSNs. The sensor indicates localization challenges related to NP-hard problems. Nature-inspired computational intelligence algorithms are used to solve NP-hard problems efficiently for sensor node localization. After the rigorous advanced search in reputable research journals, efficient newly designed Harris Hawks Optimization (HHO) algorithm has not been used to sensor nodes localization until now. Therefore, this paper does and compares the proposed work from the recently available well-known optimization algorithms such as the Salp Swarm Algorithm (SSA), Equilibrium Optimizer (EO), and Grey Wolf Optimizer (GWO). The simulation results of the proposed work showed that it can outperform in terms of average localization error, the number of localized sensor nodes, and computational cost compared to other computational intelligence algorithms.
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A Greedy Technique Based Improved Approach to Solve Graph Colouring Problem
Appears in:
sis 21(31): e4

Authors:
Ajay Narayan Shukla, Vishal Bharti, M. L. Garg

Published:
16th Feb 2021

Abstract:
Graph colouring problem is a well-known NP-class optimization problem, studied due to a lot of applications in various real-world problems. Some of these applications are: register allocation, image processing and communication networks. There are various techniques suggested …Graph colouring problem is a well-known NP-class optimization problem, studied due to a lot of applications in various real-world problems. Some of these applications are: register allocation, image processing and communication networks. There are various techniques suggested by the researchers to solve the problem which is either exact or approximate in nature. In this paper, a new greedy technique, based on degrees of vertices in the graph is presented to solve the graph colouring problem in an improved manner. The technique involves the use of adjacency matrix along with another matrix generated for the set of possible colours for each vertex in the graph. The generated colour matrix is used to assign the colours among the vertices in the graph based on decreasing degrees of the vertices. Several DIMACS colouring instances solved using the suggested approach in the article and compared with some contemporary techniques for the performance and proves compatible and having better execution time with compared technique. The obtained colouing results are mostly optimal colour values corresponding to the examined colouring instances of the graph.
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Efficient Framework for Sentiment Classification Using Apriori Based Feature Reduction
Appears in:
sis 21(31): e3

Authors:
Achin Jain, Vanita Jain

Published:
16th Feb 2021

Abstract:
This paper proposes a novel feature selection method for Sentiment Classification. UCI ML Dataset is selected having a textual review from three domains (IMDB Movie, AMAZON Product, and YELP restaurant). Text pre -processing and feature selection technique is applied to the dataset. A Novel Feature…This paper proposes a novel feature selection method for Sentiment Classification. UCI ML Dataset is selected having a textual review from three domains (IMDB Movie, AMAZON Product, and YELP restaurant). Text pre -processing and feature selection technique is applied to the dataset. A Novel Feature Selection approach using Association Rule Mining is presented in which Sentence is converted in binary form and Apriori Algorithm is applied to reduce the dataset. Four Machine Learning algorithms: Naïve Bayes, Support Vector Machine, Random Forest & Logistic Regression to implement experiment. The proposed approach shows an accuracy improvement of 4.2%, 4.9% & 5.9% for IMDB, Amazon & Yelp domain datasets, respectively. Compared with the Genetic Algorithm, Principal Component Analysis, Chi-Square, and Relief based feature selection, the proposed method shows an accuracy improvement of 9.8%, 0.4%, 0.6% & 1.9%, respectively.
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Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study
Appears in:
sis 21(31): e2

Authors:
Ravinder Singh, Yanchun Zhang, Hua Wang, Yuan Miao, Khandakar Ahmed

Published:
26th Oct 2020

Abstract:
Location-based social networks such as Swarm provide a rich source of information on human behaviour and urban functions. Our analysis of data created by users who voluntarily used check-ins with a mobile application can give insight into a user’s mobility and behaviour patterns. I…Location-based social networks such as Swarm provide a rich source of information on human behaviour and urban functions. Our analysis of data created by users who voluntarily used check-ins with a mobile application can give insight into a user’s mobility and behaviour patterns. In this study, we used location-sharing data from Swarm to explore spatio-temporal, geo-temporal and behaviour patterns within the city of Melbourne. Moreover, we used several tools for different datasets. We used the MeaningCloud tool for sentiment analysis and the LIWC15 tool for psychometric analysis. Also, we employed SPSS software for the descriptive statistical analysis on check-in data to reveal meaningful trends and attain a deeper understanding of human behaviour patterns in the city. The results show that most people do not express strong negative or positive emotions in relation to the places they visit. Behaviour patterns vary based on gender. Furthermore, mobility patterns are different on different days of the week as well as at different times of a day but are not necessarily influenced by the weather.
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Evolving A Neural Network to Predict Diabetic Neuropathy
Appears in:
sis 21(31): e1

Authors:
Shiva Shankar Reddy, Gadiraju Mahesh, N. Meghana Preethi

Published:
26th Oct 2020

Abstract:
One of the main areas where machine learning (ML) techniques are used vastly is in prediction of diseases. Diabetic neuropathy (DN) disease is a complication of diabetes which causes damage to nerves. Early prediction of DN helps diabetic patient to avoid its complications. The mai…One of the main areas where machine learning (ML) techniques are used vastly is in prediction of diseases. Diabetic neuropathy (DN) disease is a complication of diabetes which causes damage to nerves. Early prediction of DN helps diabetic patient to avoid its complications. The main aim of this work is to identify various risk factors of DN and predict it accurately using ML techniques. Radial basis function (RBF) network is an artificial neural network proposed to obtain better results than traditional ML classification techniques. CART, random forest and logistic regression are existing classification techniques considered. Accuracy, recall, f1 score, area under ROC curve (AUC), Matthews correlation coefficient (MCC) and kolmogorov-smirnov (KS) statistic are performance metrics used to evaluate and compare algorithms. From comparative study it was observed that proposed technique RBF network performed better. The performance metric values obtained for RBF network are accuracy-68.18%, recall-0.909, f1score-0 .7407, AUC-0.6405, MCC-0.4082 and KS statistic-0.5417. Accordingly, the use of RBF network while predicting DN gives accurate and better results.
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SecHMS- A Secure Hybrid Monitoring Scheme for Cloud Data Monitoring
Appears in:
sis 21(30): e8

Authors:
Anuj Kumar Yadav, Ritika Ritika, M. L. Garg

Published:
23rd Oct 2020

Abstract:
Cloud computing enables the data owners to store and host their data on the cloud storage servers, which is to accessed by data consumers from the cloud storage servers whenever needed. The approach provides not only better accessibility but also unlimited storage capacity to the users. Even thou…Cloud computing enables the data owners to store and host their data on the cloud storage servers, which is to accessed by data consumers from the cloud storage servers whenever needed. The approach provides not only better accessibility but also unlimited storage capacity to the users. Even though there are numerous benefits of the approach, but security remains the prime concern for cloud computing and cloud service users. There are few solutions that provide protection in cloud computing, but even after having these solution, trust in cloud service providers remains the prime concern for cloud users. To overcome such a situation and to enhance the faith amongst the user and cloud service provider, a hybrid monitoring scheme (SecHMS) have been proposed and evaluated in this paper. The hybrid monitoring scheme (SecHMS) uses public-key cryptography and hashing technique to provide data security in cloud computing. The hybrid scheme (SecHMS) constantly monitors the stored data on behalf of end-users. As user’s data is getting continuously monitored, it leads to the enhancement of trust for the end-user in cloud computing systems. A thorough analysis has been done on different size files, and results have been demonstrated to show the correctness of SecHMS scheme.
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An analysis of security challenges and their perspective solutions for cloud computing and IoT
Appears in:
sis 21(30): e7

Authors:
Muhammad Hassan Ghulam Muhammad, Tahir Alyas, Faraz Ahmad, Fatima Hassan Butt, Wajahat Mahmood Qazi, Shazia Saqib

Published:
23rd Oct 2020

Abstract:
INTRODUCTION: With the on-going revolution inthe Internet of Things and Cloud Computing has made the potential of every object that is connected through the Internet, to exchange and transfer data. Various users perceive this connection and interaction very helpful and serviceable in their daily ro…INTRODUCTION: With the on-going revolution inthe Internet of Things and Cloud Computing has made the potential of every object that is connected through the Internet, to exchange and transfer data. Various users perceive this connection and interaction very helpful and serviceable in their daily routines.
OBJECTIVES: The objective of this research to identify the complex configured network system is a soft target to security threats, therefore we need a security embedded framework for IoT and cloud communication models. Another objective is to provide protection of information from unauthorized access controls in IoT-cloud integrated framework and secure data from spying.
METHODS: This paper has applied an integrated IoT-cloud theoretical solution, whose activities are mainly decided by a centralized controller to provide safeguard against data attacks. Our theoretical integrated IoT-cloud theoretical solution is able to achieve unauthorized access control and data breach. RESULTS: Internet of things and cloud computing has intensively used by several real-time applications. After the thercical analysis, the different vulnerabilities explained after detail literature review to prevent unauthorized access and unauthorized data breach.
CONCLUSION: Internet of things have changed the shape of communicationand centralized data controller is the main entity that is robust against eavesdroppers. In case, any eavesdropper tries to be a normal user and attempts to access a personal file then he has been entertained with a misleading file that he considers as an authentic file but in actual it is not. Desirable IoT proposed solutions need to be design and deploy, which can guarantee: anonymity, confidentiality, andintegrity in heterogeneous environments.
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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:
5458

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:
2601

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:
2344

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:
2234

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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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:
2101

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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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:
2020

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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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:
1884

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:
1729

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

Authors:
Satyam Gangwar, Vikram Bali, Ajay Kumar

Downloads:
1627

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

Authors:
Pooja Kherwa, Poonam Bansal

Downloads:
1398

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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Publisher: EAI
ISSN: 2032-9407
Number of Volumes: 8

Last Published: 2021-06-29

EAI Endorsed Transactions on Scalable Information Systems

Hua Wang1

Xiaohua Jia2

Manik Sharma3

Hua Wang¹

Xiaohua Jia²

Manik Sharma³