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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

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), 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

  • Design and Application of Evaluation Method for Civics Classroom Based on CRITIC Fuzzy Algorithm

    Appears in:
    sis 24(1):

    Authors:
    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 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.

  • A New Hybrid COA-OOA Based Task Scheduling and Fuzzy Logic Approach to Increase Fault Tolerance in Cloud Computing

    Appears in:
    sis 24(6):

    Authors:
    Abhishek Swaroop, Vineet Goel, Manoj Kumar Malik

    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 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.

  • A hybrid intrusion detection system with K-means and CNN+LSTM

    Appears in:
    sis 24(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 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.

  • Comprehensive Review of Advanced Machine Learning Techniques for Detecting and Mitigating Zero-Day Exploits

    Appears in:
    sis 24(6):

    Authors:
    Mitra Madanchian, Hamed Taherdoost, Nachaat Mohamed

    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 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.

  • Sentinel Shield: Leveraging ConvLSTM and Elephant Herd Optimization for Advanced Network Intrusion Detection

    Appears in:
    sis 24(6):

    Authors:
    Dinesh Kumar, Aparna Tiwari

    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 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.

  • Analysis of Employment Competitiveness of College Students Based on Binary Association Rule Extraction Algorithm

    Appears in:
    sis 24(5):

    Authors:
    Lixia Guo

    Published:
    3rd May 2024

    Abstract:
      Today, assessing competition among college students in the job search is extremely important. However, various methods available are often inaccurate or inefficient when it comes to determining the level of their readiness for work. Conventional techniques usually depend on simplistic measures or miss out on crucial factors responsible for employability. The challenging characteristics of such competitive employment of college students are the lower levels of perceived stress, financing my education, and crucial professional skills. Hence, in this research, the Internet of Things Based on Binary Association Rule Extraction Algorithm (IoT-BAREA) technologies have improved college students' employment competitiveness. IoT-BAREA addresses this situation using a binary association rule extraction algorithm that helps detect significant patterns and relationships in large amounts of data involving student attributes and employment outcomes. IoT-BAREA positions itself as capable of providing insights into features that highly mediate the employability levels among students. This paper closes this gap and recommends a new IoT-BAREA method to help increase accuracy and efficiency in evaluating student employment competitiveness. Specifically, this study uses rigorous evaluation methods such as precision, recall and interaction ratio to determine how well IoT-BAREA predicts students' employability.

  • Realization of Urban Perception Art: Painting Expressions of Internet of Things Technologies in Urban Environments

    Appears in:
    sis 24(5):

    Authors:
    Hong Zhu, Lu Yao

    Published:
    3rd May 2024

    Abstract:
    INTRODUCTION: With the continuous progress of urbanization, people's perceptions and experiences of the urban environment are increasingly concerned. Traditional forms of artistic expression can no longer fully meet people's needs for urban perception. Therefore, it is especially important to explore new possibilities of urban perception art with the help of modern technology, especially intelligent technology. OBJECTIVES: The main purpose of this study is to explore the feasibility and effectiveness of utilizing advanced technology for urban perception art expression. Through an in-depth understanding of the urban environment and the perceptual needs of urban residents, as well as existing technological means, artistic expressions that can present urban perceptions more intuitively and vividly are developed. METHODS: This study adopts a combination of field research and art practice. Through urban observation and questionnaire surveys, the subjective experience and needs of urban residents for urban perception were collected. Then, using digital painting and video technology, combined with the principles of perception psychology, urban perception works with artistic and technological senses were designed. RESULTS: A series of urban perception artworks were designed in this study, covering all aspects of urban life, including architectural landscapes, transportation scenes, and humanistic customs. These works enable viewers to perceive the urban environment in a more intuitive and immersive way through digital painting and video technology, as well as real-time data and perceptual feedback. CONCLUSION: By exploring new ways of artistic expression of urban perception, this study provides urban residents with a richer and deeper experience of urban perception. The application of digital painting and video technology, as well as the interaction and feedback with urban residents, opens up new possibilities for the development of urban perceptual art.  

  • Design of Intelligent Political Test Paper Generation Method Based on Improved Intelligent Optimization Algorithm

    Appears in:
    sis 24(5):

    Authors:
    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 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.

  • Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

    Appears in:
    sis 24(5):

    Authors:
    Yujie Guo

    Published:
    3rd May 2024

    Abstract:
    Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology.

  • Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests

    Appears in:
    sis 24(5):

    Authors:
    Xiaoyan Wang

    Published:
    3rd May 2024

    Abstract:
    INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production. OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods. METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis. RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy. CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system.  

  • Topic Modeling: A Comprehensive Review

    Appears in:
    sis 19(24): e2

    Authors:
    Poonam Bansal, Pooja Kherwa

    Downloads:
    14226

    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 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.

  • The impact of using social media and internet on academic performance case study Bahrain Universities

    Appears in:
    sis 17(13): e2

    Authors:
    Abdulla Jaafar Desmal

    Downloads:
    10817

    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, 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.

  • Preventing DDoS using Bloom Filter: A Survey

    Appears in:
    sis 18(19): e3

    Authors:
    Sabuzima Nayak, Ripon Patgiri, Samir Kumar Borgohain

    Downloads:
    6823

    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 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.

  • A Parking Management System based on Background Difference Detecting Algorithm

    Appears in:
    sis 15(7): e3

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

    Downloads:
    6436

    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 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.

  • A Narrative Literature Review and E-Commerce Website Research

    Appears in:
    sis 18(17): e1

    Authors:
    K.M. Rahman

    Downloads:
    6083

    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 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.

  • A Hybrid Approach for Breast Cancer Classification and Diagnosis

    Appears in:
    sis 19(20): e2

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

    Downloads:
    6014

    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 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.

  • Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM

    Appears in:
    sis 19(25): e1

    Authors:
    Ajay Kumar, Vikram Bali, Satyam Gangwar

    Downloads:
    5560

    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 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.

  • Stock Price Prediction using Artificial Neural Model: An Application of Big Data

    Appears in:
    sis 19(20): e1

    Authors:
    Sudipta Roy, Malav Shastri, Mamta Mittal

    Downloads:
    5422

    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 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.

  • An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

    Appears in:
    sis 18(18): e5

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

    Downloads:
    5387

    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 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.

  • Machine Learning and Predictive Analysis of Fossil Fuels Consumption in Mid-Term

    Appears in:
    sis 18(15): e4

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

    Downloads:
    4928

    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 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.
Publisher
EAI
ISSN
2032-9407
Number of Volumes
11
Last Published
2024-10-05
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