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  • Open Access


    Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

    Mian Muhammad Danyal1, Sarwar Shah Khan2,4, Muzammil Khan2,*, Muhammad Bilal Ghaffar1, Bilal Khan1, Muhammad Arshad3

    Journal on Big Data, Vol.5, pp. 1-18, 2023, DOI:10.32604/jbd.2023.041319

    Abstract Movies are the better source of entertainment. Every year, a great percentage of movies are released. People comment on movies in the form of reviews after watching them. Since it is difficult to read all of the reviews for a movie, summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews. Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data. Opinion mining involves identifying and extracting the opinions of individuals, which can be positive, neutral, or negative. The… More >

  • Open Access


    An Efficient Stabbing Based Intrusion Detection Framework for Sensor Networks

    A. Arivazhagi1,*, S. Raja Kumar2

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 141-157, 2022, DOI:10.32604/csse.2022.021851

    Abstract Intelligent Intrusion Detection System (IIDS) for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall. The efficiency of IIDS highly relies on the algorithm performance. The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms. Here, a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework (SILF), is proposed to learn the attack features and reduce the dimensionality. It also reduces the testing and training time effectively and enhances Linear… More >

  • Open Access


    Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

    S. Prakash1,*, P. Vishnu Raja2, A. Baseera3, D. Mansoor Hussain4, V. R. Balaji5, K. Venkatachalam6

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1273-1287, 2022, DOI:10.32604/csse.2022.021784

    Abstract Urban living in large modern cities exerts considerable adverse effects on health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanized countries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples is becoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions. The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the… More >

  • Open Access


    Filter-Based Feature Selection and Machine-Learning Classification of Cancer Data

    Mohammed Farsi*

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 83-92, 2021, DOI:10.32604/iasc.2021.015460

    Abstract Microarray cancer data poses many challenges for machine-learning (ML) classification including noisy data, small sample size, high dimensionality, and imbalanced class labels. In this paper, we propose a framework to address these problems by properly utilizing feature-selection techniques. The most important features of the cancer datasets were extracted with Logistic Regression (LR), Chi-2, Random Forest (RF), and LightGBM. These extracted features served as input columns in an applied classification task. This framework’s main advantages are reducing time complexity and the number of irrelevant features for the dataset. For evaluation, the proposed method was compared to… More >

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