Browsing by Author "Bhoopesh Singh Bhati"

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    Performance Evaluation of various ML Algorithms for PCOS Diagnosis
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sonam Juneja; Pannee Suanpang; Manoj Gupta; Nitesh Kumar Bhati; Bhoopesh Singh Bhati; Chanyanan Somthawinpongsai; Aziz Nanthaamornphong; S. Juneja; Department of CSE, Chandigarh University, Gharuan, India; email: sonam.december@gmail.com
    PCOS is a common endocrine disturbance leading to anovulation and subsequent severe health disorders such as cardiovascular events, type 2 diabetes, and infertility. An early and correct diagnosis is crucial to managing the disease and optimizing clinical outcomes. Traditional methods used to diagnose PCOS involve physical examinations and hormone testing but are not always conclusive, particularly at an initial stage. One technology that could substitute exploring large datasets and identifying magnified patterns that might assist in predict disease is machine learning. In this paper, we examine whether numerous ML algorithms can recommend the possibility for women to have PCOS. We use a dataset from a Google Collaboratory in which our features differ from the traditional diagnostic criteria for PCOS. This is the distinctive part of the study that enables us to test the possible advantages and disadvantages of including this extra information in the forecasting models. We will test the efficacy of such characteristics in determining the precise PCOS patients across a variety of classification models. The result of the current research will contribute to the growing body of evidence suggesting that machine learning may be used to identify diseases sooner, and promote women better manage their health. © 2024 IEEE.
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    SPSO-EFVM: A Particle Swarm Optimization- Based Ensemble Fusion Voting Model for Sentence-Level Sentiment Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2024) Dimple Tiwari; Bharti Nagpal; Bhoopesh Singh Bhati; Manoj Gupta; Pannee Suanpang; Sujin Butdisuwan; Aziz Nanthaamornphong; M. Gupta; Guru Ghasidas Vishwavidyalaya Bilaspur, Department of Electrical Engineering, Bilaspur, Koni, Chhattisgarh, 495009, India; email: manojgupta35@yahoo.co.in; A. Nanthaamornphong; Prince of Songkla University, College of Computing, Phuket, 83120, Thailand; email: aziz.n@phuket.psu.ac.th
    Sentiment analysis has received incremental growth in recent years for emerging applications, including human-robot integration, social platforms monitoring, and decision-support systems. Several neural or transformer model-based solutions have been provided in the field of sentiment analysis that relies on the decision of a single classifier or neural model. These are erroneous to encode contextual information into appropriate dialogues and increase extra computational cost and time. Hence, we proposed a compact and parameter-effective Particle Swarm Optimization-based Ensemble Fusion Voting Model (PSO-EFVM) that exploited the combined properties of four ensemble techniques, namely Adaptive-Boost, Gradient-Boost, Random-Forest, and Extremely-Randomized Tree with Particle Swarm Optimization (PSO)-based hyperparameter selection. The proposed model is investigated on five cross-domain datasets after applying the foremost initialization and feature extraction using Information Gain (IG). It employs adaptive and gradient learning to incorporate the automatic attribute selection with the arbitrary loss function optimization. In short, a generalized two-block composite classifier is designed to perform context compositionality and sentiment classification. A population-based meta-heuristic optimization PSO is applied to each base ensemble learner that calculates weights for the best parameter selection. Comprehensive investigations of different domains reveal the superiority of the proposed PSO-EFVM over established baselines and the latest state-of-the-art models. © 2013 IEEE.