Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Time series with sentiment analysis for stock price prediction(Institute of Electrical and Electronics Engineers Inc., 2019) Sharma, V.; Khemnar, R.; Kumari, R.; Mohan, B.R.Stock price prediction has been a major area of research for many years. Accurate predictions can help investors take correct decisions about the selling/purchase of stocks. This paper aims to predict and gauge stock costs and patterns, utilizing the power of machine learning, content examination and fundamental analysis, to give traders a hands-on tool for keen speculations particularly for the volatile Indian Stock Market. We propose a technique to analyze and predict the stock price with the help of sentiment analysis and decomposable time series model along with multivariate-linear regression. © 2019 IEEE.Item Hardware based Analysis of Deep Neural Networks(Institute of Electrical and Electronics Engineers Inc., 2023) Karia, A.; Patil, A.; Apoorva, M.K.; Varsha, P.; Pillay, S.; Mohan, B.R.The advent of the Machine Learning (ML) era is now evident and its inculcation into early education requires students to have feasible options to work with the field. The proposed comparative analysis tests different frameworks namely sci-kit with Keras, Tensor-flow and PyTorch on various available processors and discussed further using a range of standard metrics to evaluate the model and well as the underlying hardware that they will run upon. Based on the study, the most viable combination of framework and hardware for educational purpose shall be found out. © 2023 IEEE.Item Hybrid Genetic Algorithm and Machine Learning Approach for Software Reliability Assessment in Safety-Critical Systems(Institute of Electrical and Electronics Engineers Inc., 2024) Goyal, G.; Sharma, K.; Anshuman; Mittal, V.; Singla, B.; Das, M.; Mohan, B.R.Software reliability is a paramount determinant of software quality. In this research paper, we delve into utilizing Genetic Algorithms (GAs) for feature selection and classification. We undertake a comprehensive evaluation and comparative analysis of Machine Learning models, specifically Random Forest and Logistic Regression, both with and without Genetic Algorithmdriven feature selection. Our findings substantiate the significant impact of Genetic Algorithms in improving the accuracy of software reliability analysis. © 2024 IEEE.Item Revealing Insights: Sentiment Analysis of Indian Annual Reports(Institute of Electrical and Electronics Engineers Inc., 2024) Chaithra; Mohan, B.R.Annual reports are the corporate documents companies publish every year. These documents contain crucial company performance information and are often analyzed manually and objectively. The Investor often ignores the annual report's qualitative data and focuses only on quantitative data. In literature, it has been demonstrated that managers' word choices, CSR initiatives, and sentiments expressed in annual reports are related to future stock returns, earnings, and management fraud. Therefore, the study aims to observe sentiment orientation in CEO letters, Management Discussion and Analysis(MD&A), and Corporate Social Responsibility (CSR) and examine the sentiment relation with company performance. The NSE-listed company annual reports are considered for the study. In the proposed approach, the results of the LM Dictionary-Based technique, Naive Bayes, SVM, RF, LSTM, and FinBERT model are considered to determine the final sentiment. The annual report tone is calculated and compared with the performance indicators, i.e., Return on Assets(ROA) and Return on Equity(ROE). © 2024 IEEE.Item HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Software Defect Prediction Framework(IEEE Computer Society, 2025) Das, M.; Mohan, B.R.; Guddeti, R.M.R.Defect prediction plays a crucial role for any software system across various domains, as its failure may cause unavoidable and undeniable scenarios. For reliable software, defect-free is considered as one of the most important criteria. This research aims to design a hybrid swarm-optimized machine learning software defect prediction (HSoMLSDP) framework to predict software defects. We strive to do this by designing a swarm-optimized machine learning defect prediction (SoMLDP) model within the HSoMLSDP framework. In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper introduces a hybrid swarm optimization algorithm (SOA) referred to as the gravitational force Lévy flight grasshopper optimization algorithm-artificial bee colony (GFLFGOA-ABC) algorithm. By combining the enhanced exploration feature of the gravitational force Lévy flight grasshopper optimization algorithm (GFLFGOA) with the robust exploitation capacity of the artificial bee colony (ABC), the GFLFGOA-ABC algorithm is proposed. Prior to validating the HSoMLSDP framework, the LFGFGOA-ABC algorithm's performance is first confirmed by experiments on 6 benchmark functions (BFs) to assess its mean and convergence rate. Following BF verification, the second experiment tunes the hyperparameters of ML models (ANN, GB, XGB) to improve the defect accuracy of the SoMLDP model. As an enhancement of accuracy justifies the correctness of the SoMLDP model, thus validating the HSoMLSDP framework. © 2025 IEEE.Item Improving Machine Learning Models with Hybrid Metaheuristic Algorithm for Software Defect Prediction(Springer Science and Business Media Deutschland GmbH, 2025) Das, M.; Prasad, N.; Mohan, B.R.Software defect prediction has always been an area of interest in the field of software engineering. As the prediction of software defects plays a vital role, researchers are focusing more on metaheuristic algorithms to develop better prediction models. In this paper, we focused on the parameter tuning of the machine learning (ML) models using hybrid metaheuristic algorithms. Here, we have used three metaheuristic algorithms, namely sparrow search, wolf pack, and artificial bee colony optimization algorithm (ABC), to optimize the hyperparameters of the ML model. We have developed a hybrid version of these algorithms for better performance. The sparrow search algorithm (SSA) has high search accuracy and slow convergence speed with the advantages of good stability and strong robustness. The Wolf Pack Algorithm (WPA) has a robust global optimization ability, fast convergence speed, and various optimization strategies. The artificial bee colony (ABC) optimization algorithm has the advantage of not being influenced by the initial parameters, thus enabling search in a wider search space. Considering the strongest features of the aforementioned algorithms, two new hybrid algorithms have been developed, namely sparrow search algorithm-wolf pack algorithm (SSA-WPA) and sparrow search algorithm-artificial bee colony (SSA-ABC). These two algorithms are combined with the artificial neural network and XGBOOST model for better accuracy. To achieve the correctness of the proposed method, it is being verified by five defective NASA datasets and compared with the base methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.