Volume-11 , Special Issue-01 , Nov - 2023

Disaster means a calamity which ruins the human civilization time to time at a bigger platform in many ways, it may be natural or it may be manmade, now days we are observing that almost all part of the world is suffering from CORONA a deadliest virus seems to be made by china as report and all countries corps are assuming or predicting the exact reason from year 2019 to till date. Above problem is the example of manmade disaster but apart from this we know the different types of disaster and their consequences in terms of loss in various ways. Its shows bad impact in almost all part of life from bottom to top as it influences life on all three platform i.e. socially, economically, and politically as a result peoples are start dying in lack of food and all various things which they need to survive their life on daily basis. Some of them will not only lose their family they will lose their shelter also by keeping all scenarios, Here we will try to build a completely different model which not only save the human life rather it will provides strength economically in such way that if happened such things then we all together can face any kind of problem and find a way to get out of it and also predict the future disaster in advance by applying Machine learning methods.

Disaster, Earthquake, drought, Covid19, epidemic, AI, big data, etc.

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Nirbhay Mishra, Dharmpal Singh, Amit Majumdar, Radha krishna Jana, Shinjini Nag, Manish Gupta, "Analysis of Disaster’s and its Management Using Big Data Analytics," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.1-11, 2023.

Gauging the pulse of political climes and global tides alike, the stock exchange carries considerable consequence. Given the capricious manner by which the apparatus maneuvers, present technological means and trade tenets forbid accurately foretelling how planetary perturbations may sway share values over extended time. Of late, nonetheless, informatic techniques and artificially adroit algorithms have enabled scrutinizing episodes and occurrences that surface symbiotically, prognosticating the trajectory of equity prices for most, if not all, corporations in qualifying bourses. Past the fluctuating cultural zeitgeists and mercantile vicissitudes defining our epoch`s global political topography, prognosticating distant futures proves an elusive enterprise. Our objective henceforth shall be to prognosticate the quotidian inclination of equity premiums in the stock bazaar with the utmost precision achievable. The Stacking Ensemble model is a prevalent and trustworthy fashion to anticipate chronological successions. We shall explain why we opt for it over and above the other techniques adduced, akin to Meta Prophet, and the LSTM modus operandi, posterior to assaying. We manipulate a combination exemplar to ascertain the weighted norm of all in-sample data. In this exploration, we endeavored to disparate things with the stock valuations of multiple shareholding fully remunerated ordinary portions and thereafter effectuated predictions about stock valuations.

Stock price forecast, ARIMA, SARIMA, LSTM, Meta Prophet, GRU, Kalman Filter, Ensemble

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[3]. Taylor, Sean J. and Letham, Benjamin, “Forecasting at Scale“, The American Statistician, Vol.72, pp.37-45, 2018. http://doi.org/10.1080/00031305.2017.1380080
[4]. Chatfield, Chris, “The Analysis of Time Series: An Introduction”, Sixth Edition, Taylor & Francis, Chapman and Hall/CRC, United States of America, 2003, ISBN – 9781584883173
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Mehul Kundu, Arpan Bose, Debasmita Guha, Romit Das, Aftab Alam, "Comparative Study of Time Series Algorithms on Stock Price Forecasting," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.12-28, 2023.

Suicide has emerged as a pressing societal health concern in contemporary times. Suicidal intent refers to an individual`s contemplation of taking one’s own life, and such tragedies have far-reaching impacts on families, communities, and nations. The global standardized rate of suicides per population suggests that in 2022, there were approximately 903,450 completed suicides, alongside a staggering 18,069,000 cases of individuals having suicidal thoughts but not acting upon them. These distressing figures highlight the widespread nature of this issue, affecting people of all ages, nationalities, races, beliefs, socioeconomic backgrounds, and genders. Additionally, it is pertinent to acknowledge that depression, a prevalent mental disorder, can significantly hinder daily functioning and potentially contribute to developing suicidal thoughts. This work focuses on detecting suicidal intent by employing Machine Learning classifiers such as Support Vector Machines, Naive Bayes, Logistic Regression, and Random Forest. Furthermore, this research extends its analysis by incorporating Deep Learning classifiers such as Convolutional Neural Networks, Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Bidirectional Encoder Representations from Transformers on six selected datasets. The primary aim is to identify signs of depression as a means to gauge the likelihood of suicidal thoughts. In addition to the classification algorithms, various features are extracted to provide insights into an individual`s emotional state and mindset. By combining these techniques, the study aims to improve the understanding and prediction of suicidal tendencies.

Depression, Suicide, Depression Detection, Suicidal Ideation, Text Classification, Machine Learning, Deep Learning.

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Sumit Gupta, Arya Manikya Sinha, Debjyoti Prodhan, Nirnay Ghosh, Souvik Modak, "Detecting Depression and Suicidal Ideation from Texts using Machine Learning & Deep Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.29-35, 2023.

The advent of Chat-GPT, an advanced conversational AI technology developed by OpenAI, has revolutionized human-computer interactions and holds significant implications for India and the global landscape. This research paper explores the impact of Chat-GPT on various domains, including customer service, education, content generation, and communication, and investigates the potential benefits, challenges, and implications that arise from its widespread adoption. Chat-GPT, powered by the Transformer architecture, has the ability to understand and generate human-like text, making it a powerful tool in natural language processing and machine learning. In customer service, businesses in India can leverage Chat-GPT-powered chatbots to enhance customer experiences, streamline support processes, and increase satisfaction by providing prompt and accurate responses. Similarly, in education, Chat-GPT can offer personalized learning experiences, aiding students with tutoring, homework assistance, and access to educational resources, ultimately enhancing engagement and learning outcomes. The transformative potential of Chat-GPT extends to content generation and curation, where it can automate the creation of written content, saving time and effort for content creators in India. Furthermore, the adoption of AI technologies like Chat-GPT can foster economic growth, drive digital transformation, and create job opportunities in AI-related fields, contributing to India`s socio-economic development. However, the integration of Chat-GPT also presents challenges and ethical considerations. Issues such as bias, fairness, privacy, and transparency must be addressed to ensure responsible and beneficial use of the technology. Policymakers, businesses, and researchers play a crucial role in navigating these challenges to maximize the positive impact of Chat-GPT while mitigating potential drawbacks. Through a comprehensive analysis of existing literature, case studies, and expert insights, this research paper provides valuable insights and recommendations for stakeholders. It highlights the transformative potential of Chat-GPT and the need for responsible integration into various spheres of society. By understanding the impact of Chat-GPT on India and the wider world, this research aims to contribute to the advancement of AI technologies and their ethical deployment, ultimately shaping a future where AI serves the collective benefit of society.

Chat-GPT, OpenAI, Artificial Intelligence, Neural network, Reinforcement learning etc.

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[3].Kalla, D. and Smith, N., 2023. Study and Analysis of Chat GPT and its Impact on Different Fields of Study. International Journal of Innovative Science and Research Technology, 8(3), 2023.
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[5]. George, A.S. and George, A.H., 2023. A review of ChatGPT AI`s impact on several business sectors. Partners Universal International Innovation Journal, 1(1), pp.9-23.
[6]. YU, H., Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching. Frontiers in Psychology, 14, p.2156.
[7]. Cano, C.A.G., Castillo, V.S. and Gallego, T.A.C., 2023. Unveiling the Thematic Landscape of Generative Pre-trained Transformer (GPT) Through Bibliometric Analysis. Metaverse Basic and Applied Research, 2, pp.33-33, 2023.
[8]. George, A.S., George, A.H. and Martin, A.G., 2023. The Environmental Impact of AI: A Case Study of Water Consumption by Chat GPT. Partners Universal International Innovation Journal, 1(2), pp.97-104, 2023.
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[10]. Verma, M., Analyzing the Innovative Challenges and Possible Solutions of Polymer and Related Material based on AI Chatbot (Chat GPT) Responses.
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[12]. Ray, Partha Pratim, and Poulami Majumder. "AI Tackles Pandemics: ChatGPT`s Game-Changing Impact on Infectious Disease Control." Annals of Biomedical Engineering: pp.1-3, 2023.
[13]. Singh, S., Tiwari, S., Yadav, P.K., Kumar, A. and Srivastava, V., CHAT GPT: EXPLORING THE CAPABILITIES AND LIMITATIONS OF A LARGE LANGUAGE MODEL.
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[16]. Biswas, S., Joshi, N. and Mukhopadhyaya, J.N., ChatGPT in Investment Decision Making: An Introductory Discussion.
[17]. Feng, Y., Poralla, P., Dash, S., Li, K., Desai, V. and Qiu, M., 2023, May. The impact of chatgpt on streaming media: A crowdsourced and data-driven analysis using twitter and reddit. In 2023 IEEE 9th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), pp.222-227, 2023. IEEE.
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[19]. Suganya, P. and Pranesh, K., 2023. A REVIEW OF CHATGPT AI’S BENEFITS AND IMPACT ON E-COMMERCE SECTORS. EPRA International Journal of Research and Development (IJRD), 8(4), pp.323-325, 2023.
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[29].https://www.ndtv.com/india-news/chatgpt-maker-ceo-sam-altman-meets-pm-modi-discusses-global-regulation-for-ai-4106852

Nirbhay Mishra, Manish Gupta, Kumar Harsh, Divya Ojha, Ayushman Mitra, Onkar Gupta, "Exploring the impact of Chat-GPT on India and global socioeconomic Landscape: Opportunities, Challenges, and Implications," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.36-48, 2023.

It is very important to predict car price before launching it in the market. In the research, regression models are developed to predict the price of the car. Three models have been developed in the research paper: Backward Elimination, Backward Elimination with VIF, and forward selection. The data is taken from Kaggle. The most important factors are decided by correlating other variables with the car price. A linear regression model is finally developed, with engine size as the most influencing factor, the type of driver as the second influencing factor, and the type of the car body as the third influencing factor. Linear regression model predicts the car price with good model accuracy. The exploratory data analysis is done to know about the data set. The variables having variance influence factor r more than ten are omitted to avoid the problem of multicollinearity. The first model developed is forward selection, in which engine size is used to build the first regression model having a single variable. The value of adjusted R2 is 0.764, and the aim is to increase the value of this factor, and all the coefficients in this model are statistically significant. The second variable included is the type of carburetor (2bbl) that is incorporated in the model, and a regression model is developed. The adjusted R2 is 0.778 and all the coefficients are statistically significant. The third regression model is developed by incorporating types of the drive (Reverse drive), and the value of adjusted R2 is 0.802, and all the coefficients are statistically significant. Further, it was tried by the hit and trial method incorporated the other variables in the model to increase the model accuracy and adjusted R2, but there was no benefit. The types of variables are selected based on correlation and VIF. The second approach adopted to build the regression model is backward elimination, in which all the variables are included and eliminated one by one based on VIF and statistical significance. The adjusted R2 is 0.919, but some variables are statistically insignificant as the p-value is more than 0.05 with a 95% confidence interval. After eliminating all the variables having VIF of more than ten and statistically significant, the final regression model has adjusted R2 is 0.863. The third approach adopted is backward elimination, where only statistically significant factors are considered. The final regression model with backward elimination has adjusted R2 is 0.863. Finally, we recommend the forward selection method of regression to predict the price of the car as it has less omitted variables bias.

Linear regression, correlation, forward selection, backward elimination, data analysis, Backward elimination

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Mohd. Vaseem, Amandeep Singh Grover, Akhtarul Islam Amjad, "New Car price prediction model using AI before launch: Forward selection Regression," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.49-55, 2023.

Detecting Brain tumors through human eye inspection has a probability of errors in analysis and a higher number of MRI reports cannot be inspected in a feasible amount of time. Thus, we need an easier automated approach towards this, that can be easily used and can give accurate results in Tumor detection. In this paper, we have implemented a Machine Learning Model based on Convolutional Neural Network, with the help of Global Average Pooling to fulfill this goal. Here we have two models, where one can do a binary classification of the images to detect if they have a trace of tumor in the MR Images or not, and another model that can detect the type of Tumor categorically among 3 types which are Glioma, Meningioma, and Pituitary. This model has acquired an accuracy score of 96.02% and 99.46% in the Binary and Categorical Models respectively.

CNN, Neural Network, Global Average Pooling, MRI, Batch Normalization, Max Pooling, Dropout, Dense layer.

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[2] M. Siar and M. Teshnehlab, "Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm," 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), pp.363-368, 2019. www.doi.org/10.1109/ICCKE48569.2019.8964846.
[3] L. Gaur, M. Bhandari, T. Razdan, S. Mallik, Z. Zhao. “Explanation-Driven Deep Learning Model for Prediction of Brain Tumor Status Using MRI Image Data.” FrontGenet,Mar 14, 2022.
www.doi.org/10.3389/fgene.2022.822666.
[4] E. Av?ar, Salçin K. “Detection and classification of brain tumors from MRI images using faster R-CNN ". Tehni?ki glasnik. 2019;13(4): pp.337–342. 2019. https://doi.org/10.31803/tg-20190712095507.
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[9] X. W. Gao and R. Hui, "A deep learning based approach to classification of CT brain images," 2016 SAI Computing Conference (SAI), London, UK, pp.28-31, 2016. www.doi.org/10.1109/SAI.2016.7555958.
[10] Y. Xu, Z. Jia, Y. Ai, F. Zhang, M. Lai and E. I. -C. Chang, "Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation," 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, Australia, pp. 947-951, 2015. www.doi.org/10.1109/ICASSP.2015.7178109.

Aparna Datta, Pritam Mukherjee, Gourab Paul, "CNN-based Binary and Categorical Model to Detect Tumor from MR Images," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.56-61, 2023.

Heart disease is one of the most serious and life threatening problems. If predicted beforehand, many lives can be saved. But, the problem is that medical datasets are highly imbalanced, which leads machine learning algorithms to perform poorly on the minority class. Which in terms leads to wrong predictions. In healthcare it is highly risky to predict something wrongly, because, people’s lives are on stake. The ratio of minority and majority class data should be 1:1, or near about equal, in order to get a good result. Synthetic Minority Oversampling TEchnique(SMOTE) is one such oversampling technique that makes it come true, which is used in this work. In addition we have used eXplainable AI(XAI) to better visualise the predictions. We have used LIME (Local Interpretable Model-agnostic Explanation) and SHAP (Shapely Additive Explanations) algorithms to understand the contributions of features towards the predictions.

Heart Disease, SMOTE, Machine Learning, Explainable AI, LIME, SHAP

[1] Deldar, K., Mahdavi, M., & Mohammadzadeh, N. (2020). Handling imbalanced healthcare data with supervised and unsupervised methods: A systematic literature review. Journal of biomedical informatics, 109, 103516.
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[4] Al-Bahrani, R., Huang, W., & El-Sheimy, N. (2019). imbalanced healthcare data using ensemble methods and data sampling techniques. Applied Sciences, Vol.9, Issue.13, 2721, 2019.
[5] https://www.cdc.gov/heartdisease/facts.htm [DATASET]
[6] Wang, H., Yang, X., & Zhang, Q. (2019). A deep learning framework for handling imbalanced medical data. IEEE Access, 7, 89154-89162.
[7] Yao, J., Wang, S., Li, W., & Zhang, Y. (2020). Handling imbalanced electronic health record data using convolutional neural networks with auxiliary training. Journal of biomedical informatics, 110, 103530.
[8] L.H. Yang, J. Liu, Y.M.Wang, L. Martínez, A micro-extended belief rule-based system for big data multiclass classification problems, IEEE Trans. Syst. Man Cybern. Syst. pp.1–21, 2018.
[9] P.V. Ngoc, C.V.T. Ngoc, T.V.T. Ngoc, D.N. Duy. A C4. 5 algorithm for english emotional classification, Evolving Syst. 10, pp.425–451, 2019.
[10] Datta, Shounak, and Swagatam Das.Near-Bayesian Support Vector Machines forImbalanced Data Classi?cation with Equal or Unequal Misclassi?cation Costs. NeuralNetworks 70: pp.39–52, 2015.
[11] ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.114.008729

Sandip Das, Gairik Sajjan, Arkajyoti Poddar, Tamojit Dasgupta, Sayani Patty, Debmitra Ghosh, "Handling Imbalanced Heart Disease Data and Explaining the Factors," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.62-65, 2023.

The COVID-19 pandemic had a profound impact on global financial markets, including the stock market in India and worldwide exchanges. This abstract provides an overview of the effects of COVID-19 on these markets and the factors influencing their performance. The outbreak of COVID-19 in early 2020 caused disruptions across various sectors, resulting in significant volatility in stock markets worldwide. In India, benchmark indices such as BSE Sensex and NSE Nifty experienced sharp declines as investors reacted to the uncertainties and potential economic consequences of the virus. The Indian government`s strict lockdown measures further contributed to market turmoil, as businesses faced challenges in maintaining operations and generating revenue. Government interventions and monetary stimulus measures aimed at mitigating the economic impact of the crisis provided some stability to the Indian stock market as the pandemic progressed. Sectors like healthcare, pharmaceuticals, and information technology showed relative resilience and even growth due to increased demand and digitalization trends. Conversely, sectors such as travel, hospitality, and retail faced significant setbacks, leading to declines in their stock prices. Similarly, global stock markets witnessed a similar pattern during the pandemic. The initial onset of the crisis led to widespread sell-offs, resulting in substantial declines in major indices such as the Dow Jones Industrial Average, S&P 500, and FTSE 100. However, governments and central banks worldwide implemented unprecedented fiscal and monetary measures, gradually helping stock markets recover. Stimulus packages, low interest rates, and liquidity injections boosted investor confidence and provided support for equities. The pace of economic recovery and the success of vaccination campaigns played crucial roles in shaping stock market performance, both in India and globally. Positive developments regarding vaccine approvals and declining infection rates often resulted in market rallies, while setbacks in virus containment or the emergence of new variants led to increased market volatility. It is worth noting that the impact of COVID-19 on stock markets varied across sectors and companies. Industries that adapted well to the pandemic and capitalized on digital infrastructure experienced growth, while others struggled to survive. Investors increasingly focused on companies with strong balance sheets, robust digital presence, and the ability to adapt to changing consumer behaviours. In conclusion, the COVID-19 pandemic significantly affected the Indian stock market and global stock exchanges, leading to periods of high volatility and market declines. Government interventions and stimulus measures played a crucial role in stabilizing markets, while the pace of economic recovery and vaccination campaigns influenced investor sentiment. The pandemic highlighted the importance of adaptability and resilience for businesses, emphasizing the need for careful risk assessment and diversification in investment strategies.

COVID-19, Indian stock market, Global stock market, Pandemic, Market Fluctuations etc.

[1] Varma, Y., Venkataramani, R., Kayal, P. and Maiti, M., 2021. Short-term impact of COVID-19 on Indian stock market. Journal of Risk and Financial Management, Vol.14, Issue.11, pp.558, 2021.
[2] Chaudhary, R., Bakhshi, P. and Gupta, H., 2020. The performance of the Indian stock market during COVID-19. Investment Management and Financial Innovations, Vol.17, Issue.3, pp.133-147, 2020.
[3] Sahoo, M., COVID?19 impact on stock market: Evidence from the Indian stock market. Journal of Public Affairs, 21(4), p.e2621, 2021.
[4] Alam, M.N., Alam, M.S. and Chavali, K., 2020. Stock market response during COVID-19 lockdown period in India: An event study. The Journal of Asian Finance, Economics and Business (JAFEB), Vol.7, Issue.7, pp.131-137, 2020.
[5] Verma, D. and Sinha, P.K., 2020. Has COVID 19 infected Indian stock market volatility? Evidence from NSE. AAYAM: AKGIM Journal of Management, Vol.10, Issue.1, pp.25-35, 2020.
[6] Kumar, R., Bhatia, P. and Gupta, D. The impact of the COVID-19 outbreaks on the Indian stock market–A sectoral analysis. Investment Management and Financial Innovations, Vol.18(3), pp.334-346, 2021.
[7] Bora, D. and Basistha, D. The outbreak of COVID?19 pandemic and its impact on stock market volatility: Evidence from a worst?affected economy. Journal of Public Affairs, Vol.21, Issue.4, p.e2623, 2021.
[8] Shankar, R. and Dubey, P. Indian stock market during the COVID-19 pandemic: vulnerable or resilient? sectoral analysis. Organizations and Markets in Emerging Economies, Vol.12, Issue.1, pp.131-159, 2021.
[9] Guru, B.K. and Das, A., 2021. COVID-19 and uncertainty spillovers in Indian stock market. Methods, 8, pp.101199, 2021.
[10] Sreenu, N. and Pradhan, A.K., 2022. The effect of COVID-19 on Indian stock market volatility: can economic package control the uncertainty? Journal of Facilities Management, (ahead-of-print), 2022.
[11] Mishra, A.K., Rath, B.N. and Dash, A.K., Does the Indian financial market nosedive because of the COVID-19 outbreak, in comparison to after demonetization and the GST?. Emerging Markets Finance and Trade, Vol.56, Issue.10, pp.2162-2180, 2020.
[12] Mittal, S. and Sharma, D. The impact of COVID-19 on stock returns of the Indian healthcare and pharmaceutical sector. Australasian Accounting, Business and Finance Journal, Vol.15(1), pp.5-21, 2021.
[13] Dhillon, M.A. and Tyagi, V., 2021. Impact of Covid-19 on Indian stock market. Journal of Contemporary Issues in Business and Government Vol.27, Issue.1, 2021.
[14] Ahmed, F., Syed, A.A., Kamal, M.A., de las Nieves López-García, M., Ramos-Requena, J.P. and Gupta, S., 2021. Assessing the impact of COVID-19 pandemic on the stock and commodity markets performance and sustainability: A comparative analysis of South Asian countries. Sustainability, Vol.13, Issue.10, p.5669, 2021.
[15] Saravati, D., Agrawal, S. and Saravati, M., 2021. Indian stock market analysis and prediction using LSTM model during COVID-19. International Journal of Engineering Systems Modelling and Simulation, 12(2-3), pp.139-147, 2021.
[16] Aruna, B. and Rajesh, A.H., 2020. Impact of COVID 19 Virus Cases and Sources of Oil Price Shock on Indian Stock Returns. Structural VAR Approach. In IAEE Energy Forum/COVID-19 Issue Vol.2020, pp 68-70, 2020.
[17] Kumar, M.P. and Kumara, N.M., 2021. Market capitalization: Pre and post COVID-19 analysis. Materials Today: Proceedings, 37, pp.2553-2557, 2021.
[18] Garg, K.D., Gupta, M. and Kumar, M., 2021. The impact of Covid-19 epidemic on Indian economy unleashed by machine learning. In IOP Conference Series: Materials Science and Engineering (Vol. 1022, No. 1, p. 012085). IOP Publishing.
[19] Rajamohan, S., Sathish, A. and Rahman, A., 2020. Impact of COVID-19 on stock price of NSE in automobile sector. Int. J. Adv. Multidisc. Res, 7(7), pp.24-29.
[20] Sidhu, G.S., Rai, J.S., Khaira, K.S. and Kaur, S., 2020. The Impact of COVID-19 pandemic on different sectors of the Indian Economy: A descriptive study. International Journal of Economics and Financial Issues, 10(5), pp.113-120, 2020.
[21] Pandey, D.K. and Kumar, R., 2022. Lockdown, unlock, stock returns, and firm-specific characteristics: the Indian tourism sector during the Covid-19 outbreak. Current Issues in Tourism, 25(7), pp.1026-1032.
[22] Arshi, D., Sahoo, B.P., Gulati, A. and Haq, I.U., 2021. Repercussions of COVID-19 on the Indian stock market: A sectoral analysis. Linguistics and Culture Review, 5(S1), pp.1495-1509.
[23] Bhama, V., 2022. Macroeconomic variables, COV?D-19 and the Indian stock market performance. Investment Management & Financial Innovations, 19(3), p.28.
[24] Thomas, T.C., Sankararaman, G. and Suresh, S., 2020. Impact of Covid-19 announcements on Nifty stocks. Journal of Critical Reviews, 7(13), pp.471-475.
[25] Okorie, D.I. and Lin, B., 2021. Adaptive market hypothesis: the story of the stock markets and COVID-19 pandemic. The North American Journal of Economics and Finance, 57, p.101397.
[26] Syed, A.A., Tripathi, R. and Deewan, J., 2021. Investigating the impact of the first and second waves of the COVID-19 pandemic on the Indian stock and commodity markets: An ARDL analysis of gold, oil, and stock market prices. Indian Journal of Finance, 15(12), pp.8-21.
[27] Verma, R.K., Kumar, A. and Bansal, R., 2021. Impact of COVID-19 on different sectors of the economy using event study method: an Indian perspective. Journal of Asia-Pacific Business, 22(2), pp.109-120.
[28] Singh, M.K. and Neag, Y., 2020. Contagion effect of COVID?19 outbreak: Another recipe for disaster on Indian economy. Journal of Public Affairs, 20(4), pp.e2171, 2020.
[29] Kumar Naik, P., Shaikh, I. and Duc Huynh, T.L., 2022. Institutional investment activities and stock market volatility amid COVID-19 in India. Economic research-Ekonomska istraživanja, 35(1), pp.1542-1560, 2022.
[30] Dharani, M., Hassan, M.K., Huda, M. and Abedin, M.Z., 2023. Covid-19 pandemic and stock returns in India. Journal of Economics and Finance, 47(1), pp.251-266, 2023.
[31] Maheen, M.S., 2021. Impact of COVID-19 on the performance of emerging market mutual funds: evidence from India. Future Business Journal, 7, pp.1-8, 2021.
[32] Das, N.M. and Rout, B.S., 2020. Impact of COVID-19 on market risk: appraisal with value-at-risk models. The Indian economic journal, 68(3), pp.396-416, 2020.
[33] Manu, K.S. and Shetty, A.S., 2022. Impact of COVID-19 on the Performance of Indian Stock Market: An Empirical Analysis. Jindal Journal of Business Research, Vol.11, Issue.2, pp.175-186, 2022.
[34] Hanif, M., Hassan, M., Henchiri, B. and AlDaas, M., 2022. Impact of the COVID-19 pandemic on banking and financial sector stock returns. International Journal of Accounting, Business and Finance, Vol.1, Issue.2, pp.19-35, 2022.
[35] Agarwala, S. and Singhb, A., 2020. Covid-19 and its impact on Indian economy. INTERNATIONAL JOURNALOF TRADE & COMMERCE-IIARTC, p.2, 2020.
[36] Ghosh, I., Alfaro-Cortés, E., Gámez, M. and García-Rubio, N., 2023. Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI. Expert Systems with Applications, 219, pp.119695, 2023.
[37] Jasuja, D. and Sharma, P., 2020, June. Anticipation of Consequences & Sectoral Impact of COVID-19–An Indian Outlook. In XV National Conference on Sustainable Management Practices & Economic Slowdown in India.
[38]https://blogs.lse.ac.uk/southasia/2022/01/24/indian-stock-markets-discord-with-the-real-economy/

Nirbhay Mishra, Dharmpal Singh, Radhakrishna Jana, Sudipta Kumar Dutta, Arnab Majee, Debmitra Ghosh, "COVID-19 and the Stock Market: A Comparative Study of India and the World," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.66-75, 2023.

Heart disease has been a serious threat to mankind. According to research 7 out of 10 people die due to heart failure. In this paper, we have proposed a framework using which we can determine if a person has heart ailments or not. We have used various ML classification algorithms such as Logistic Regression, SVM, Random Forest, Decision tree, KNN, MLP, and Neural Network to determine the existence of heart disease. The best result has been obtained by Random Forest. Timely detection of a disease can save many people’s lives, thereby controlling the mortality rate to some extent.

Cardiovascular disease, KNN, SVM, Neural Network, Random Forest, Decision Tree, MLP, Logistic regression

[1] S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, Vol.7, pp.81542–81554, 2019, doi: 10.1109/ACCESS.2019.2923707.
[2] Dinesh Kumar G, Arumugaraj K, Santhosh Kumar D and Mareeswari V, “Prediction of Cardiovascular Disease Using Machine Learning Algorithms”, Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore, India.
[3] D. Shah, S. Patel, and S. K. Bharti, “Heart Disease Prediction using Machine Learning Techniques,” SN Comput Sci, Vol.1, no.6, pp.345, Nov. 2020, doi: 10.1007/s42979-020-00365-y.
[4] R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, and P. Singh, “Prediction of Heart DiseaseUsing a Combination of Machine Learning and Deep Learning,” Comput Intell Neurosci, Vol.2021, 2021, doi: 10.1155/2021/8387680.
[5] A. U. Haq, J. P. Li, M. H. Memon, S. Nazir, R. Sun, and I. Garciá-Magarinõ, “A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms,” Mobile Information Systems, Vol.2018, 2018, doi: 10.1155/2018/3860146.
[6] M. M. Ali, B. K. Paul, K. Ahmed, F. M. Bui, J. M. W. Quinn, and M. A. Moni, “Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison,” Comput BiolMed, vol. 136, Sep. 2021, doi: 10.1016/j.compbiomed.2021.104672.
[7] J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, “Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare,” IEEE Access, Vol.8, pp.107562–107582, 2020, doi: 10.1109/ACCESS.2020.3001149.
[8] A. K. Dwivedi, “Performance evaluation of different machine learning techniques for prediction of heart disease,” Neural Comput Appl, Vol.29, no.10, pp.685–693, May 2018, doi: 10.1007/s00521-016-2604-1.
[9] https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset
[10]Archana Singh, Rakesh Kumar, “Heart Disease Prediction Using Machine Learning Algorithms”, 2020 International Conference on Electrical and Electronics Engineering (ICE3-2020)
[11] https://www.cdc.gov/heartdisease/facts.htm

Aparna Datta, Sreeja Ghosh, "Methodical Prediction of Cardiovascular Disease Using Consolidated Machine Learning Classification Algorithms and Analysis," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.76-80, 2023.

Accurate identification of fish species plays a crucial role in fisheries management and conservation. However, traditional methods struggle to address the diverse marine species found in India, resulting in inaccuracies and time-consuming processes. Manual identification by experts becomes particularly challenging, especially for large-scale conservation and monitoring efforts. To tackle this issue, we propose an Artificial Intelligence (AI) based framework for precise and efficient fish species identification in India. Our framework utilizes convolutional neural networks (CNNs) to extract features from fish images and employs the Random Forest Classifier for species identification. Trained on a comprehensive dataset encompassing various regions in India, our model achieved an impressive accuracy of 98.20 percent in rigorous testing, highlighting its effectiveness. Specifically, our proposed Random Forest Classifier exhibited remarkable accuracy in classifying fish species from grayscale images. By employing this AI framework, fish species identification in India can be significantly improved, leading to tangible benefits in fisheries management, conservation efforts, marine biology research, and aquaculture. Furthermore, the versatility of our approach allows its application to other countries with similar fish species diversity, offering potential solutions for real-world scenarios, such as underwater cameras.

Artificial Intelligence, Fish Species, Convolutional Neural Networks, Random Forest Algorithm, aquaculture research, identification, conservation, classification.

[1] M. Bhanumathi and B. Arthi, “Future Trends and Short-Review on Fish Species Classification Models Based on Deep Learning Approaches,” in 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), 2022.
[2] J. Dewan, A. Gele, O. Fulari, B. Kabade and A. Joshi, “Fish Detection and Classification,” in 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA, 2022.
[3] V. Ananthan, “Fish Species Detection and Tracking Based on Fusion Intensity Entity Transformation using Optical Flow Algorithm,” in 2022 International Conference on Edge Computing and Applications (ICECAA), 2022.
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Souvik Roy, Sayak Mondal, Shreyashree Sarkar, Sumit K Banerjee, Suman Bhattacharya, Mahamuda Sultana, "AI based Framework for Fish Species Identification and Classification," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.81-88, 2023.