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IEEE Punecon

作者： Ashish Deshmukh Aniket Dhage Ritika Gadapa Sanika Butle Anuradha Yenkikar Nilesh P. Sable Department of CSE – Artificial Intelligence Vishwakarma Institute of Information Technology Pune India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

In this generation, social media platforms such as Twitter, Instagram and also e-commerce websites have become an integral part of our society. Social media is used for communication and it is also a platform where the users can express their thoughts and feelings. Also, users share their reviews on e-commerce websites. To analyze what exactly is the emotion of the user our machine learning models explores the sentiment analysis techniques for predicting emotions from the given text data. It follows a series of steps, starting with importing libraries, acquiring datasets, visualization, pre-processing, and model building. The data is used to train on a variety of models, including LSTM, Naive Bayes, Random Forest, Gradient Boosting, and SVM. The performance of each model is evaluated using metrics like accuracy, precision, recall, and F1-score. Comparative analysis is conducted to determine the most effective model for emotion prediction. Furthermore, the research investigates emotion prediction for huge datasets and provides a procedure for exporting trained models for future usage. It provides insights into the performance of various machine learning algorithms in sentiment analysis, hence contributing to the understanding and interpreting emotions from textual data on various platforms.

关键词： Support vector machines Sentiment analysis Analytical models Accuracy machine learning algorithms Social networking (online) Predictive models Data models Long short term memory Random forests

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Enhancing Financial Market Analysis Bitcoin vs Gold through machine learning algorithms: A Study on Risk Assessment and Portfolio Management

Enhancing Financial Market Analysis Bitcoin vs Gold through ...

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India Council International Subsections Conference (INDISCON), IEEE

作者： Richa Golash Kushagra Golash SCSET Bennett University Greater Noida India SCSET BITS Pilani Dubai

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

Financial markets exhibit complex, nonlinear dynamic behavior that is often challenging to interpret using traditional linear analysis methods. Risk assessment and, accordingly, portfolio management are the most important tasks for investors in order to avoid big losses. Gold trading is considered a safe haven, but it shows slow growth. In contrast, bitcoin or cryptocurrency trading is being proliferated. Many established financial institutions, like PayPal and Fidelity, are making it easier than ever for individuals to buy and sell Bitcoin. While Bitcoin trading can be potentially lucrative, it’s crucial to understand the significant risks involved before diving The volatile nature of Bitcoin can be emotionally stressful, leading to anxiety and poor decision-making. High-risk activity like stock trading requires a strong understanding of the market, particularly in high-risk assets like Bitcoin and gold, which demands a deep comprehension of market dynamics and the ability to tolerate volatility. Through this study, we delve into the application of machine learning algorithms to comprehend correlations, causality, and predict volatility in stock trading. Specifically, we focus on analyzing the trading behavior of cryptocurrency (bitcoin) vs. physical assets (gold) in terms of their volatility and comparing them in correlation with the stock index of crude-oil, S&P 500, and the dollar. This structured study aims to shed light on the effectiveness of machine learning algorithms in understanding and predicting volatility in stock trading, with a specific focus on Bitcoin and gold as high-risk assets.

关键词： Gold machine learning algorithms Correlation Uncertainty Stability criteria Bitcoin Risk management Reliability Stock markets Portfolios

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Prediction and comparison of psychological health during COVID-19 among Indian population and Rajyoga meditators using machine learning algorithms

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Procedia computer science 2023年 218卷 697-705页

作者： Prashant Kumar Sushil Chandra CSIR-Central Scientific Instruments Organisation Chandigarh160030 India. Institute of Nuclear Medicine & Allied Sciences-DRDO New Delhi 110054 India.

Issues of providing mental health support to people with emerging or current mental health disorders are becoming a significant concern throughout the world. One of the biggest effects of digital psychiatry during COVID-19 is its capacity for early identification and forecasting of a person's mental health decline resulting in chronic mental health issues. Therefore, through this study aims at addressing the hological problems by identifying people who are more likely to acquire mental health issues induced by COVID-19 epidemic. To achieve this goal, this study includes 1) Rajyoga practitioners' perceptions of psychological effects, levels of anxiety, stress, and depression are compared to those of the non practitioners 2) Predictions of mental health disorders such as stress, anxiety and depression using machine learning algorithms using the online survey data collected from Rajyoga meditators and general the population. Decision tree, random forest, naive bayeBayespport vector machine and K nearest neighbor algorithms were used for the prediction as they have been shown to be more accurate for predicting psychological disorders. The support vector machine showed the highest accuracy among all other algorithms. The f1 score was also the highest for support vector machine.

关键词： COVID-19 DASS machine learning algorithms mental health

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Harnessing machine learning algorithms to Revolutionize Crime Detection and Prevention

Harnessing Machine Learning Algorithms to Revolutionize Crim...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： E. Monika T.Rajesh Kumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institite of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

machine learning's (ML) quick development has opened up revolutionary possibilities for crime prevention and detection by making accurate, effective, and scalable solutions possible. In order to improve the precision of criminal behavior identification and prediction, this study investigates the integration of cutting-edge machine learning methods, such as gradient boosting machines, random forests, and convolutional neural networks (CNNs). Utilizing both structured and unstructured data sources, including social media analytics, surveillance footage, and past crime records, our approach makes use of enormous datasets. Accuracy, precision, recall, and F1 score are important evaluation criteria. According to experimental results, CNN-based models were able to identify criminal actions from video surveillance with 94.8% accuracy, 92.5% precision, 90.3% recall, and 91.4% F1 score. High-risk areas and persons were profiled using Random Forest algorithms, which produced an F1 score of 87.8%, accuracy of 89.7%, precision of 88.2%, and recall of 87.5%. When used to detect anomalies in financial transactions, gradient boosting machines demonstrated 93.2% accuracy, 91.8% precision, 90.6% recall, and 91.2% F1 score. The system's performance demonstrates how well it can handle a variety of crime scenarios, such as real-time monitoring, fraud detection, and predictive policing. The study also emphasizes how crucial it is to strike a balance between recall and precision in order to reduce false positives and negatives, which is crucial in practical applications. This study provides a scalable and flexible framework to transform crime detection and prevention by utilizing cutting-edge machine learning algorithms, giving law enforcement useful information.

关键词： Accuracy machine learning algorithms Prevention and mitigation System performance Soft sensors Transforms Boosting Video surveillance Convolutional neural networks Random forests

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Predicting Water Safety: Harnessing the Power of Simple machine learning algorithms

Predicting Water Safety: Harnessing the Power of Simple Mach...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Dipali Dumbre Seeta Devi Ranjana Chavan Symbiosis College of Nursing (SCON) Symbiosis International (Deemed University) (SIU) Pune India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

One of the most valuable resources that is necessary for all life is water. Water pollution lowers the quality of the water, which affects the health of marine life and, consequently, that of humans who utilize it. Because of this, it’s imperative to monitor water quality and guarantee the survival of marine life. This research will use artificial intelligence and machine learning techniques to evaluate the efficacy of several models in forecasting water safety. Orange tools are used to apply several prediction models, including Decision Tree, Gradient Boosting, Naïve Bayes, Neural Network, SGD, kNN, and CN2 rule inducer. The review employs publicly available secondary data from Keggle, ensuring transparency and accessibility in the research methodology. The results demonstrate that the GB, Neural Network, and Tree achieved superior accuracy ratings of 0.992, 0.998, 0.997, and 0.997, respectively, with remarkable AUC values of 1.000, 1.000, and 0.969. In addition, their recall and accuracy scores are 0.998, 0.998, 0.998, and 0.997, confirming their outstanding performance in predicting water safety.

关键词： machine learning algorithms Accuracy Reviews Neural networks Water quality Predictive models Prediction algorithms

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Real Time Fake news Detection Web App Enhanced by machine learning algorithms

Real Time Fake news Detection Web App Enhanced by Machine Le...

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Intelligent Technologies (CONIT), International Conference on

作者： V Gunasekaran D Karthikeyan School of Information and Technology Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In this era, the pervasive proliferation of fabricated news has come to light as a formidable adversary, corroding the credibility of journalistic sources, sowing confusion, and distorting public discourse. To counteract this escalating predicament, this research introduces a pioneering and comprehensive solution-an interactive web application engineered to detect and mitigate the impact of fabricated news. Through the strategic integration of sophisticated machine learning algorithms, this platform not only identifies spurious news content but also operates in real time, enabling swift responses to the proclamation of misinformation. The key aim of this study is to elevate the precision and efficiency of fabricated news identification. This is achieved through a meticulously devised amalgamation of advanced natural language processing (NLP) techniques, meticulous feature engineering strategies, and the robust framework of ensemble learning methods. The fusion of these techniques equips the system with an arsenal of tools to scrutinize news story, disentangling the intricate web of linguistic nuances that often obscure deceptive content. The real-time facet of the application is pivotal, ensuring that the detection process transpires in sync with the constant influx of news story across digital platforms. This immediacy minimizes the potential amplification of false narratives, thereby curbing the negative impact of fabricated news proclamation. The heart of the system lies in its fabricated news detection capability, fortified by the intricacies of modern machine learning. By training on vast repositories of verified news data and contrasting it with known instances of false information, the algorithms learn to recognize patterns that distinguish authentic news from falsified narratives. Accessed through standard web browsers, the web application interface offers an intuitive and accessible medium for users to engage with the detection process.

关键词： Training Heart machine learning algorithms Linguistics Real-time systems Natural language processing Pattern recognition

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Fault Detection On Heat Pump Operational Data Using machine learning algorithms

Fault Detection On Heat Pump Operational Data Using Machine ...

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International Conference on Internet of Things: Systems, Management and Security

作者： Md Mahbubur Rahman Reza Malekian Vilhelm Akerstroem Department of Computer Science and Media Technology Malmö University Malmö Sweden Internet of Things and People Research Centre Malmö University Malmö Sweden Engineering Software Robert Bosch GmbH Lund Sweden

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

Heat pumps, being complex systems, are susceptible to various malfunctions. By harnessing contemporary IoT technologies, these devices continuously transmit data which enables monitoring, maintenance, and efficiency. This study focuses on identifying compressor short duration cycles as faults through supervised machine learning algorithms such as XGBoost, Random Forest, SVM, and k-NN. Data preprocessing and labeling were conducted using extensive logged data from heat pump systems, addressing issues like high dimensionality, data sparsity, and temporal dependencies. The methodology included feature engineering, interpolation of missing data, and downsampling for compressor short duration cycles. Supervised machine learning models were applied to classify these short duration cycles. Among the models, XGBoost achieved the highest accuracy and F1-scores, effectively distinguishing between normal and fault conditions. The findings highlight the potential of machine learning to enhance predictive maintenance and operational efficiency in heat pumps.

关键词： Training Support vector machines Interpolation machine learning algorithms Heat pumps Fault detection System performance Time series analysis Labeling Security

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Design and Implementation of Under Water Level Maintaining and Automatic Controlling System for Industries Using machine learning algorithms

Design and Implementation of Under Water Level Maintaining a...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： R Parthasarathy M Kiruthiga Devi K. Manikandan A.P. Babu R Selvi A. Peter Soosai Anandaraj Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Dr. MGR Educational and Research Institute Chennai India Department of Biomedical Engineering Sona College of Technology Salem Dr.MGR Educational and Research Institute of Technology Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Industrial water consumption has been the target of conscious and reduction policies in order to avoid the waste of natural resources and financial resources. This is the biggest problem for governments. The notified draft rules said that even district magistrates have the power to grant NOC if the water withdrawal was 20–50 cubic meters per day depending on the type of area. This project describes the development of an android application for monitoring and automatic control using a flow sensor to measure water consumption for industries. The proposed system collects data from a flowmeter equipped with a borehole and sends this information via IOT (internet of things) to the server (municipality) and consumers, access records in the cloud at any time through platforms such as Thing Speak, issuing automatic flow readings water. This method enables live monitoring of water consumption and also provides identification of consumption patterns. Idea of using an ML model to gather and transmit real-time data to a licenced central command center regarding the water levels of various water bodies that supply the dams that evaluates the information gathered and chooses the appropriate course of action. The decision of whether to release the water can subsequently be made in accordance with the ML Model's recommendation. Where it reproduced the failure of the real environment. Wasting water is the biggest problem in households in developing countries like India,

关键词： Industries Natural resources Regulators machine learning algorithms Real-time systems Internet of Things Servers Relays Water resources Monitoring

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Long-term Energy Demand Analysis using machine learning algorithms: A Case Study in Bangladesh

Long-term Energy Demand Analysis using Machine Learning Algo...

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International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Halima Haque Mainul Islam Md. Asif Imties Alvy Md. Shamsul Islam Md. Abdur Razzak Department of Electrical and Electronic Engineering Independent University Bangladesh Dhaka Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur Bangladesh Dhaka Electric Supply Company Limited Dhaka Bangladesh

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

In each energy sector from production to consumption machine learning and statistical models have intimidated the power system for the past few decades. Among these, energy demand analysis is one of the approved research projects in Bangladesh due to its necessity. Such a study introduces energy consumption forecasting along with necessary environmental featured variables. These energy demands vary depending on short-term, medium-term, and long-term analysis. Although, this paper utilizes long-term energy demand research while predicting energy consumption. The dataset in the study includes three years of monthly data consisting of more than one million samples and diverse consumer types with versatile features of the capital of Bangladesh, Dhaka (mainly Uttara). Different machine learning models like; Random Forest, k-neighrest Neighbors regression, Extreme Gradient Boosting Method, and Light Gradient Boosting Method have been used and in order to evaluate them various performance constraints have been utilized. Among the models, KNN has performed considerably well. It has the result of about 0.9447 as R 2 , 163.9 as RMSE, and 28.7 as MAE. Such study will surely subsidize upgrading to the power system management of Bangladesh.

关键词： Radio frequency Energy consumption Accuracy machine learning algorithms Power system management Predictive models Nearest neighbor methods Boosting Forecasting Random forests

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Detection of SQL Injection Attacks Using machine learning algorithms Based on NLP-Based Feature Extraction

Detection of SQL Injection Attacks Using Machine Learning Al...

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International Conference on Computer Science and Engineering (UBMK)

作者： Hakan Can Altunay Carsamba Ticaret Borsasi Meslek Yüksekokulu Ondokuz Mayis Üniversitesi Samsun Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

SQL injection attack is one of the cyber attack types that puts individuals and institutions in a difficult situation in terms of data disclosure and material damage. This attack type, which is frequently preferred due to its case of use, has emerged with different usage features in recent years. In this study, various machine learning algorithms were tested to detect SQL Injection attacks. In the data pre-processing section, feature extraction was performed using Natural Language Processing techniques. While the relevance of expressions to each other was calculated with the Word Level TF-IDF method, term search was also performed. In the study, the classification process was performed using convolutional neural network, support vector machine, naive bayes and logistic regression from machine learning methods. At the end of the experimental study, it was found that convolutional neural network was the model that detected SQL Injection attacks with the highest success.

关键词： Support vector machines Logistic regression machine learning algorithms machine learning SQL injection Feature extraction Vectors Natural language processing Convolutional neural networks Cyberattack

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