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Utilizing Machine Learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media Platforms 1

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3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Manoharan, Geetha Jyothi, B. Swarna Bhat, Romica Acharjee, Purnendu Bikash Sharma, Ankush Rawal, Priyanka School of Business SR University Telangana Warangal India Rajeev Gandhi Memorial College of Engineering and Technology Nandyala India Amity School of Communication Amity University Kolkata Kolkata India Computer Science CHRIST University Pune Lavasa Campus Lavasa India Jagran Lakecity Business School Faculty of Management and Commerce Jagran Lakecity University Madhya Pradesh Bhopal India Faculty of Management and Commerce Jagran Lakecity University Madhya Pradesh Bhopal India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

Social media is increasingly regarded as one of the most abundant online resources for information gathering and knowledge exchange. Among the most widely used social media sites is Twitter available today. When attempting to comprehend the information in any unknown word-based data (such as social media), natural language processing (NLP) techniques are crucial since they help remove noise from data, identify stem words, etc. It also helps with comprehension of the sentiment or semantic contents. Using social media, we apply machine learning techniques (clustering and classification) to determine the viewpoint's polarity in the information. Several classifiers and clusters, including SVM, RF, Naive Byes, and KNN, are used to detect content on social media. Sentiment analysis is the process of automatically classifying user-generated content as neutral, negative, or positive. It is possible to utilize the text, sentence, feature, or aspect as criteria to group feelings into distinct categories. This study demonstrates the application of machine learning techniques to the analysis of emotions expressed on the Twitter network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Integrating Renewable Energy Sources into Smart Grids with an Aggregator-Based Energy Management System for Efficiency and Resilience

Integrating Renewable Energy Sources into Smart Grids with a...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： B Santhosh Kumar Anusuya Devi Smita Sharma Munugapati Bhavana B Sankara Babu Navdeep Singh Department of CSE AI and ML Institute of Aeronautical Engineering Hyderabad Telangana India Department of Applied Science New Horizon College of Engineering Bangalore Karnataka India Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering GRIETBachupally Hyderabad India Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Integrating renewable energy sources (RESs) into smart grids (SGs) is challenging due to the intermittent and unpredictable nature of the sources, which are often the sun or wind. Additionally, it is important to think about how to get customers involved in the electricity sector so that we can lower energy prices globally. According to this article, an energy management system (EMS) may help the SG run more efficiently. The EMS aggregates distributed energy resources to make SG market participation easier. Integrating demand-side management (DSM) with active management strategies (AMS) has financial and environmental benefits as it increases the use of renewable energy sources while decreasing consumer energy costs. By incorporating their preferences and costs into market processes, energy supply/demand and distribution system operators (DSOs) may respond to real-time price signals. This has the potential to make the grid more adaptable and resilient. To verify the effectiveness of the proposed EMS, a 23-bus, $11-\mathrm{kV}$ transmission network is used.

关键词： Renewable energy sources Wind Costs Supply and demand Real-time systems Hardware Software Smart grids Energy management systems Sun

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Machine Learning Techniques for True and Fake Job Posting 1

Machine Learning Techniques for True and Fake Job Posting

引用

1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Mehta, Kamakshi Rajagopal, Navaneetha Krishnan Gaikwad, Sagar Balu Yadav, Sachin Amity College of Commerce Amity University Haryana India Department of Business Studies University of Technology and Applied Science Salalah Oman Department of Commerce & Management Vidyalankar School of Information Technology Maharashtra Mumbai India Department of Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9781607685395

According to researches, there are around 188 million unemployed people around the globe. We may find many job vacancies on job portals and across the internet to help the job seekers. India alone has more than a hundred job portals. One major issue people face here is that the job seekers are not sure if the employer is real or fake. Most of these portals do not have a system that could check if the employer, posting a job is real or fake. Scammers are making use of this opportunity to post fake job offers which might look genuine to the job seekers applying for it. This way the poor job seekers might lose a large amount of money and time. A best possible solution for this problem would be that the job portal itself being able to identify if the job being posted is real or fake. Paper suggests using a machine learning model to achieve this goal. An idea here is to use natural language processing to understand and analyze the job posting and then making use of a machine learning model to predict if the job posting is real or fake. The first step is to import a dataset which has real life real and fake job posting. In this project, Employment Scam Aegean Dataset provided by University of Aegean Laboratory of Information and Communication system Security is being used. Linear SVC is being used in this project for predicting real and fake job posting on a job portal. © The Electrochemical Society

关键词： Linear SVC Machine Learning Natural Language Processing Random Forest

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An Analysis of Machine Learning Methods used in Cyber Security during the Last Decade

An Analysis of Machine Learning Methods used in Cyber Securi...

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Intelligent & Innovative Practices in engineering & Management (IIPEM), International Conference on

作者： Rakesh Thoppaen Suresh Babu Tarun Dhar Diwan Ankit Vishnoi P. William Parul Madan Keshav Kaushik HEXAWARE TECHNOLOGIES INC Iselin New Jersey Atal Bihari Vajpayee University Bilaspur India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Deaprtment of Information Technology Sanjivani College of Engineering Kopargaon Amity University Dubai UAE Amity School of Engineering and Technology Amity University Punjab Mohali India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

This paper examines cybersecurity machine learning (ML) methods over the last decade. In this study, we examine how cyberthreats have evolved and how machine learning has progressed to combat them. Each problem is well discussed. Both topics are being discussed extensively. Cybersecurity has long employed several machine learning methods. This category includes reinforcement, supervised, and unsupervised learning methods. Other teaching methods include unsupervised learning. These methods excel in identifying possible threats. This article provides an overview of machine learning (ML)-based cybersecurity solutions to highlight major topics, issues, and research opportunities. The study uses academic, commercial, and real-world data to do this. It highlights important issues, difficulties, and opportunities.

关键词： Support vector machines Machine learning algorithms Neural networks Education Intrusion detection Machine learning Threat assessment Malware computer security Unsupervised learning

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Statistical Analysis of Social Media and E-commerce data to get insights on consumer behaviour

Statistical Analysis of Social Media and E-commerce data to ...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Meenakshi Tyagi Tanushree Sanwal Sandhya Avasthi Puja Sareen Sapna Yadav Shrishti Singh KIET School of Management KIET Group of Institutions KIET School of Management KIET Group of Institutions Ghaziabad India Computer Science and Engineering ABES Engineering College Ghaziabad Amity Business School Amity University Noida U.P Jaypee Business School Jaypee Institute of Information Technology Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The universal influence of social media has become deeply ingrained in our daily lives, it has changed the mindset and behavior of the people. Customers' purchasing habits are also impacted by the rise in social media use since these platforms offer a wide range of products that go above and beyond what customers expect. As customers, we have begun to use it to research and purchase the goods and services that meet our needs. E-commerce platforms have allowed sellers to reach a wider audience and effectively market and sell their goods and services. These platforms also make it easier to gather important information and consumer input, which can be used to create marketing plans and regulations. This study aims to examine the impact of social media advertising on consumers' buying decisions across a range of demographic categories, such as age, gender, income, and education. Data has been analyzed and presented using a variety of statistical techniques, such as tabulation, pie charts, bar charts, and the Chi-square test. This study looks at the significant relationship between age and how social media marketing affects consumer buying decisions. The results of this study will help marketers prioritize these factors when creating marketing plans.

关键词： Surveys Consumer behavior Social networking (online) Statistical analysis Media Social intelligence Electronic commerce Web sites Multimedia communication Advertising

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Analyse and Evaluate Quixbugs with Open AI Codex and Powering Next Generation Application

Analyse and Evaluate Quixbugs with Open AI Codex and Powerin...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Deepanshu Tyagi Sarvesh Tanwar Neetu Mittal Sumit Badotra Amity Institute of Information Technology Amity University Noida Uttar Pradesh India School of Computer Science and Engineering Bennett University Noida Uttar Pradesh India

The paper investigates the ordinary language age limits of massive language models, with applications to the development of two types of learning tools commonly found in programming courses. We construct programming workouts and code explanations, reviewing these abstractly and statistically, using the Open AI Codex as the massive language model. Our findings indicate that the majority of the content so given is both unique and sensible, and that it is occasionally ready to use without any guarantees. While rehearsing, we discovered that it is astonishingly simple to affect both the programming ideas and the practical subjects they include simply by offering expressions of commitment to the model. Findings indicate massively generative computer intelligence models have fundamental value.

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Estimating Challenges Of Atificial Inteligence (ML): Information Extraction Perspective

Estimating Challenges Of Atificial Inteligence (ML): Informa...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Ahmad Tasnim Siddiqui Nidhi Goyal Mohammad Nadeem Ahmed (School of Computer Science & Engineering) Sandip University Nashik Maharashtra Amity Institute of Information Technology Amity University Lucknow Campus Department of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The advancement of “quantifiable artificial intelligence (ML)” ways of thinking served to re-advocate the field following the past “Man-made awareness (computerized reasoning) winter.” The new and impending progressions in computer based intelligence and AI are the essential focal point of this short piece on mechanical turn of events. A growing interest in artificial intelligence (AI) is being expressed by academics as well as the general public. This interest includes gains that can be measured from massive amounts of data. Considering present day speculations, the basic and rotational seasons of mirrored information are making at a bewildering rate. The production of a bound together European man-made understanding exploration place has been moved by two or three social events, and the European Connection has essentially expanded supporting for PC based information research. schools are coordinating recreated insight and artificial intelligence into their instructive projects and long stretch goals. Even though the current methods appear to be working well, there are still significant issues that need to be resolved: Because of their indistinctness, the models that produce the best outcomes are alluded to as “secret components.” It is endeavoring to utilize such “black box” headway thinking about open and by and large in security, confirmation, control, and security. This study analyzed discretionary data assortment techniques for managing top notch research considering the examination objective. These headways offer stimulating, cutting edge deals with any consequences regarding testing genuine issues for planners and producers. Obviously, man-made scholarly ability (repeated understanding) has a huge number of purposes. The accessibility of various speculations, calculations, and techniques makes it conceivable. Choosing the ideal artificial intelligence technique for the fitting assembling or designing strategy and settings is testing. Furthermore, significant

关键词： Standards organizations Europe Production Organizations Information retrieval Probabilistic logic Security

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Privacy-Aware Artificial Intelligence with Homomorphic Encryption using Machine Learning

Privacy-Aware Artificial Intelligence with Homomorphic Encry...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： B. Srinivasa Rao Saumitra Chattopadhyay Prashant Singh Bramah Hazela G. Sabarinathan Kalva Yamini Department of Computer Science and Engineering Bachupally Hyderabad Telangana India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Mathematics PSNA College of Engineering and Technology Dindigul Tamilnadu India Cyber Security in Computer Science and Engineering Sri Ramachandra Institute of Higher Education and Research Chennai Tamilnadu India

Along with the expansion of machine learning (ML) applications, the amount of data required to create predictions increases. Big-data ML has always been limited by off-chip memory capacity and computational speed. Considerably, privacy is one of the limitations of big data, which can be solved by homomorphic encryption (HE). Due to the combination of HE and ML, the multi-party privacy-protected ML suggested in this research may assist numerous users in doing artificial intelligence (AI) without disclosing private data. The technique may train common models in situations of data abuse, particularly in private data protection. The model trained using the ML technique named Artificial Neural Network (ANN) has a similar impact to the model developed using all data on a single computer, according to experiments using the algorithm. The gradient data is simply transmitted by all parties, and homomorphic procedures in the main computing system combine the gradient data. Besides, the optimal key is selected using the significance of the Lion Algorithm (LA). After homomorphic procedures, the learning model is modified depending on the new gradient data.

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Legal Considerations for Artificial Intelligence and Machine Learning

Legal Considerations for Artificial Intelligence and Machine...

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International Conference on Computing and Networking technology (ICCNT)

作者： Gunjan Bhatnagar Alpika Verma Nilanjan Chakraborty Suman Ashwini Kumar Aman Mittal School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand Department of LAW Invertis University Bareilly Uttar Pradesh Amity Law School Amity University Kolkata India Department of Computer Science & Engineering School of Engineering & Technology K. R. Mangalam University Gurugram Haryana INDIA Department of Engineering and Information Technology ARKA JAIN University Jamshedpur Jharkahnd India Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Artificial intelligence (AI) and gadgets gaining knowledge of (ML) have revolutionized organizations’ overall performance, leading to accelerated performance and productivity. This era has become a fundamental part of our daily lives, from personal assistants to self-using vehicles. However, as AI and ML continue to improve and become more pervasive, it’s crucial to undergo the prison implications that would stand up. One of the primary criminal considerations for AI and ML is record privacy and safety. With the sizeable series and processing of extensive records, the danger of fact breaches and privacy violations increases. Corporations should follow applicable legal suggestions and policies, the overall records protection law (GDPR), and the California Purchaser Privacy Act (CCPA). Another interest is a legal responsibility. As AI and ML become more impartial, questions arise about who is responsible for any damage or harm because of those structures. This trouble has become more complicated, even as a few events concern improving and deploying AI and ML technologies.

关键词： Productivity Industries Privacy Data privacy Law Medical services Machine learning Artificial intelligence Protection Guidelines

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Exam Cheating Classification Using a Deep Learning Approach

Exam Cheating Classification Using a Deep Learning Approach

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Bishaw, Molla Ejigu Salau, Ayodeji Olalekan Vats, Satvik Kaushik, Keshav Alemu, Wubie Belay Tin, Ting Tin Debre Tabor University Department of Information Technology Debre Tabor Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248007 India Amity School of Engineering and Technology Amity University Mohali Punjab India Debark University Department of Information Technology Debark Ethiopia Inti International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia

ISBN: (纸本)9798350379716

Exam cheating has become a significant concern in educational institutions due to its potential negative impact on academic integrity. Researchers have attempted to overcome the issue. However, the previous research did not use multiple cheating activities in their datasets and the attributes of the studies used were not strong enough to represent exam cheating, particularly about paper positions. The study utilizes the dataset containing cheating and non-cheating images to develop a model using deep learning approaches. The dataset consists of images collected from network colleges, confirming the presence of cheating and non-cheating instances. Preprocessing techniques, including image resizing, noise removal, histogram equalization, augmentation and feature extraction are applied to the dataset, and both original and augmented versions of the datasets are used in experimental studies. Different input image sizes are employed for the deep CNN, AlexNet, GoogLeNet, and ResNet architectures and evaluated for their performance in classification in a paper based exam cheating. As the experimental results demonstrate from the models used in this study the customized CNN model achieves the highest accuracy of 93% and exhibits excellent performance in terms of loss function and accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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