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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Arul, S. Kavitha, P. Kamalakkannan, S. School of Computing Sciences Department of Computer Science Chennai Pallavaram India School of Computing Sciences Department of Computer Applications Chennai Pallavaram India School of Computing Sciences Department of Information Technology Chennai Pallavaram India

ISBN: (纸本)9798331504403

Smart cities are being created all over the world to enhance the safety and quality of life for their residents via the use of technology. Video surveillance, which includes placing cameras at key locations across the city to keep an eye on public areas and give law enforcement and other local authorities real-time access to recordings, is a crucial part of the architecture of smart cities. Cuttingedge technologies like deep learning, blockchain, edge computing, and cloud computing are used in modern video surveillance systems. This paper offers a comprehensive review of video surveillance systems for smart cities, examining both their benefits and drawbacks. In order to improve inhabitants' safety, security, and overall well-being, this article aims to show the benefits of video surveillance systems in smart cities and offers suggestions for their application. © 2024 IEEE.

关键词： Smart city

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Deep Learning-Based Algorithmic Trading Based on News and Events Strategies 7th

Deep Learning-Based Algorithmic Trading Based on News and Ev...

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7th International Conference on Soft Computing: Theories and applications, SoCTA 2023

作者： Kayathri, V. Prabakaran, P. Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai600 117 India

ISBN: (纸本)9789819720309

Successful stock market investors should consider these trends. This article defines price effect, optimal execution, and placement through the lens of algorithmic trading and strategies based on news and events. By using feedbacks controlled to include consideration of a stop-loss order, algorithmic trading allows traders and investors at large to liquidate or construct big securities positions in a fully automated manner, thus generalising the existing stock trading results. Trading decisions made by the AI agent could affect the asset price. We see this as a marketplace where people are bartering and swapping goods. Creating a new market is the driving force behind our research into generative models. This news sentiment does a good job of describing stocks, and AI systems that read the news and quantify them react to factors that affect prices far more quickly than humans do. All three of the stock market's underpinnings—price, volume, and volatility—respond strongly to news events. Sentiment analysis is a method for gauging the overall tone of a news piece and classifying it as optimistic, pessimistic, or neutral. The simulation is conducted in python to test the effectiveness of the model in trading in an automated manner for stocks. The results show that the proposed sentimental analysis for stock trading achieves higher rate of accuracy and precision than the other methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Financial markets

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Learning Techniques for Depression Detection: A Comparative Studies

Learning Techniques for Depression Detection: A Comparative ...

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Das, Arnab Debbarama, Mrinal Kanti Barik, Rabindra K. National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350399387

Depression is a common mental problem that can fundamentally affect individuals' emotional wellness as well as their everyday lives. After COVID-19 other pandemics and subsequent social isolation this issue is more potent than ever. Numerous research works have been going on searching for methods that effectively recognize depression in order to detect depression. In this regard, a number of studies have been proposed. In this study, it examines a number of previous ones utilizing various Machine Learning (ML) and Artificial Intelligence (AI) methods for depression detection. In addition, various methods for determining an individual's mood and emotion are discussed. This study also discusses how facial expression, voice, gesture can be understood by chatbot and classified it as a depressed person or not. Addition to this, it reviews all the related research works and evaluates their methods to detect depression. © 2023 IEEE.

关键词： COVID-19

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Unlocking Math Potential: EDSense - A Personalized Intervention Tool for Children with Dyscalculia 3

Unlocking Math Potential: EDSense - A Personalized Intervent...

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3rd International Conference on Advanced Computing Technologies and applications, ICACTA 2023

作者： Jadhav, Dipti Chettri, Sarat Kumar Tripathy, Amiya Kumar Ghate, Om Chaudhari, Raj Avhad, Sarvesh Don Bosco Institute of Technology Department of Computer Engineering Mumbai India School of Technology A.D.B.U. Department of Computer Applications Guwahati India

ISBN: (纸本)9798350348347

Math difficulties, such as dyscalculia, can hinder children's ability to acquire strong mathematical problem-solving capabilities, limiting their potential for success. Early identification and intervention are essential in addressing these challenges. This research paper introduces EDSense, a specialized Bayesian Knowledge Tracing (BKT) model developed for math skill assessment in children who may have dyscalculia. The system employs a Directed Acyclic Graph (DAG) to represent the dependencies between probabilities and incorporates the prior probability of mastery, slip rate, and guessing rate to update the student's knowledge state. The case study findings revealed challenges in math comprehension, multiplication, and symbol identification among children who may have dyscalculia, highlighting the need for targeted interventions and instructional strategies. The personalized and data-driven approach of EDSense contributes to the field of dyscalculia intervention, providing a promising tool for math skill assessment in children who may have dyscalculia. The development of EDSense has the potential to enhance early identification and intervention for children with dyscalculia, leading to improved math outcomes and better support for students with this specific learning disorder. © 2023 IEEE.

关键词： Directed graphs

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Blockchain platform for prophylactic vaccine preparation using signature based secured authentication

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International Journal of Information technology (Singapore) 2024年第7期16卷 4209-4224页

作者： Shinzeer, C.K. Kushwaha, Ajay Shriram Bhagat, Avinash School of Computer Applications Lovely Professional UniversityDelhi G.T. Road Phagwara Sharda School of Engineering & amp Technology Department of CSA Sharda UniversityGreater Noida-201310

The consensus algorithm is necessary for all blockchain services since it is critical to maintaining data consistency and integrity in the blockchain network. In this paper, a new architecture algorithm, namely SigBlockchain, to function as an enterprise node within Multi-chain and form the basis of a preventative vaccine preparation framework is proposed. The SigBlockchain algorithm is made to withstand node failures and yet preserve the consistency and integrity of data. Enterprise nodes within the SigBlockchain framework serve as a vital component for aggregating user profile information. They enable users to receive rewards for sharing their data with other providers, all while adhering to their specified privacy preferences outlined in smart contracts. To ensure seamless processing and utilization of this data by other nodes, the repository converts user data into an open format and transmits it through blockchain streaming. Double deposit collateral is required by the smart contract that uses the SigBlockchain algorithm to guarantee the integrity of all participants. Through empirical evaluation, SigBlockchain achieves a significant improvement in accuracy, reaching 97.5% compared to existing techniques. This enhancement underscores the effectiveness and reliability of the SigBlockchain algorithm in ensuring data integrity and trustworthiness within the blockchain network. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Block chain Decoding Encoding Hash Value Private key Public key Signature generation

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Prediction of Cyber Attacks Utilizing Deep Learning Model using Network/Web Traffic Data 3

Prediction of Cyber Attacks Utilizing Deep Learning Model us...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kannan, Balaji Sakthivanitha, M. Jayashree, S. Maruthi, R. School of Computing Science Department of Information Technology Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350375190

Nowadays, cyber-attacks are growing predominantly due to the development of technologies. It will lead to financial losses to a company and the other problems related to attacks. It is very important to predict such attacks from outsiders to safeguard our networking systems to provide effective security. The Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques leverage enormous amounts of data to identify the cyber attacks. These learning approaches are used to identify a broad range of cyber-attacks by analyzing the web traffic or network traffic to identify potential threats such as malware, network intrusions and other types of attacks. The study demonstrates the various deep learning methods to predict the anomalies and other potential threats with more accuracy in real time. © 2024 IEEE.

关键词： Forecasting

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AI-Driven Solutions for Predictive Maintenance in Smart Transportation Systems

AI-Driven Solutions for Predictive Maintenance in Smart Tran...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Hundekari, Sheela Mittal, Tanusha Dutt, Amit Bhavana, Munugapati Kumar, Rakesh Nijhawan, Ginni School of Computer Applications Pimpri Chinchwad University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331512965

Predictive maintenance: A game changer in infrastructure and vehicle management, and able to cut maintenance expenses and downtime of smart transportation systems. This study explores ways in which smart transportation systems might use AI in order to plan maintenance schedules and forecast equipment breakdowns. Predictive algorithms, that can detect potential problems before raising to expensive repair or system breakdown, use real time sensor data from infrastructure and vehicles. Decision trees and neural networks are two of these models. By allowing AI driven solutions, transportation systems may transition from a reactive to a proactive maintenance practice which may support efficiency, safety, and reliability. Unsupervised learning methods are used to do anomaly detection for real time operations, and we examine the patterns in previous maintenance data with supervised learning approaches. The use of AI for predictive maintenance is also being studied for use in smart cities, where transportation networks are becoming more interconnected by IoT. The paper also considers challenges on data integration as well as the importance of scaling the algorithms and guaranteeing cybersecurity in these systems. Based on our research, smart transportation systems could benefit from AI to increase operational efficiency, reduce the cost of material and labor to maintain and repair equipment systems, and decrease the rate of disruptions. © 2025 IEEE.

关键词： Predictive maintenance

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Artificial Intelligence based System to Prevent Animal Accidents in the Railway Tracks 8

Artificial Intelligence based System to Prevent Animal Accid...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Kannan, Balaji Lakshmi, R.Bagavathi Sakthivanitha, M. Maruthi, R. School of Computing Science Department of Information Technology Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350386578

The wildlife population has been in danger due to frequent animal accidents on railway tracks. In India, animal train collisions cause an average of 20 elephant deaths every year all over the nation. At the same time railway departments are taking necessary steps to identify the cause and implement prevention methods. Despite this, lots of accidents are happening in the railway tracks near the forest areas. This problem has been addressed by proposing a well-defined technological mechanism to alert the animals to be away from the railway tracks to reduce the wildlife deaths. In this study, Machine Learning(ML) frameworks are employed to detect the animals that are coming near the railway tracks and the developed module will provide appropriate warning or signals to the animals as well as to the loco pilot. © 2024 IEEE.

关键词： Invertebrates

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IoT based Smart wheelchair For Disabled Person

IoT based Smart wheelchair For Disabled Person

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Lalit Kushwaha, Ajay Shriram Sherawat, Kartikeya Siddique, Zishan IILM University School of Computer Scienceand Engineering Department of Computer Application Uttar Pradesh Greater Noida India Sharda University Sharda School of Engineringand Technology Department of Computer Scienceand Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350378092

Smart wheelchairs are revolutionizing mobility for disabled individuals, offering enhanced independence and improved quality of life. These advanced devices incorporate cutting-edge technologies like obstacle avoidance systems, voice control, and self-navigation capabilities, transforming the traditional wheelchair into an intelligent mobility solution. Obstacle avoidance systems utilize sensors to detect and avoid obstacles, ensuring safe navigation in complex environments. Voice control enables users to operate the wheelchair hands-free, providing greater convenience and control. Self-navigation capabilities allow the wheelchair to autonomously navigate to pre-programmed destinations, reducing user burden and enhancing independence. Smart wheelchairs also offer connectivity features, enabling remote monitoring and control by caregivers. This provides an added layer of safety and support for users, ensuring timely assistance when needed. The integration of these advanced technologies into smart wheelchairs is transforming mobility for disabled individuals, empowering them with greater independence, safety, and control over their movement. As smart wheelchair technology continues to evolve, the possibilities for enhanced mobility and improved quality of life for disabled individuals are limitless. © 2024 IEEE.

关键词： Disabled persons

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Integrating Natural Language Processing in Artificial Intelligence-Driven Online Spam Detection: "A Forward-Looking Perspective" 5

Integrating Natural Language Processing in Artificial Intell...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Kumar, Biresh Kumari, Varsha Singh, Kamakhya Narain Amity Institute of Information Technology Amity University Jharkhand Jharkhand Ranchi India School of Computer Applications KIIT Deemed University Bhubaneswar India

ISBN: (纸本)9798350361155

This study proposes a spam discovery framework utilizing Normal Language Processing (NLP) to dissect messages for spam watchwords and connections. The interaction incorporates information pre-processing, highlight extraction, and model determination. The framework is prepared, assessed, and adjusted, offering continuous observation and client connection. By consolidating NLP with AI and profound learning, the framework successfully channels spam messages, offering a flexible and versatile answer for email security, tending to the tireless test of spam in the present computerized correspondence scene. It gives a proactive safeguard against spontaneous and possibly destructive messages, guaranteeing the trustworthiness and well-being of email interchanges. The strategy includes the assortment of different datasets containing both genuine and spam content, empowering the preparation of a strong man-made intelligence model. Highlights like text-based designs, client conduct, and metadata are separated to improvise the model's tendency to observe among authentic and spam content. The preparation cycle utilizes managed learning methods, permitting the model to sum up designs and adjust to advancing spam strategies. As the volume of online substance keeps on developing dramatically, the issue of spam represents a critical danger to the quality and respectability of computerized correspondence. This paper presents a sharp method for managing battling web spam through the combination of computerization method. © 2024 IEEE.

关键词： Machine learning

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