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3rd IEEE International Conference on Intelligent technologies, CONIT 2023

作者： Ramanathan, Sivasubramanian Syed, Kulsoom Chavan, Tejaswini B. Tech Computer Science & Business Systems SVKMs NMIMS Navi Mumbai Mumbai India School of Technology Management & Engineering SVKMs NMIMS Navi Mumbai Mumbai India

ISBN: (纸本)9798350338607

This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse IoT systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urban systems and subsystems. We propose taking into consideration several attributes when analyzing human mobility patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urban green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainable and green smart cities. © 2023 IEEE.

关键词： Smart city

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Energy Proficient and Dependable Cluster Routing in Wireless Sensor Network

Energy Proficient and Dependable Cluster Routing in Wireless...

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2023 IEEE International Conference on Intelligent and Innovative technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Hemavathi, S. Jeya Ganesan, J. Geetha, G. Kishore Kumar, K. Kohila, R. Sri Sairam Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India B v Raju Institute of Technology Department of Computer Science Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science Engineering Tamil Nadu Chennai India Tamil Nadu Namakkal India

ISBN: (纸本)9781665462631

Huge energy utilization information of structures is created with the improving information technology. The energy utilization information is processed by an energy depletion observing system through the wireless sensor network (WSN). Conventional security approaches can face external attacks. Nevertheless, they cannot proficiently solve interior attacks triggered by the captured nodes. Energy-efficient and Dependable Cluster Routing (EPDC) in WSN. This approach aims to reduce energy expenditure and enhance reliable routing in WSNs. The highest energy and highest available bandwidth are selected as a cluster head (CH). In every cluster, the selected CH has received the observed information from the cluster member and then forwarded it to the bS through a relay node. The forwarder relay node is selected based on the average energy faith and transmission faith during data transmission. The whole faith is greater than the average value that node is selected as a data forwarder from cluster to bS in the WSN. Furthermore, the Path Optimization Algorithm (POA) is used to detect the obstacle and build the obstacle-aware route efficiently. The simulation results explain why the proposed method has better residual energy. In addition, it minimized both the network delay and the packet loss rate in the WSN. © 2023 IEEE.

关键词： bandwidth

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Customer Analytics using Surveillance Video

arXiv

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arXiv 2025年

作者： Ijjina, Earnest Paul Joshi, Aniruddha Srinivas Kanahasabai, Goutham Priyanka, P. Keerthi Department of Computer Science and Engineering National Institute of Technology Warangal506004 India B.Tech Department of Computer Science and Engineering National Institute of Technology Warangal506004 India

The analysis of sales information, is a vital step in designing an effective marketing strategy. This work proposes a novel approach to analyse the shopping behaviour of customers to identify their purchase patterns. An extended version of the Multi-Cluster Overlapping k-Means Extension (MCOKE) algorithm with weighted k-Means algorithm is utilized to map customers to the garments of interest. The age & gender traits of the customer;the time spent and the expressions exhibited while selecting garments for purchase, are utilized to associate a customer or a group of customers to a garments they are interested in. Such study on the customer base of a retail business, may help in inferring the products of interest of their consumers, and enable them in developing effective business strategies, thus ensuring customer satisfaction, loyalty, increased sales and profits. Copyright © 2025, The Authors. All rights reserved.

关键词： Sales

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Context-Aware Summarization for PDF Documents using Large Language Models

Context-Aware Summarization for PDF Documents using Large La...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Akshara Ramprasad P. Sivakumar B. Tech Computer Science with specialization in AI & ML School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

In an era characterized by the overflow of textual information, the demand for effective text summarization techniques has become increasingly evident. This research study presents a novel solution to address this demand: the development of a PDF Summarizer Generative AI Platform for context-aware text and document summarization. The developed platform empowers users to generate informative summaries from lengthy textual documents. It features an interface powered by Gradio for enabling user-friendly accessibility. Equipped with essential components such as OpenAI's language models for text comprehension, Chroma vector enables document embedding and conversational retrieval chains for contextual understanding, where the platform offers a comprehensive solution. Users can effortlessly upload documents in various formats, including PDFs, and interactively engage with the system to obtain concise summaries tailored to their specific requirements.

关键词： Generative AI Databases Large language models Text summarization Portable document format Vectors Context modeling

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Real-Time Video Enhancement with Spatio-Temporal Attention via Deep Convolutional Neural Networks 3rd

Real-Time Video Enhancement with Spatio-Temporal Attention v...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Maria, Anto bennet balasubramanian, Swaminathan Vijayan, Rajmohan Sonthi, Vijaya Krishna Ramakrishnan, Jayapratha Manogaran, Umamaheswari Sengan, Sudhakar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Karnataka Bengaluru560069 India Tamil Nadu Chennai602107 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The realm of video content has witnessed exponential growth, and alongside it emerges the challenge of enhancing video quality efficiently. Traditional techniques, although valuable, often fall short of addressing the intricate details and temporal consistencies required for high-quality video upscaling. Moreover, real-time processing demands methods ensuring accuracy and swift computational speeds. Addressing these challenges, this paper introduces the STA-DCNN. This innovative architecture is rooted in combining spatial and temporal attention mechanisms to produce superior video super-resolution. The Spatio-Temporal Attention Module (STAM) acts as a cornerstone, singling out essential features across frames, while the Deep Convolutional backbone (DCb) intensifies these frames, capitalizing on attention-informed features. Furthermore, the Temporal Memory Unit (TMU) preserves crucial aspects from preceding frames, guaranteeing a seamless video rendition. Empirical tests confirm that STA-DCNN notably surpasses its counterparts, offering an ideal balance of quality and real-time processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

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Dynamic Website Authentication Using Time-based One-Time Password (TOTP) for Enhanced Security

Dynamic Website Authentication Using Time-Based One-Time Pas...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： M Caleb Gunalan bavani K Mathew Hayden Manoj Hariharan R Rahul S B.Tech Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

In today's digital landscape, the proliferation of fake websites poses significant threats to user security, trust, and data integrity. This paper presents a novel solution to address this issue through the implementation of a dynamic website authentication system using Time-based One-Time Password (TOTP) verification. The system allows websites to register securely with a central authentication server, generating unique TOTP codes that users can verify to determine the authenticity of the website. Website owners are issued a secret key upon registration, which is used to generate TOTP codes that refresh every 30 seconds. This project emphasizes user-friendly verification mechanisms and secure key management to protect users from phishing attacks. The system architecture, implementation, and potential applications are discussed, along with future directions for integrating multi-factor authentication and expanding security features.

关键词： Codes Multi-factor authentication Phishing Prevention and mitigation Data integrity Systems architecture Passwords QR codes Registers Servers

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Individual Privacy Protection in Cybersecurity Using Data Analytics and Machine Learning

Individual Privacy Protection in Cybersecurity Using Data An...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Arun, M. bhavadharani, K. Supreeth, b.R. Pundir, Sumit Pradeepa, K. Razak, K. Abdul Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Tamil Nadu Karur India Oxford Degree and Pg College Department of Computer Science Sir. M. VisveshwaraiahLayout Ullal Bangalore India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Kpr College of Arts Science and Research Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India K. Ramakrishnan College of Engineering Department of Mathematics Tamil Nadu Trichy India

ISBN: (纸本)9798331543617

Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs data analytics and machine learning (ML) to promote individual privacy in the digital realm. The system analyzes several data sources, including system events, network traffic, and user activity logs, using advanced ML methods like Random Forests (RF), Support Vector Machines (SVM), and Neural Networks (NN), to detect and prevent privacy violations. Ensemble learning techniques, feature engineering, and real-time monitoring enable adaptability in the face of shifting threats. According to the results, the proposed system surpassed the existing system in terms of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregular data requests is proved by anomaly detection rates utilizing One-Class SVMs (91%) and Isolation Forest (89%). The system's low false positive rate of 5% demonstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises. © 2024 IEEE.

关键词： Cybersecurity

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Recommendation of Personalized Learning Path in Smart E-learning Platform Using Reinforcement Learning Algorithms 8th

Recommendation of Personalized Learning Path in Smart E-lear...

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8th International Conference on Inventive Communication and Computational technologies, ICICCT 2024

作者： Deepa, S. Arunkumar, M.S. Kanimozhi, T. Eswaran, balamurugan Duraivelu, Vivek Sweatha, M. Department of Computer Technology-PG Kongu Engineering College Tamil Nadu Erode Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India School of Information Technology Texila American University Lilayi Zambia Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur502313 India Kongu Engineering College Tamil Nadu Erode Perundurai638060 India

ISBN: (纸本)9789819777099

The exponential expansion of e-learning has significantly broadened educational opportunities globally, empowering students to engage in learning experiences from anywhere. However, the abundance of online courses poses a challenge in customization, often resulting in diminished learning outcomes as learners struggle to select suitable courses tailored to their unique requirements. Personalized learning addresses this by adapting activities to individual needs, fostering the development of capabilities and personality. The proposed system integrates Markov Decision Processes (MDPs) and Reinforcement Learning (RL), leveraging deep Q-learning for sequential recommendations and MDP adaptability to tailor learning paths. This combined approach holds promise for revolutionizing e-learning by delivering personalized, adaptive experiences. Learners benefit from tailored courses, activities, and learning paths, optimizing their educational journey. Continuous adaptation and improvement, facilitated by the core MDP framework and user feedback, further enhance effectiveness in e-learning environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Federated learning

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Prevention from gas devastation

Prevention from gas devastation

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2020 International Online Conference on engineering Response to COVID-19, IOCER-COVID-19 2020

作者： Datta, Sanghita Sah, Ankita Gautam, Sidharth Das, Upamita Maity, Saikat B. Tech Computer Science Engineering JIS University India

LPG leakages are the most common issues found in household and gas plants. This issue is life threatening not only for the humans in the vicinity but also for the surrounding areas. The idea behind our project is to save the lives of those innocents who can get affected due to the carelessness of certain people. So, as soon as the gas gets detected by the system, the concerned people around will receive messages as well as the alarm rings. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Gas plants

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An IoT and Random Forest-DT-based Early Enhanced Forest Fire Detection in Large and Small Targets 3

An IoT and Random Forest-DT-Based Early Enhanced Forest Fire...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Prabakaran, Manjula Wadhwani, Omesh Pandiaraj, V. Zainab, Kaneez Nishant, Neerav Justin, Z. Andhra Pradesh India Shri Ramdeobaba College of Engineering and Management Department of Information Technology Nagpur India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mechanical Engineering Chennai India B.N. College of Engineering and Technology Department of Computer Science & Engineering Lucknow India Babu Banarasi das University School of Engineering Department of CSE Lucknow India Thiruvalluvar India

ISBN: (纸本)9798350381931

The world is confronted with more complex and severe environmental concerns than in the past. Extreme weather events have become more common and destructive in the last several decades, endangering forests and urban forests around the globe. Massive forest fires are among the most catastrophic natural catastrophes that affect Earth's climate and the diversity of life on it. Thus, in order to minimize harmful impacts on ecosystems and people, it is vital to have well-planned and coordinated plans for early warning, prevention, and reaction. Image preprocessing, feature extraction, and training the model are the initial steps in the suggested methodology. Histogram matching and picture smoothing are part of the image preparation procedure. The gray histogram is a way to see how many pixels fall into each grayscale level, and image smoothing is a way to enhance a large region of a picture using image processing. The GLCM and LbP histograms are employed in the feature extraction process. Training RF-Multivariate ARS models requires feature extraction as a prerequisite. The system improved accuracy to 97.29 percent after applying the approach. © 2024 IEEE.

关键词： Feature extraction

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