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Enhanced Monitoring and Security Measures with Video Analysis

Enhanced Monitoring and Security Measures with Video Analysi...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Jyothi, V. Esther Rakesh, G. Aruna, Vipparla Gorintla, Shobana Ayyappa, Yalanati Naidu, U. Ganesh Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Department of Computer Applications A.P. Vijayawada India Department of Computer Science and Engineering Pothavarapadu A.P India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Avadi India B V Raju Institute of Technology Telangana Narsapur India

ISBN: (纸本)9798331509828

In modern surveillance, activities have increasingly become dependent on the continuous observation offered by CCTV systems. Still, with massive amounts of video data generated in a minute, sifting through this information manually to pick up any anomalies would be extremely burdensome and nearly impossible without significant human labor and vigilant watchfulness. CNN and CLIP models changed everything with regards to the working of surveillance systems. It makes use of CNN's capability for video frame processing and analysis to detect and classify these activities in the frames as either normal or suspicious. At the same time, it improves this by adding the CLIP model's capability of understanding textual descriptions and visual content together for better nuance detection in suspicious activities. This methodology transforms video surveillance by breaking video streams into frames and analyzing the behavior and interactions of persons in those frames. The synergy of CNN and CLIP models not only ensures real-time, efficient, and accurate surveillance but also minimizes dependency on extensive manual labor for monitoring. This paper aims to provide a contribution toward developing better security infrastructures in public spaces, transportation hubs, and private facilities. The introduction of this anomaly-detection mechanism can hugely improve the capability of implemented surveillance systems with a proactive response based on real-time detection and response capabilities against suspicious activities. © 2025 IEEE.

关键词： Video analysis

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Comparative Analysis of Techniques for Efficient Resource Utilization in Cloud Environments Through VM Placement Optimization

Comparative Analysis of Techniques for Efficient Resource Ut...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Natarajan, P. Panjatcharam, V.G. Rajkumaran, T. Erode Sengunthar Engineering College Dept of Mca Erode India Vet Institute of Arts and Science Dept of Computer Science Erode India Vet Institute of Arts and Science Dept of Computer Science '&' Applications Erode India

ISBN: (纸本)9798350300857

This study focuses on evaluating and comparing different VM placement techniques in a simulated cloud environment. The performance metrics, including resource utilization, load balancing, number of servers used, average response time, fault tolerance, and storage capacity, are analyzed for each technique. The results indicate that the Supervised technique achieves the highest resource utilization, load balancing, and storage capacity, along with the lowest average response time. The Bin Packing technique demonstrates efficient server utilization and medium fault tolerance. The Load-Aware and Genetic techniques exhibit a trade-off between resource utilization, load balancing, and fault tolerance. These findings provide valuable insights into selecting appropriate VM placement techniques based on specific requirements. © 2023 IEEE.

关键词： Virtual machine

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Predicting Chronic Kidney Disease Progression Using Classification and Ensemble Learning 8th

Predicting Chronic Kidney Disease Progression Using Classifi...

引用

8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Valli Mayil, V. Ratha Jeyalakshmi, T. Gupta, Rishu Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Department of MCA Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India

ISBN: (纸本)9789819732418

A serious medical problem that impacts millions of people globally is chronic kidney disease, or CKIDD. Accurate CKIDD progression prediction is essential for disease management and early intervention. Machine learning (ML) has shown great potential in predicting the course of CKIDD by employing a variety of medical, laboratory, and demographic factors. In our research, we suggested an ensemble approach for the increased level of accuracy. We contrast the outcome with well-known machine learning models such as neural networks, Dec-Trees, random forest, and support vector machines. On a sizable dataset of CKIDD patients, where good progression outcomes are identified, our model is trained. Our model is trained on a big dataset of CKIDD patients and proposed to find the result that the patient is affected or not. The chronic kidney disease dataset from Kaggle is utilized to test the model. Standard metrics including accuracy, precision, recall, F1-score, and area under receiver operating characteristic curve (AUC-ROC) are considered for proving the capability of the proposed work. In comparison to the conventional method, our model's experiment results demonstrate 100% accuracy in pattern prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Use of Polygram Substitution in Spotting the Swindler in Secret Sharing 11th

Use of Polygram Substitution in Spotting the Swindler in Sec...

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11th International Conference on Intelligent Computing and applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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Virtual Mouse Using Face Gesture and NLP 2

Virtual Mouse Using Face Gesture and NLP

引用

2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Zad, Ajay Jagannatha, S. Kumar, Manish Ajitha, S. Prakash, B.R. M S Ramaiah Institute of Technology Department of Masters of Computer Applications Bangalore India East Point College of Engineering Department of Computer Applications Bangalore India M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

The mouse plays a very important role in interacting with laptop's and computer system, to perform necessary operations. In this paper, we are proposing a method to carry out mouse operations on the computer system without a physical mouse by utilizing voice commands and face-motion gestures. The system employs an internal webcam to detect facial gestures and an internal microphone to receive voice commands from the user. By employing a virtual mouse based on face gestures and natural language processing (NLP), we can accomplish all the fundamental functionalities of a mouse by issuing voice commands to the computer system. The system accepts these commands and executes corresponding mouse events such as "click,""right-click,""double-click,""scroll-up,""scroll-down,""minimize,"and "close."The mobility of the mouse cursor on the monitor is achieved through face motion gestures, enabling the cursor to move up, down, left, and right, respectively. © 2023 IEEE.

关键词： Natural language processing systems

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Deep Learning-Based Self-Supervised Transfer Learning for Medical Image Classification 2

Deep Learning-Based Self-Supervised Transfer Learning for Me...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Shaikh, M.Z. Singh, Samender Varshney, Neeraj Saraswat, Birendra Kumar Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India

ISBN: (纸本)9798350371055

Self-supervised switch getting to know is a practical approach for the scientific image type. It includes using unlabeled statistics (from non-clinical images) to teach models to categorize medical photos with low attempts and excessive accuracy. This approach has been utilized in numerous duties, including clinical photo segmentation, computer-aided prognosis, and disease category. To summarize the research studies carried out in this discipline, a complete survey of self-supervised switch learning for scientific image type was performed in 2020. The survey evaluations numerous transfer mastering techniques often used in healthcare, which include area adaptation, multitasking studying, and zero/one-shot mastering. It additionally gives an in-intensity analysis of the modern-day challenges and capability solutions. © 2024 IEEE.

关键词： Medical Outperforms Results Unlabeled Utilizes

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The λNF Problem 2nd

The λNF Problem

引用

2nd International Conference on Cybersecurity and Evolutionary Data engineering, ICCEDE 2022

作者： Dutta, Anurag Munjal, DeepKiran Department of Computer Science and Engineering Government College of Engineering and Textile Technology Serampore India Department of Computer Applications G L Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9789819950799

The Dutch National Flag problem—DNF Problem, a Computational Problem proposed by the Dutch computer Scientist—Edsger Wybe Dijkstra—brought a new edge to the domains of Sorting. In this work, we would extend Dijkstra’s Problem to a generalized version, where we would be considering flags with variable numbers of colours and will try to devise an algorithm to arrange them in a monotonic order by its magnitude. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Computational complexity Dnf sort Partitioning Sorting algorithms

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PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

PSST: A Benchmark for Evaluation-driven Text Public-Speaking...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9798891761681

Language style is necessary for AI systems to understand and generate diverse human language ***, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application *** overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking *** in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of *** such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized *** experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Precaution Chatbot for Epilepsy Patients using Natural Language Processing and Deep Learning Sequential Model 5

Precaution Chatbot for Epilepsy Patients using Natural Langu...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Kasthuri, E. Subbulakshmi, S. Sreedharan, Rajasree Department of Computer Science and Engineering Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amritapuri India Division of Nephrology Department of Pediatrics Medical College of Wisconsin MilwaukeeWI United States

Epilepsy is a brain disorder that can affect people of all ages which is one of the most common neurological disorders affecting around 50 million individuals. Healthcare Chatbots schedule appointments, respond to patient inquiries, and offer relevant information, freeing medical staff to handle life crises. This paper implements a chatbot for epilepsy patients to increase awareness about epilepsy. Patients may easily access this AI-based healthcare chatbot whenever they have queries regarding precautions to be taken by epilepsy patients. Chatbot uses a rich set of epilepsy-related datasets collected from trusted websites which are preprocessed with NLP techniques to prepare the data for model ***, LSTM, and RNN models are created and evaluated to produce answers to user queries related to possible precautions to avoid seizures, safety measures to be taken by patients with seizure history, etc. Evaluation of models shows that the RNN model generates more accurate answers with 94% accuracy. Finally, the epilepsy question answering Chatbot for precaution is concluded with the RNN model. © 2024 The Authors. Published by Elsevier B.V.

关键词： Deep neural networks

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AI in Medical Imaging: Enhancing Diagnostic Accuracy with Deep Convolutional Networks

AI in Medical Imaging: Enhancing Diagnostic Accuracy with De...

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

作者： Shrivastava, Anurag Bhadula, Shuchi Kumar, Rakesh Kaliyaperumal, Gopal Rao, Bolleddu Devananda Jain, Alok Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The past has witnessed a time of astounding developments in AI, particularly notable deep convolutional networks, which has greatly facilitated medical imaging by opening up previously unattainable doors for both precision and effectiveness of diagnoses. This study is an investigation into how deep convolutional neural networks (CNNs) might be applied (using MRI, CT, and X-ray as examples) to medical imaging for the purposes of improving accuracy, consistency, and practicability of diagnostic procedures. Through automated feature extraction and categorization, convolutional neural networks (CNNs) enable earlier, and more accurate, detection of illnesses such as cancer, cardiovascular disease and neurological problems by detecting minor deviations that humans may miss. Here we evaluate the accuracy, sensitivity, and computing efficiency of several convolutional network (CNN) designs. ResNet and UNet are these architectures. The results indicate that CNN based models provide better performance compared to conventional diagnostic approaches with a substantial diminution in both false positives and false negatives. The article also covers combining CNNs with others AI methods such as transfer learning, ensemble methods, etc. to further improve diagnostic results. Although the encouraging outcomes, data protection, interpretability, and clinical acceptance continue to be obstacles. This study shows how AI driven technology can revolutionize medical imaging to enhance patient outcomes as well as tailored healthcare solutions. © 2025 IEEE.

关键词： Convolutional neural networks

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