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检索条件"机构=Computer Applications in Science and Engineering"

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

Comparative Analysis of Techniques for Efficient Resource Ut...

引用

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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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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Multiple Manifold Regularization Based on Non-Negative Matrix Factorization for Multi-View Clustering 3

Multiple Manifold Regularization Based on Non-Negative Matri...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Sirisha, Mutyala Khan, Ghufran Ahmad Khan, Jalaluddin Pant, Hemlata Zainab, Kaneez Ansari, Zulfikar Ali Ansari, Shamshul Haq Koneru Lakshmaiah Education Foundation Department of Computer and Applications Andhra Pradesh Vaddeswaram522502 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Kanpur209305 India B.N. College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow226201 India Pune Campus Lavale Pune412115 India

ISBN: (纸本)9798331519582

In recent years, non-negative matrix factorization (NMF) cluster analysis of multi-view data has shown outstanding results in data mining and machine learning. Multi-view data typically encompasses complementary elements from multiple perspectives. Finding a consensus solution that works for all the different views is a difficult task. This study proposes a novel NMF-based clustering method that utilizes various manifold regularizations for multi-view data to address the previously mentioned problem. In this method, the NMF will decompose the input data into the two non-negative matrices. Next, we employ the manifold scenario to preserve the geometric structure within the data. Lastly, we design the novel objective function by combining all the earlier mentioned terms and then optimize it using the iterative strategy method to achieve the optimal solution. The empirical analysis of real-world datasets indicates that the proposed method outperforms numerous existing techniques in clustering efficiency. © 2025 IEEE.

关键词： Non-negative matrix factorization

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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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Incomplete Multi-view Clustering via Diffusion Contrastive Generation 39

Incomplete Multi-view Clustering via Diffusion Contrastive G...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yuanyang Lin, Yijie Yan, Weiqing Yao, Li Wan, Xinhang Li, Guangyuan Zhang, Chao Ke, Guanzhou Xu, Jie School of Computer Science and Engineering Southeast University Nanjing210096 China College of Computer Science Sichuan University China School of Computer and Control Engineering Yantai University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications [Southeast University Ministry of Education China College of Computer National University of Defense Technology China College of Computer Science and Technology Zhejiang University China Department of Control Science and Intelligence Engineering Nanjing University China School of Economics and Management Beijing Jiaotong University China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)157735897X

Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to address this challenge involves recovering the missing views before applying conventional multi-view clustering methods. Although imputation-based IMVC methods have achieved significant improvements, they still encounter notable limitations: 1) heavy reliance on paired data for training the data recovery module, which is impractical in real scenarios with high missing data rates;2) the generated data often lacks diversity and discriminability, resulting in suboptimal clustering results. To address these shortcomings, we propose a novel IMVC method called Diffusion Contrastive Generation (DCG). Motivated by the consistency between the diffusion and clustering processes, DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data. By performing contrastive learning on a limited set of paired multi-view samples, DCG can align the generated views with the real views, facilitating accurate recovery of views across arbitrary missing view scenarios. Additionally, DCG integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data, achieving robust and end-to-end clustering. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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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 Findings of the Association for Computational Linguistics, 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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