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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karthigeyan, Shreyas Bhol, Sampark Jain, Akshat Yadav, Karthik Sinha, Akash Padma Priya, R. School of Computer Science and Information Systems Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS. © 2025 The Authors. Published by Elsevier B.V.

关键词： Hybrid power

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AI Based Smart Resource Sharing and Donation System for Hospitality and Charities 3

AI Based Smart Resource Sharing and Donation System for Hosp...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Deepak Kumar, A. Ashibha, S. Janani Priya, S. Balamurali, A. St. Joseph's Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542375

This platform allows hospitality establishments and charities to collaborate more easily by streamlining contributions, improving inventory management and resource sharing. Restaurant menus will be accessible to users as well as employees of participating hotels and charity representatives through straightforward registration and login that provides the safe, role-based access. The administrator controls all lists of food items to include cost, availability and expirations. A secure payment mechanism makes a difference between paid and free resources. A donation module may be applied whereby hotels can share their surplus food, apparel, and drugs with nearby organizations, and it will also make easier the process of conducting requests and searches in real-time based on the specific location. AES-128 and RSA-2048 are used to encrypt personal data and financial transactions. The system is developed in Java, HTML, CSS, JavaScript, and JSP. This has made the system capable of meeting speed criteria and interacting effectively with many user groups. This system will contribute to society as it reduces waste and helps in improving operational efficiency. © 2025 IEEE.

关键词： Nonprofit organization

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New Multi-Qubit CHSH Games and their application to Device-Independent Quantum Key Distribution protocols 17

New Multi-Qubit CHSH Games and their application to Device-I...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Venugopal, S. Singh, Kunwar National Institute of Technology Tiruchirappalli Department of Computer Science and Engineering Tamil Nadu Trichy620 015 India

ISBN: (纸本)9798331531195

In the Quantum Computing field, the CHSH (Clauser-Horne-Shimony-Holt) game plays a vital role in determining whether a system is in classical or quantum domain. This quantum advantage property is applied in determining the security of the quantum systems, especially in quantum cryptography, in the form of Device-Independent (DI) secure Quantum Key Distribution (QKD). The traditional CHSH game is designed for a system with two players, where each player has access to one entangled qubit from the Bell's state. In 2024, Hamza Jaffali and Frédéric Holweck proposed a new three-qubit CHSH game by extending the traditional CHSH game to three players using the three-qubit Greenberger-Horne-Zeilinger (GHZ) state and W *** this paper, first, we present our findings of new CHSH quantum games with quantum advantage in the four-qubit and five-qubit systems. These CHSH quantum games, when played using their quantum strategy, are found to outperform any classical strategy. Second, we apply the quantum advantage property of the existing three-qubit game to develop the corresponding three party DIQKD protocol. Third, we then apply the quantum advantage property of the newly developed four-qubit and five-qubit games to develop their corresponding four party and five party DIQKD protocols. © 2025 IEEE.

关键词： Qubits

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Hybrid Algorithm for Secure Routing in MANET Using Cryptographic Applications

Hybrid Algorithm for Secure Routing in MANET Using Cryptogra...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 IEEE.

关键词： Encryption algorithms

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Sarcasm Detection in English-Hindi Code-Mixed Text Data 2

Sarcasm Detection in English-Hindi Code-Mixed Text Data

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Prajapati, Abhinav Singh, Ajeet Arora, Vasudha Bansal, Nidhi Department of Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India Department of Computer Science & Engineering Moradabad Institute of Technology Uttar Pradesh Moradabad India Nagpur Campus Pune India

ISBN: (纸本)9798331541217

Sarcasm-also termed as verbal irony, when the speaker presents something in a mocking or humorous way such that he or she actually conveys the opposite of what they seem to say. This article has a detailed analysis of sarcasm detection in English-Hindi code-mixed texts based on the difficulties one faces while identifying sarcasm in linguistically complex data. Several machine learning and deep learning methods are adopted and compared, Long Short-Term Memory, Bidirectional Encoder Representations from Transformers, and Support Vector Machines are some of them. In addition, the complexity in the detection of sarcasm is further compounded by the nuance of code-mixed language because the former does not have any tradition that can be adapted to traditional text classification methods. Feature extraction and the subsequent decision-making processes are also factored with broader implications for sentiment analysis. The results of the models analysed underwent a comparative analysis, which showed that the model produced out of the combination of BERT and Gated Recurrent Unit (GRU) surpasses other models to be used for this particular task with its detection accuracy rate, said to be the best in determining sarcasm. © 2024 IEEE.

关键词： Support vector machines

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Effective NSGA-II Algorithm for a Limited AGV Scheduling Problem in Matrix Manufacturing Workshops with Undirected Material Flow

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Complex System Modeling and Simulation 2025年第1期5卷 68-85页

作者： Xuewu Wang Jianing Zhang Yi Hua Rui Yu School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China Institute for Sustainable Manufacturing and also with the Department of Electrical and Computer EngineeringUniversity of KentuckyLexingtonKY 40506USA

Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and *** paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion *** address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is *** NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution *** dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two *** addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is *** local search operators based on ideal point are proposed to find a better local *** improve the global exploration ability of the algorithm,a dual population restart mechanism is *** tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.

关键词： limited automatic guided vehicle(AGV)scheduling problem nondominated sorting genetic algorithm II(NSGA-II) dual population co-evolution matrix manufacturing workshop

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Leveraging AI to Unlock the Potential of Data Science 15

Leveraging AI to Unlock the Potential of Data Science

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Yadav, Rakesh Kumar Sudhamsu, Gadug Goyal, Manish Kumar Yuvaraj, S. Deshpande, Leena A. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance. © 2024 IEEE.

关键词： Smart manufacturing

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COCOs Nucifera Tree Disease Prediction Using Faster Region-Convolutional Neural Network with ResNet-50 3

COCOs Nucifera Tree Disease Prediction Using Faster Region-C...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kavitha, M.G. Raviprakash, M.L. Alsalami, Zaid Nagendar, Y. Ramachandra, A.C. Kalpataru Institute Of Technology Department Of Computer Science And Engineering Tiptur India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Computer Science And Engineering Sr University Warangal India Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India

ISBN: (纸本)9798350318609

Coconut diseases were predicted based on various categories such as color, shape, time frame, and changes in growth. Deep learning (DL) was applied for this purpose. However, the image dataset used was unlabelled, leading to challenges such as overfitting and complexity in computational time for prediction. The proposed method is the Faster Region-Convolutional Neural Network (R-CNN) model for detecting and classifying coconut clusters, aiming to minimize overfitting in the identification of coconut nucifera diseases. The coconut disease dataset involved performing image classification, distinguishing between healthy and unhealthy coconuts. Normalization, feature extraction, and classification techniques were utilized for this task. For feature extraction, the VGG-16 model was involved, which includes maximum pooling layers to extract features at different levels of the network enhancing the quality of feature maps and providing better region identification. Classification was performed using the R-CNN with ResNet-50. The dataset contained images of both healthy and unhealthy coconuts, and the regions were predicted to identify and classify the different diseases within the images. ResNet-50 captures high-level features and helps mitigate the vanishing gradient problem during training, making it easier to connect with the R-CNN method. The proposed Faster R-CNN with ResNet-50 achieved a performance accuracy of 98.40%. This performance was compared to existing methods such as Convolutional Neural Network (CNN), Deep-CNN (DCNN) and Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Long short-term memory

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A Comprehensive Analysis of Fast-Food Classification Using You Only Look Once (YOLO) Model 15

A Comprehensive Analysis of Fast-Food Classification Using Y...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bandyopadhyay, Shaon Dutta, Dipankar Pratihar, Sanjoy Indian Institute of Information Technology Computer Science and Engineering Department West Bengal Kalyani KalyaniPIN 741235 India University Institute of Technology The University of Burdwan Computer Science and Engineering Department West Bengal BurdwanPIN 713104 India

ISBN: (纸本)9798350370249

In modern fast-paced society, the prevalence of fast food has spurred a surge in its consumption, emphasizing the need for accurate food identification systems. This study explores the efficacy of using the 'You Only Look Once' (YOLO) principles for classifying fast foods using the Food-101 dataset, which includes 1 0 1, 0 0 0 images across 101 food categories. Traditional machine learning (ML) methods, i.e., methods not using deep learning (DL), have struggled with the significant variation within classes and minimal distinction between classes characteristic of food images. In contrast, DL models, particularly convolutional neural networks (CNNs), have shown significant promise. This research leverages YOLO version 5 (YOLOv5), a cutting-edge object detection model, to achieve a classification accuracy of 8 0. 2 0 %. It outperforms other models such as Inceptionv3 and GoogLeNet. The study employs advanced techniques such as data augmentation, batch normalization, and sophisticated loss functions to optimize performance. The results underscore YOLOv5's superior capability in handling the complexities of fast-food classification. In the future, this can be useful for developing a robust solution for automated dietary monitoring and caloric intake assessment. © 2024 IEEE.

关键词： Convolutional Neural Networks Deep Learning Fast-Food Classification Food-101 Dataset YOLO

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Briefing of Textual Information using Text Rank 2

Briefing of Textual Information using Text Rank

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Bethel, G.N. Beena Divyasree, Baki Sandhya, Vangapandu Nimisha, K.A. Bodduluri, Anjani Sreemanth Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Department Hyderabad India

ISBN: (纸本)9781665491990

The world is very much advanced with the abundant growth of technology and the way we communicate is rapidly changing with it Lot of information is being generated day by day. It is important to extract the useful information from it Text Summarization is one of the solutions to it Different statistical methods like TF-IDF, TF are used for extracting the summary. These statistical methods focus mainly on most frequently occurred words and less on importance of sentences. There are many limitations of TF-IDF method like it is based on Mg of words model, so it does not capture position in text, semantics, co-occurrences in different documents. The other limitation is it assigns low values to words that are relatively important So, we proposed a system that uses a graph-based approach which extracts the significant sentences from the given text. The proposed system provides extractive summary, abstractive summary and keywords for single document. The keywords extracted from the summary helps in understanding the main idea of the document The proposed model helps users in providing summary with important points and saves user time. © 2023 IEEE.

关键词： Statistical methods

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