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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sripriya, T. Borade, Monali R. Deepaware, Neeta Kumar, Jambi Ratna Raja Khalid, Md. Saifullah Singh, K. Ranjith Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Management Jabalpur India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350319125

The Encapsulating Security Payload (ESP) is the primary issue of safety structure for wireless networks. It inspires to make specific cozy switches of records across a wireless network by offering encrypted messages, information integrity guarantee, and authentication of communications. ESP can also provide excellent selective filtering of community site visitors and provides levels of authentication required for ease of network access. Due to the advances in the wireless era, the overall performance of ESP in Wi-Fi networks is becoming increasingly essential. Consequently, intelligent optimization and estimating the overall performance of ESP in wireless networks is an important task. Clever optimization of ESP in Wi-Fi networks determines the most reliable parameters for ESP to maximize its performance and reduce the network overhead that may arise while looking to take care of records transmission and information protection. Traditional optimization techniques, including Genetic Algorithms, can be used for this project, as they give more accurate outcomes and might effortlessly be implemented in the community infrastructure. Clever optimization can't only decorate the performance of ESP but can also reduce the variety of false alarms in the machine. Moreover, developing an accurate estimation for the overall performance of ESP in Wi-Fi networks is essential to get the most out of its talents. The usage of mathematical models, including a Markov Chain modeling technique, to symbolize various. © 2023 IEEE.

关键词： authentication encapsulating optimization

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Real-Time Text Classification Algorithm Based On Combined Machine Learning Models 2

Real-Time Text Classification Algorithm Based On Combined Ma...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Ravivarman, G. Mahajan, Sulabh Sowmiya, S. Goyal, Shitij Gupta, Rahul Wanjale, Kirti H. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350388916

Web text categorization is a prominent area of research and a vital technology in the fields of Web information retrieval and data mining. A great deal of attention and quick progress in this field has occurred in recent years. One sort of deep learning model that has been more useful for extracting important characteristics from textual input while reducing model complexity is the convolutional neural network (CNN). In the meanwhile, a time-tested machine learning method known as support vector machine (SVM) has a stellar reputation for reliability and accuracy. By merging an improved CNN with SVM, this research suggests a new method for Web text categorization. Text characteristics are extracted using the CNN model, which has a five-layer network structure. These features are then used for classification as well as forecasting using SVM. This method presents a new approach to Web text categorization using enhanced models by combining the capabilities of convolutional neural networks (CNNs) and support vector machines (SVMs). Applying this method to datasets with mixed text yields outstanding results, all things considered. © 2024 IEEE.

关键词： Adversarial machine learning

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Graph Contrastive Learning with Cohesive Subgraph Awareness 24

Graph Contrastive Learning with Cohesive Subgraph Awareness

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33rd ACM Web Conference, WWW 2024

作者： Wu, Yucheng Wang, Leye Han, Xiao Ye, Han-Jia Key Lab of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University Nanjing China

ISBN: (纸本)9798400701719

Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy for learning representations of diverse graphs including social and biomedical networks. GCL widely uses stochastic graph topology augmentation, such as uniform node dropping, to generate augmented graphs. However, such stochastic augmentations may severely damage the intrinsic properties of a graph and deteriorate the following representation learning process. We argue that incorporating an awareness of cohesive subgraphs during the graph augmentation and learning processes has the potential to enhance GCL performance. To this end, we propose a novel unified framework called CTAug, to seamlessly integrate cohesion awareness into various existing GCL mechanisms. In particular, CTAug comprises two specialized modules: topology augmentation enhancement and graph learning enhancement. The former module generates augmented graphs that carefully preserve cohesion properties, while the latter module bolsters the graph encoder's ability to discern subgraph patterns. Theoretical analysis shows that CTAug can strictly improve existing GCL mechanisms. Empirical experiments verify that CTAug can achieve state-of-the-art performance for graph representation learning, especially for graphs with high degrees. The code is available at https://***/10.5281/zenodo.10594093, or https://***/wuyucheng2002/CTAug. © 2024 ACM.

关键词： Stochastic systems

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Improving Video Search and Retrieval Through Semantic Annotation 15

Improving Video Search and Retrieval Through Semantic Annota...

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

作者： Balaji, N.V. Katyal, Ashu Jaisudha, J. Parmar, Yuvraj Asha, S. More, Priyanka D. Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - UG Management Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper affords a singular approach for enhancing video search and retrieval through the usage of semantic annotation. The proposed method includes levels of semantic annotation and semantic search and retrieval. Within the first level, semantic descriptors are mechanically generated from low-degree features of the video. Inside the 2 nd, those semantic descriptors are used to locate relevant films efficiently. Experiments are performed to evaluate the accuracy and performance of the proposed machine. Consequences indicate that the proposed gadget extensively outperforms existing techniques for video seek and retrieval, demonstrating the application of semantic annotation. © 2024 IEEE.

关键词： Annotation Demonstrating Experiments Features Outperforms Retrieval Semantic Singular

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Performance Evaluation and Comparison of Clustering Algorithms for Social Network Dataset 6

Performance Evaluation and Comparison of Clustering Algorith...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ramakrishna Kumar, M.V.N.M. Sharma, Vibhoo Gupta, Keshav Jain, Abhishek Priya, Bhanu Prasad, M.S.R. College of Engineering Department of Electrical and Communication National University of Science and Technology Muscat Oman Hindustan College of Science & Technology Department of I. T. Uttar Pradesh Mathura India GRD Institute to Technology and Management Uttara Khand Dehradun India Uttaranchal University Department of C.S.E. Uttara Khand Dehradun India Teerthanker Mahaveer University Department of Computer Science & Engineering U.P. Moradabad India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350304480

Evaluation of the effectiveness of various clustering methods using an enhanced K-Mean algorithm is shown in this paper. Essential data mining techniques, such as clustering techniques, divide things into proper subgroups. Data can be described and analyzed using clustering. Compared with K-Means, Hierarchical, and E.M. Algorithms in this study is the Improved K-Means Clustering Algorithm. The algorithms are used to compare the performance of the clusters based on how long it takes to assemble them. The enhanced K-Mean Algorithm performed much better results than traditional algorithms. Additionally, the Confusion Matrix is revealed. © 2023 IEEE.

关键词： Clustering Data Mining EM algorithm Hierarchical K-Mean

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Analyzing the Efficiency of Cross-Layer Design for Location-Aware Ad Hoc Networks 15

Analyzing the Efficiency of Cross-Layer Design for Location-...

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

作者： Srividhya, S. Jain, Romil Preetham, K. Meshram, Vishal A. Yuvaraj, K. Singh, Manpreet Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement-UG Management Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140417 India

ISBN: (纸本)9798350370249

This paper affords an evaluation of the performance of pass-layer design for region-aware advert hoc networks in phrases of mobility, node density, distributed monitoring, and network performance. First, a theoretical analysis of the longitudinal mobility of a cell ad hoc community is performed, and the effects of this analysis are confirmed through simulations. Then, the influence of node density on connectivity metrics is discussed, and it's far observed that a boom in density can enhance global node connectivity. Similarly, distributed tracking of the network is evaluated with the aid of introducing the area your neighbor (LYN) metric, and the outcomes are in comparison with the prevailing distributed tracking procedures. Sooner or later, overall performance signs for advert hoc networks are examined in phrases of put off, throughput, and packet transport ratio, and the outcomes of the go-layer design are located to be higher than the conventional layered layout. The overall outcomes highlight the potential of pass-layer design for enhancing the performance of place-aware advert hoc networks. © 2024 IEEE.

关键词： Ad hoc networks

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Artificial Intelligence based Smart Cosmetics Suggestion System based on Skin Condition 1

Artificial Intelligence based Smart Cosmetics Suggestion Sys...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Kavyashree, N. Rama Satish, K.V. Rasal, Prasanna Rajaram Jadhav, Rahul Jalindar Saidireddy, M. Kharade, K.G. Department of MCA Dr.Ambedkar Institute of Technology Bangalore India Department of AIML RNS Institute of Technology Bengaluru India Yashwantrao Mohite Institute of Management Karad India Department of CSE Computer Science and Engineering Koneru Lakshmaiah Education Foundation India Department of Computer Science Shivaji University Maharashta Kolhapur India

ISBN: (纸本)9781665460842

Today, cosmetics have a big impact on how people look. Customers can access a range of products and conduct e-commerce on the website. Choosing the best product for our skin might be challenging. It offers a prediction approach so that individuals can select the goods that are suitable for their skin type. Configuration concepts perform better than conventional methods. It uses deep learning technology to streamline the complex procedures in the IT industry for cosmetics and beauty care. Along with its clients and product offerings, the beauty sector has grown over time. With the expansion of products and consumers, selecting the right cosmetics becomes more and more important. Due to the substantial influence that cosmetics have on a person's appearance, the client should choose the best cosmetics for themselves based on personal factors (skin type). Given that every person has a distinct type of skin, choosing cosmetics that suit the person may be difficult. The composition depends on whether the client has oily, dry, or neutral skin. Even the product the user is looking for can have an extremely difficult skin condition;AI algorithms are perfectly suited to address this problem since they can evaluate massive amounts of unstructured data and generate insightful results. © 2022 IEEE

关键词： Cosmetics

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Validity-Preserving Delta Debugging via Generator Trace Reduction

arXiv

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arXiv 2024年

作者： Ren, Luyao Zhang, Xing Hua, Ziyue Jiang, Yanyan He, Xiao Xiong, Yingfei Xie, Tao Key Laboratory of High Confidence Software Technologies Ministry of Education School of Computer Science Peking University Beijing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computer and Communication Engineering University of Science and Technology Beijing China

Reducing test inputs that trigger bugs is crucial for efficient debugging. Delta debugging is the most popular approach for this purpose. When test inputs need to conform to certain specifications, existing delta debugging practice encounters a validity problem: it blindly applies reduction rules, producing a large number of invalid test inputs that do not satisfy the required specifications. This overall diminishing effectiveness and efficiency becomes even more pronounced when the specifications extend beyond syntactical structures. Our key insight is that we should leverage input generators, which are aware of these specifications, to generate valid reduced inputs, rather than straightforwardly performing reduction on test inputs. In this paper, we propose a generator-based delta debugging method, namely GReduce, which derives validity-preserving reducers. Specifically, given a generator and its execution, demonstrating how the bug-inducing test input is generated, GReduce searches for other executions on the generator that yield reduced, valid test inputs. The evaluation results on five benchmarks (i.e., graphs, DL models, JavaScript programs, SymPy, and algebraic data types) show that GReduce substantially outperforms state-of-the-art syntax-based reducers including Perses and T-PDD, and also outperforms QuickCheck, SmartCheck, as well as the state-of-the-art choice-sequence-based reducer Hypothesis, demonstrating the effectiveness, efficiency, and versatility of GReduce. Copyright © 2024, The Authors. All rights reserved.

关键词： Software testing

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Design of an Efficient Approach for Performance Enhancement of COVID-19 Detection Using Auxiliary GoogLeNet by Using Chest CT Scan Images 4

Design of an Efficient Approach for Performance Enhancement ...

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4th IEEE Bombay Section Signature Conference, IBSSC 2022

作者： More, Pranav Ratre, Sushila Ligade, Sunil Bhise, Rajesh School of Technology Management & Engineering SVKM's Nmims University Navi Mumbai Navi Mumbai India Bharati Vidyapeeth Deemed to Be University Computer Science & Engineering Department of Engineering and Technology Navi Mumbai India Bharath Institute of Higher Education and Research Computer Science Engineering Chennai India Amity University Computer Science & Engineering Maharashtra Navi Mumbai India

ISBN: (纸本)9781665492911

In every country on this planet, COVID-19 disease s right now one of the most unsafe issues. The expedient and precise space of the Covid virus infection s major to see and take better treatment for the infected patients will increase the chance of saving their lives. The quick spread of the Covid virus has blended complete interest and caused greater than 10 lacks cases to date. To battle this spread, Chest CTs arise as a basic demonstrative contraption for the clinical association of COVID-19 related to a lung illness. A modified confirmation device is essential for assisting in the screening for COVID-19 pneumonia by making use of chest CT imaging. The COVID-19 illness detection utilizing supplementary GoogLeNet is shown in this study. Deep Convolutional Neural Networks were built by researchers at Google, and one of their innovations was the Inception Network. GoogLeNet is a 22-layer deep convolutional neural network that is a variation of the inception Network. GoogLeNet is utilized for a variety of additional computer vision applications nowadays, including face identification and recognition, adversarial training, and so on. The findings indicate that the GoogLeNet method is superior to the CNN Method in terms of its ability to detect COVID-19 sickness. © 2022 IEEE.

关键词： COVID-19

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IoT Based Mobile App for Continuous Health Monitoring of the Person 4

IoT Based Mobile App for Continuous Health Monitoring of the...

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4th International Conference on Emerging Research in Electronics, computer science and Technology, ICERECT 2022

作者： Priyadarsini, Indira Tejaswini, B. Kumar, Ashok Manochitra, I.S. Chakrapani, I.S. Alaskar, Kamal SKR Government College Department of Botany Nagari India East Point College of Engineering and Technology Department of Information Science and Engineering Bengaluru India Banasthali Vidyapith Department of Computer Science Rajasthan India Kalasalingam Academy of Research & Education Department of Computer Science & Information Technology Srivilliputhur India PRR&VS Government College Department of Zoology Nellore India Bharati Vidyapeeth Institute of Management Department of Computer Application Kolhapur India

ISBN: (纸本)9781665456357

In the sphere of medicine, IOT is meant to keep people safe and healthy plays a crucial part in communicating with doctors and patients through the use of health monitoring equipment and lowering healthcare costs in the future years. The internet of things (IoT) is making the world a smarter and more efficient village by allowing a variety of sensors and smart gadgets to gather and analyse data for a variety of reasons. As a result of these smart things, the healthcare system is growing wiser. When basic health facilities lack comprehensive medical care infrastructure, emerging countries gain. However, there is currently no specialized architecture for smart health units that can allow for this gathering and transferring patient health information to headquarters hospitals where live patient assistance is offered. Here, a smart IoT -based healthcare system is proposed, which includes a smart medical kit linked to sensors and a server for frequent health tracking. This smart medical kit is associated with sensors to measure the health parameters like body temperature, blood pressure, and heart rate for the effective function of the body. The proposed idea can alert the patient and their relatives in case of any abnormalities in their health parameters and also get suggestions from the doctor without physical contact with the doctor. © 2022 IEEE.

关键词： Blood pressure

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