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

作者： Sheril, S. More, Priyanka D. Mishra, Rahul Das, Neeraj Ramachandran, T. Ahmed, Zahid Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350319125

This examines examines the cutting-edge state of security protocols for wi-fi sensor networks within the mobile communications area. We examine the performance of present protection protocols for wi-fi sensor networks that specialize in their effectiveness, strength-efficiency, and scalability. We then endorse an advanced protection protocol which can higher cope with the security challenges of wi-fi sensor networks. Our new protection protocol incorporates cryptographic algorithms tailored for the unique necessities of wi-fi sensor networks, as well as key trade mechanisms that offer more desirable scalability and energy-performance. We evaluate our proposed security protocol, and examine its tradeoffs in evaluation with existing security protocols. Our consequences display that the proposed protocol is extra comfortable, gives higher scalability, and achieves more electricity-efficiency. We also check out the impact of different routing protocols on the security of wireless sensor networks in cellular communications and analyze how they may be tormented by exclusive security protocols. Our findings advise that a hybrid of two current routing protocols provides suitable security for wireless sensor networks. © 2023 IEEE.

关键词： Networks Protocols Scalability Security Wireless

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When Hypergraph Meets Heterophily: New Benchmark Datasets and Baseline 39

When Hypergraph Meets Heterophily: New Benchmark Datasets an...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Ming Gu, Yongchun Wang, Yi Fang, Yujie Bai, Lu Zhuang, Xiaosheng Lio, Pietro Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China Zhejiang Institute of Optoelectronics China School of Computer Science and Technology Zhejiang Normal University China School of Artificial Intelligence Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University China Department of Mathematics City University of Hong Kong Hong Kong Department of Computer Science and Technology Cambridge University United Kingdom

ISBN: (纸本)157735897X

Hypergraph neural networks (HNNs) have shown promise in handling tasks characterized by high-order correlations, achieving notable success across various applications. However, there has been limited focus on heterophilic hypergraph learning (HHL), in contrast to the increasing attention given to graph neural networks designed for graphs exhibiting heterophily. This paper aims to pave the way for HHL by addressing key gaps from multiple perspectives: measurement, dataset diversity, and baseline model development. First, we introduce metrics to quantify heterophily in hypergraphs, providing a numerical basis for assessing the homophily/heterophily ratio. Second, we develop diverse benchmark datasets across various real-world scenarios, facilitating comprehensive evaluations of existing HNNs and advancing research in HHL. Additionally, as a novel baseline model, we propose HyperUFG, a framelet-based HNN integrating both low-pass and high-pass filters. Extensive experiments conducted on synthetic and benchmark datasets highlight the challenges current HNNs face with heterophilic hypergraphs, while showcasing that HyperUFG performs competitively and often outperforms many existing models in such scenarios. Overall, our study underscores the urgent need for further exploration and development in this emerging field, with the potential to inspire and guide future research in HHL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Systematic Literature Review: American Sign Language Translator

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Procedia computer science 2021年 179卷 541-549页

作者： Andra Ardiansyah Brandon Hitoyoshi Mario Halim Novita Hanafiah Aswin Wibisurya Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Mobile Application and Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Sign Language Recognition (SLR) is a relatively popular research area yet contrary to its popularity, the implementation of SLR in daily basis is rare; this is due to the complexity and various resources required. In this literature review, the authors have analyzed various techniques that can be used to implement an automated sign-language translator through the analysis of the methodologies and models used to make a working model of any sign-language translator from various sources. The purpose of this study is to explore various possible ways to implement Artificial Intelligence technology to improve the automated American Sign Language translator that is applicable. The authors have identified 22 different research papers within the period of the years 2015 - 2020. The analysis showed that every research studies picked have achieved respectable results, however, they are not perfect, since each research demonstrates its own unique strengths and weaknesses. There are some methods that might be suitable for our need to create an applicable Sign Language Translator, that is by using standard video camera for obtaining data, and either Convolutional Neural Network or Support Vector Machine can be used for the classification.

关键词： computer vision sign language recognition cnn hmm image recognition svm

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Smart Prediction of Water Quality System for Aquaculture using Machine Learning Algorithms

TechRxiv

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TechRxiv 2023年

作者： Sen, Sohom Maiti, Samaresh Manna, Sumanta Roy, Bibaswan Ghosh, Ankit Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India School of Nuclear Studies and Application Jadavpur University Kolkata India

Intelligent and Smart aquaculture is nowadays one of the most important latest development trends in the field of the aquaculture industry to overcome the problems of the farmers due to water quality monitoring, food feeding, temperature imbalance and recycling of water. Smart water quality prediction in the real-time environment using intelligent machine learning models and IoT sensors establish the basis for the evaluation, planning and intelligent regulation of the aquaculture environment. The purpose of this study is to propose an intelligent Machine learning and IoT-based Biofloc system that enhance the efficiency, production, water recycling system and automatic food feeding system. This paper proposed a system that collects process data from sensors, store data in the cloud, and analyses it using different latest machine learning models such as a Decision tree classification model, and Random Forest Model to predict the water quality and provides real-time monitoring through an android app. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed linear regression, Random Forest and other ML models have been implemented and tested to validate and achieve a satisfactory result. A performance comparative analysis of the different ML algorithms has been conducted based on a few metrics such as accuracy, recall, precision and F1-score. Random Forest Classifier has surpassed all the other models in terms of accuracy, recall, precision and F1 score. Random Forest Classifier has exhibited an accuracy of 73.76%, recall, precision and F1-score of 90%, 75% and 82% respectively. © 2023, CC BY.

关键词： Aquaculture

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Blockchain-Enabled Smart Contracts: An Evolutionary Algorithm Approach for Automated Financial Agreement 15

Blockchain-Enabled Smart Contracts: An Evolutionary Algorith...

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

作者： Sekhar, J Chandra Prasad, Arun B. Kanade, Shantanu Kanade, Anuradha Dhilipan, C. Bala, B Kiran Nri Institute of Technology Guntur India Institute of Law Nirma University Ahmedabad India Symbiosis International University Symbiosis School for online and Digital Learning Maharashtra Pune India Mit World Peace University School of Computer Science and Application Maharashtra Pune India Jain Deemed to be University Cms Business School Bangalore India College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350370249

Blockchain technology has revolutionized various industries by introducing decentralized and secure systems. Smart contracts, self-executing contracts with the terms of the agreement directly written into code, represent a significant advancement in automating and enhancing the efficiency of financial agreements. This paper proposes an evolutionary algorithm approach to further optimize the implementation of smart contracts on the blockchain for automated financial agreements. The evolutionary algorithm, inspired by the principles of natural selection and genetic algorithms, aims to enhance the adaptability and performance of smart contracts over time. Through a continuous process of selection, crossover, and mutation, the algorithm evolves the codebase of smart contracts to better suit the dynamic and evolving nature of financial markets. This approach not only improves the efficiency of automated financial agreements but also enables smart contracts to adapt to changing market conditions, regulatory requirements, and user preferences. Furthermore, the security implications and challenges associated with the evolutionary algorithm are examined, along with potential strategies for mitigating risks. In conclusion, the integration of evolutionary algorithms with blockchain-enabled smart contracts represents a promising avenue for advancing the automation of financial agreements. This approach not only addresses the challenges of adaptability and optimization but also contributes to the ongoing evolution of blockchain technology in the realm of decentralized finance. The findings of this research have implications for financial institutions, regulators, and developers seeking to leverage the full potential of smart contracts in an ever-changing financial landscape. © 2024 IEEE.

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Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

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computers, Materials & Continua 2021年第12期69卷 4151-4168页

作者： Vinay Arora Karun Verma Rohan Singh Leekha Kyungroul Lee Chang Choi Takshi Gupta Kashish Bhatia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaPunjabIndia Associate Application ITConcentrixGurugramHaryanaIndia School of Computer Software Daegu Catholic UniversityGyeongsanKorea Department of Computer Engineering Gachon UniversitySeongnam13120Korea Information Security Engineering Soonchunhyang UniversityKorea Department of Computer Engineering University College of EngineeringPunjabi UniversityPatialaPunjabIndia

The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension,irregular cardiac functioning,and heart ***-based learning of heart sound is an efficient technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac ***(PCG)and electrocardiogram(ECG)waveforms provide the much-needed information for the diagnosis of these *** this work,the researchers have converted the heart sound signal into its corresponding repeating pattern-based *** 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform *** existing models,***,Xception,Visual Geometry Group(VGG16),ResNet,DenseNet,and InceptionV3 of Transfer Learning have been used for classifying the heart sound signals as normal and *** PhysioNet 2016,DenseNet has outperformed its peer models with an accuracy of 89.04 percent,whereas for PASCAL 2011,VGG has outperformed its peer approaches with an accuracy of 92.96 percent.

关键词： PCG signals transfer learning repeating pattern-based spectrogram biomedical signals internet of things(IoT)

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Optimizing Resource Efficiency in Smart Greenhouses Through IoT

Optimizing Resource Efficiency in Smart Greenhouses Through ...

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Emerging technology (INCET), International Conference for

作者： Subhra Debdas Shikhar Pandey Shivam Gupta Ranjan Kumar Dash Manik Chand Patnaik Anusthan Ajay Singh School of Electrical Engineering KIIT Deemed to Be University Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Computer Science and Application Odisha University of Technology and Research Bhubaneswar India Department of CSE Roland Institute of Technology Brahmapur India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In the pursuit of sustainable agriculture, smart greenhouses have emerged as a vital solution, enhancing crop productivity while assuring resource conservation. This research delves into the optimization of resource efficiency in smart greenhouses by leveraging the Internet of Things (IoT). Through a comprehensive review of current technologies and methods, the study identifies key challenges and opportunities within the domain. A novel framework is proposed, integrating IoT sensors and actuators to monitor and control the microclimate, ensuring optimal growth conditions while minimizing resource usage. Data from sensor networks are analyzed to predict resource needs, enabling precision farming practices. The framework emphasizes the use of Machine Learning algorithms to enable predictive analytics for resource allocation, ensuring that water, nutrients, and energy are distributed more efficiently. The potential of Lightweight Resource Allocation (LaRA) algorithms is also explored to maintain the delicate balance between maximizing yield and minimizing environmental impact. Ultimately, this research highlights the transformational potential of IoT in creating green, efficient, and productive agricultural practices within smart greenhouses.

关键词： Temperature measurement Temperature sensors Machine learning algorithms Green products Greenhouses Internet of Things Resource management

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An Analysis of Cyber security Risks and Authentication Systems 10

An Analysis of Cyber security Risks and Authentication Syste...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sampath, P. Shukla, Vijay Kumar, Santosh Gehlot, Anita Samatha, V. Kumar, T. Sampath School of Computing Easa College of Engineering and Technology Coimbatore641105 India Institute of Technology Department of Cse Greater Noida India Lucknow Public College of Professional Studies Department of Computer Science Vinamra Khand Gomti Nagar Uttar Pradesh Lucknow226010 India Uttaranchal Institute of Technology Uttaranchal Univeristy India Dkw Govt College for Women Computer Application Nellore524003 India Sr University Dept of CSandAI Telangana India

ISBN: (纸本)9789380544472

This paper presents an analysis of cyber security risks and authentication systems. It provides an overview of the most commonly used authentication systems, their pros and cons, and the potential security risks associated with each one. It then discusses the importance of implementing an effective authentication system, the challenges involved in doing so, and the best practices to ensure a secure system. Finally, the paper concludes with recommendations for organizations to consider when implementing authentication systems. The paper provides an in-depth look at cyber security risks and authentication systems in order to help organizations make informed decisions on how to protect their data. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Authentication

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A Multi-criteria Decision-Making Approach to Analyze Python Code Smells 1

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International Conference on Advances in Electrical and computer Technologies, ICAECT 2021

作者： Gupta, Aakanshi Sharma, Deepanshu Phulli, Kritika Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Executive Branch-IT Indian Navy New Delhi India Application Development Associate Accenture India

ISBN: (数字)9789811911118

ISBN: (纸本)9789811911101

Frequent modifications in the software leads to the issues in the code which deteriorates software quality. The suitable approach to rectify it is to refactor it. Consequently, this research devises a ranking using the MCDM approach for prioritizing Python-based code smells. This order ranking has been studied over 10,552 classes for five generic code smells diffused in 20 Python software. The software metrics extracted over these python classes have been considered as multiple criteria which are processed using rule-based machine learning approach. In this study, an approach providing compromised solution—VIKOR have been practiced along with TOPSIS and WASPAS methods. Additionally, the MCDM approaches have utilized Shannon’s Entropy and CRITIC method as weight estimation techniques. The extracted vital software metrics achieved over 90% accuracy. The prominent outcome portrays that the most dominant smell is Collapsible "IF" which needs to be refactored at the early stage. This should be accompanied by Cognitive Complexity smell so as to attain sustainable development environment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

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Hybrid Transformer-CNN Model for Automated Diagnosis of Pomegranate Fruit Diseases

Hybrid Transformer-CNN Model for Automated Diagnosis of Pome...

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International Conference on Electrical Energy Systems (ICEES)

作者： Raj Gaurang Tiwari Alok Misra Himani Maheshwari Ambuj Kumar Agarwal Manish Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara Punjab India School of Computing Graphic Era Hill University Dehradun Uttrakhand India Department of Computer Science and Engineering Sharda University Sharda School of Engineering and Technology India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

Improving crop quality and output relies on early and precise disease diagnosis in pomegranate fruits. This study presents a new Feature Fusion Network for disease classification in pomegranate fruits, which combines Transformer and CNN modules. The proposed model classifies pomegranate fruit into five categories that are Healthy, Bacterial Blight, Anthracnose, Cercospora Fruit Spot, and Alternaria Fruit Spot. While the CNN module is great at collecting local visual aspects, the Transformer module is used to gather global contextual information. The model uses both global and local information for robust disease categorization by combining two complimentary feature representations. Adam, Adadelta, Gradient Descent, and RMSProp optimization algorithms are used to fine-tune the suggested Feature Fusion Network. After extensive training with Adam Optimizer, the suggested method attained a maximum accuracy of 96.45% in disease classification of pomegranate fruits. In addition, when the performance of the proposed model is compared with other popular deep learning architectures like ResNet, Inception, VGG, and mobileNet, it outperforms.

关键词： Training Visualization Accuracy Microorganisms Sensitivity and specificity Transformers Multitasking Feature extraction Optimization Diseases

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