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

作者： Shivank Jain, Romil Reena, R. Kadam, Prema S. Das, Sunanda Singh, Swati Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

The cease-to-give up Congestion Avoidance Scheme for Underwater high pace verbal exchange Networks, commonly known as E2E-UCAN, is a critical technique for lowering plausible throughput by using efficiently warding off congestion inside a community. E2E-UCAN is a disbursed congestion control mechanism which uses statistics obtained from cease-to-end feedbacks to regulate sending prices on the give up systems. By means of monitoring the differences among expected network overall performance and actual performance, E2E-UCAN is able to pick out congestion stages inside the device, and is therefore able to regulate to prevent congestion before it happens. This enables to improve the stableness of the community and to increase the throughput of a network. Moreover, E2E-UCAN is likewise capable of efficaciously coping with queuing, avoiding packet loss in non-uniform networks, and in preferred, permitting applications to maximize performance. © 2023 IEEE.

关键词： Community Congestion Lowering Mechanism Performance

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Experimental investigation of different unconventional machined surface on hardox steel

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Materials Today: Proceedings 2022年 66卷 1162-1167页

作者： Shanmuganathan, A. Suresh, R. Manirathnam, D. Mohandas Gandhi, A.G. Rajasekar, P. Senthil, T.S. Ramaswamy, Subbiah Department of Mechanical Engineering NIMS University Rajasthan Jaipur India Department of Mechanical Engineering Annamalai University Tamil Nadu Chidambaram India Department of Mathematics Panimalar Engineering College Tamilnadu Chennai India Department of Mechanical Engineering RVS Technical Campus Tamilnadu Coimbatore India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamilnadu Chennai India Department of Mechanical Engineering Panimalar Engineering College Tamilnadu Chennai India Department of Mechanical Engineering Gokaraju Rangaraju Institute of Engineering and Technology Telangana Hyderabad India

Machinability and surface behaviours are playing an essential role in metal forming and manufacturing processes. Hardox 500 steel is machined by ultrasonic machining (USM) and spark erosion machining (SEM) processes. The different input factors are used in both the processes and evaluate the rate of metal removal. Current, voltage and pulse duration are the control factors for spark erosion. Power rating, slurry concentration and grit size are the control factor for USM. Scanning force microscopy (SFM) is used to investigate the surface topography of the hardox steel. The effect of input constrains on rate of metal removal (RMR) is validated by variance analysis. In SEM process, current was the most effective factor on rate of metal removal and it's provided the maximum contribution (79.75%) than other factors such as pulse duration (16.73%) and voltage (3.43%). In USM process, slurry concentration was the most effective factor on rate of metal removal and it's provided the maximum contribution (83.78%) than other factors. The contribution effect of grit size and power rating is 13.15% and 2.53% respectively. Based on surface plot analysis, the machined surface of SEM is better than USM surface. © 2022

关键词： Metals

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Assessing the Feasibility of Incorporating AI for Efficient data Access Strategies

Assessing the Feasibility of Incorporating AI for Efficient ...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Yadav, Dhyan Geetha, G. Elackiya, R. Kumar, Santosh D. Sharma, Ritu Jain, Romil Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350329773

The use of AI in statistics gets entry to techniques has recently emerged as a critical topic in the discipline of the information era. AI and gadgets gaining knowledge of strategies can enhance the performance and accuracy of facts to get entry to strategies. Also, they can offer extra blessings, including advanced scalability, privacy, and protection. This paper examines the feasibility of incorporating AI for efficient information entry techniques. An in-depth review of present studies inside the place of AI for statistics gets entry is supplied, highlighting the challenges and possibilities related to its use. The paper also examines the practical factors of incorporating AI for information get entry to techniques and assesses the price, scalability, and complexity of such procedures. We concluded that incorporating AI for records access techniques is viable and might benefit information customers significantly. However, similar studies are wanted to decide the most incredible layout and implementation of such techniques. © 2023 IEEE.

关键词： Scalability

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Empowering NGOs with Remote Sensing and CNN-LSTM Models for Social and Environmental Transformation

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Remote Sensing in Earth systems sciences 2024年第4期7卷 554-568页

作者： Ramachandran, Jayakarthik Gupta, Ashwani Kumar Syamala, Maganti Banerjee, Sudipta Baburao, D. Ranadive, Amol Deivasigamani, Shanmugavel Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India School of Business and Commerce Chandigarh University Unnao India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India Campus Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science and Engineering Vidya Jyothi Institute of Technology (VJIT) Telangana Hyderabad India BIMHRD Sri Balaji University Pune India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Tamilnadu Chennai India

Non-governmental organizations (NGOs) play a pivotal role in addressing some of the most pressing environmental and social challenges across the globe. With the rise of advanced technologies, NGOs have an unprecedented opportunity to enhance their impact through the adoption of innovative tools. Among these, remote sensing technology has emerged as a cornerstone in environmental monitoring, offering real-time data on land, water, and atmospheric conditions. The integration of remote sensing with CNN-LSTM models offers a transformative approach for NGOs to tackle environmental and social challenges. This study explores the efficacy of these technologies in monitoring deforestation and habitat loss. By leveraging high-resolution satellite imagery and ground truth data, our proposed CNN-LSTM model successfully identifies and tracks changes in land cover. Throughout various tests, the model demonstrated a high accuracy of 0.97, showcasing its potential in providing reliable, real-time data for environmental conservation efforts. The findings suggest that combining CNNs and LSTMs enhances the ability to interpret complex spatiotemporal data, thus supporting NGOs in their decision-making processes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： CNN-LSTM models Deforestation Environmental monitoring Habitat loss Non-governmental organizations (NGOs) Remote sensing Satellite imagery Spatial pattern recognition Temporal dynamics

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Strengthening Authentication: The Role of Multi-Level Hashing in OSPA Protocol 5

Strengthening Authentication: The Role of Multi-Level Hashin...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Hariharasudhan, S. Varghese, Vineetha Malarvizhi, K. Santhoshkumar, S.P. Nalini, T. Vikram, D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India Jct College of Engineering and Technology Department of Computer Science Al Business Systems Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence Data Science Coimbatore India

ISBN: (纸本)9798331518097

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture. © 2024 IEEE.

关键词： Authentication Protocol

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Automated Defect Detection in Electronic Components using Convolutional Neural Networks 9

Automated Defect Detection in Electronic Components using Co...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Shobanadevi, A. Kottu, Sreekanth Kumar, K. R. Senthil Amudha, K. Praveena, K. Venkatesh, R. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Chennai600026 India Mallareddy University Department of Computer Science & Engineering Telangana Hyderabad500043 India R.M.K. Engineering College Department of Mechanical Engineering Tamil Nadu Kavaraipettai601206 India Department of Science And Humanities-Physics Tamil Nadu Kavaraipettai601206 India Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Department of Electronics And Communication Engineering Andhra Pradesh 517102 India Department of Physics Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350365092

This exploration paper explores the operation of convolutional neural networks(CNNs) in automating the discovery of blights in electronic factors. With the rapid-fire advancement of technology, the demand for high- quality electronic factors has surged, emphasizing the need for effective disfigurement discovery styles. using the capabilities of CNNs, this study aims to develop a robust and accurate automated system able to relate colorful types of blights to electronic factors. By training the CNN model on a different dataset of disfigurement images, the system learns to fete subtle variations and anomalies reflective of faults, thereby enhancing examination delicacy and effectiveness. The proposed approach offers promising results in streamlining disfigurement discovery processes, reducing homemade intervention, and ameliorating overall quality control in electronics manufacturing. Results indicate the effectiveness and trustability of CNNs in automating disfigurement discovery, paving the way for enhanced productivity and product trustability in electronic diligence. © 2024 IEEE.

关键词： Quality control

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Predictive Analytics for Proactive Maintenance in Industrial IoT Applications 7

Predictive Analytics for Proactive Maintenance in Industrial...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Mabel Rani, A. Jaya Yasotha, B. Sevanthi, P. Karthika, K. Jeevanantham, Y.Alexander Pandi, V.Samuthira Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Thiruverkadu Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Tamilnadu Chennai India RMK Engineering College Kavarapettai Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chennai Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350340600

Industrial Internet of Things (IIoT) enabled predictive analytics have enabled the manufacturing sector to transition from reactive to proactive maintenance. This article discusses the use of predictive analytics in the IIoT field of preventive maintenance. The objectives consist of increased output, availability, and decreased malfunctions-research on preventive maintenance cast light on its evolution. The proposed work presents a framework for IIoT data acquisition, cleansing, feature selection, and analytical processing. Experiments and case studies validate the methodology. Utilizing predictive analytics for routine maintenance can improve the effectiveness and productivity of machinery. data integrity, model consistency, and scalability must be resolved before implementation. The proposed work's findings and recommendations have significant implications for future research into predictive analytics for proactive maintenance in IIoT systems and for industry practitioners. © 2023 IEEE.

关键词： Internet of things

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Disease Diagnosis system Using IoT Empowered with Fuzzy Inference system

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Computers, Materials & Continua 2022年第3期70卷 5305-5319页

作者： Talha Mahboob Alam Kamran Shaukat Adel Khelifi Wasim Ahmad Khan Hafiz Muhammad Ehtisham Raza Muhammad Idrees Suhuai Luo Ibrahim A.Hameed Department of Computer Science and Information Technology Virtual University of PakistanLahorePakistan School of Information and Physical Sciences The University of NewcastleAustralia Department of Computer Science and Information Technology Abu Dhabi UniversityAbu DhabiUnited Arab Emirates School of Computer Science National College of Business Administration&EconomicsLahorePakistan Sir Syed College of Computer Science University of Engineering and TechnologyLahorePakistan Department of Data Science University of the PunjabLahorePakistan Department of ICT and Natural Sciences Norwegian University of Science and TechnologyTrondheimNorway

Disease diagnosis is a challenging task due to a large number of associated *** in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated *** paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things(IoT)empowered by the fuzzy inference system(FIS)to diagnose various *** Fuzzy systemis one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties,and fuzzy logic is the best way to handle *** proposed system differentiates new cases provided symptoms of the ***,it becomes a time-sensitive task to discriminate symptomatic *** proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and *** coefficients have been employed to predict and compute the identified disease’s severity for each sign of *** study aims to differentiate and diagnose COVID-19,Typhoid,Malaria,and *** study used the FIS method to figure out the disease over the use of given data related to correlating with input *** tool is utilised for the implementation of *** procedure on the aforementioned given data presents that affectionate disease can derive from the *** results of our proposed method proved that FIS could be utilised for the diagnosis of other *** study may assist doctors,patients,medical practitioners,and other healthcare professionals in early diagnosis and better treat diseases.

关键词： Disease diagnosis system COVID-19 healthcare biomedical rules extraction

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Predicting Crops Based on Soil and Environmental Characteristics Using ARIMA and LSTM Model 4

Predicting Crops Based on Soil and Environmental Characteris...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Naresh Kumar, U. Madhana Mohan, K. Pragadeeswaran, S. Scaria, Jobin M. Baseera, A. Seenivasan, D. Annamacharya University Department of Artificial intelligence & Data science Rajampet India Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India School of Information Technology Ibs University Mt. Eriama Campus Port Moresby Papua New Guinea School of Computing Science and Engineering Vit Bhopal University Sehore India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (纸本)9798350352931

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. data preparation involves processing data before storing it machine-readable. SFFS and RFE select features. The model was trained using a hybrid ARIMA-LSTM technique, which blends deep learning and time-series forecasting. The ARIMA-LSTM model outperforms solo ARIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARIMA-LSTM can boost agricultural productivity while minimizing environmental impact. © 2024 IEEE.

关键词： crop productivity long short-term memory (lstm) soil management

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Machine Learning Development in Solving Critical Medical Problems 6

Machine Learning Development in Solving Critical Medical Pro...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Fatima, Shaista Sangeetha, G. Ponmurugan, P. Arularasan, A.N. Prabhu Chakkaravarthy, A. Denis, R. Chinnasamy, A. Department of Electronics and Communication Engineering MJ College Telangana Hyderabad500053 India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Puduvoyal Tamil Nadu Thiruvallur India Department of Research Meenakshi Academy of Higher Education and Research Tamil Nadu Chennai600078 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Poonamallee Tamil Nadu Chennai600123 India Department of Networking and Communications School of Computing College of Engineering and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science Mount Carmel College Autonomous Karnataka Bengaluru560052 India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur campus Tamil Nadu 603203 India

ISBN: (纸本)9798350398496

As a result of ML, the healthcare industry undergoes substantial innovation and improvement. As a result, data management, clinical operations, drug research, and surgery are all progressing more quickly. The healthcare sector is now required to use this cutting-edge technology because of the Covid-19 pandemic. More importantly, people stand to benefit the most from technology because it may improve their health outcomes by identifying the best treatment alternatives for them. Thanks to ML's enhanced early disease detection capabilities, the frequency of re-admissions to hospitals and clinics can be reduced. In this article, we'll look at the main applications of machine learning in healthcare as well as its exceptional benefits in changing the industry. © 2023 IEEE.

关键词： Machine learning

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