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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Visumathi, J. Gurusubramani, S. Mouleeswaran, S.K. Sammeta, Naresh Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Bangalore India Rmk College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9781665462167

Mobile Wireless Sensor Network (MWSN) allows rapid and transitory connections among mobile sensor nodes lacking the help of structure. due to the rapid growth of MWSN, traffic necessitates a severe and unbalanced load. For this reason, an efficient routing approach is necessary to build the load balance. Multi-Path (MP) balances the routing load and minimizes the network delay. This approach presents Enhancing Reliability in multi-path (ERMP) in the MWSN. This approach's main objective is to reduce network congestion and diminish packet losses in the network. This approach balances the network load by evaluating node capacity by queue length and congestion window size. The node capacity cannot access the highest load at a time using Multi-path routing. Thus, equates to the routing load in the MWSN. In addition, the Genetic Algorithm (GA) selects the efficient relay node for transmitting the data efficiently. The simulation results demonstrate that the ERMP approach minimizes the routing load and increases the network throughput in the MWSN. Furthermore, the proposed approach reduces the delay and packet loss rate in the MWSN. © 2023 IEEE.

关键词： Genetic algorithms

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Energy-Efficient Transmission in Wireless Networks Using Resnet with Gravitational Search Algorithm 2

Energy-Efficient Transmission in Wireless Networks Using Res...

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2nd International Conference on disruptive Technologies, ICdT 2024

作者： Rajakumari, S. Hc, Kantharaju Ahmad, Shadab Sharma, Rupa Rani Sudha, S. Ramesh, T. R.M.D. Engineering College Department of Mathematics India Vemana Institute of Technology Department of Ai & Ml Karnataka Bengaluru India Samara University Department of Electrical and Computer Engineering Afar region Ethiopia G.L Bajaj Institute of Technology and Management Department of Applied Science Uttar Pradesh Greater Noida India Sri Ranganathar Institute of Engineering and Technology Department of Ece Tamilnadu Coimbatore India R.M.K Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350371055

Although wireless networks are extremely important in modern communication, one of the most significant challenges they face is the quantity of energy that they use. Within this study, a new way of sending data over wireless networks is suggested. The gravitational search algorithm (GSA) and residue networks of the ResNet type are used together in this method. By using wireless networks, the main goal of this method is to find answers to problems that come up with using energy more efficiently. The creation and use of a hybrid ResNet-GSA model for wireless communication is one of the most important parts of our method. Because it follows the rules of gravity, GSA can quickly look through the solution space to find the right parameter values. At the same time, the network works better when the deep learning features of ResNet are used. The results of our research show that there are big improvements in how much energy is used compared to using traditional ways. If you want to cut down on the amount of energy used, the ResNet-GSA strategy that was suggested also works well. The findings demonstrate that optimisation techniques and deep learning methodologies are not only compatible with one another but also perform effectively together to make wireless networks more environmentally friendly. © 2024 IEEE.

关键词： Energy Efficiency Gravitational Search Algorithm Residual Networks Transmission Optimization

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Machine learning Algorithms used in Crop Yield Prediction for Precision Agriculture 4

Machine Learning Algorithms used in Crop Yield Prediction fo...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Umamaheswari, R. Shanmugapriya, S. Vimala, P. Ganesan, R. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr.Rangarajan Dr. Sagunthala Engineering College Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331515720

Agriculture is a crucial sector that plays a vital role in producing food for human consumption. However, predicting crop yield accurately can be a challenging task due to various natural factors, such as soil quality, climate, rainfall, and resource availability. In the past, farmers relied on their expertise and judgment to determine crop yield, but their traditional methods have become less effective due to the unpredictable nature of these factors. To address this challenge, researchers have developed various algorithms and models for predicting crop yield based on different parameters, such as soil quality, climate, and image analysis. These algorithms can help farmers make informed decisions about when and where to grow their crops, leading to improved productivity and higher yields. Among these algorithms, neural networks are showing great promise as they can analyze vast amounts of data and identify patterns that may not be immediately apparent to human observers. By training neural networks on historical crop yield data, researchers can develop models that accurately predict crop yield based on a variety of factors. Ensuring accurate crop yield prediction is critical to ensuring food security and preventing famine, particularly in developing countries such as India. As technology continues to advance, we can expect even more advanced algorithms and models for predicting crop yield that will help farmers make better decisions and ultimately lead to increased productivity and better outcomes for agriculture. © 2024 IEEE.

关键词： Agriculture Crop yield prediction (CYP) Machine learning Neural Network (NN)

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Advancing Personalized Recommendation Systems with a Groundbreaking Collaborative Filtering Algorithm driven by Machine learning 2

Advancing Personalized Recommendation Systems with a Groundb...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sriram, K.P. Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattu Siruseri Chennai India Assistant Professor Information Technology St.Joseph's Institute of Technology Chennai India

ISBN: (纸本)9798350383867

By introducing this collaborative filtering algorithm, which is dependent on machine learning that can be used in enhancing the user-based recommendation systems, this paper is trying to achieve more advanced personalized recommendation systems. Utilizing recent innovative designs of networks together with relevant contextual information, the algorithm provides high accuracy recommendation, scalability, and aids in improving user experience than traditional approaches. The model is shown to perform exceedingly well in all respects by its being subjected to a thorough assessment and compared with the baseline and enhanced models. It joins ranks with the best in all the indices of performance like precision, recall, F1-score, mean average precision and user satisfaction. In addition, hyperparameter tuning, and scalability metrics, so ending efficiency for model configuring and deployment recommendation models which could deal with large-scale datasets and offer users timely suggestions are of vital importance. To summarize, these results highlight the disruptive character of the latest machine learning algorithms that might create a new reality of recommendation systems with the trend of the time of individualization of relationships in the digital world. ©2024 IEEE.

关键词： Collaborative filtering

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Assessing Secure Cloud Information Sharing Through Authentication and Encoded Indexing 8

Assessing Secure Cloud Information Sharing Through Authentic...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Pandiaraja, P. Karthik, K. Rajesh Kanna, P. Ushapriya, C. Mahesh, P.C.Senthil Muthumanickam, K. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600 062 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Tamilnadu Thalavapalayam639113 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Erode638401 India St. Martin's Engineering College Department of Ai & Ml Telangana Secunderabad500100 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India

ISBN: (纸本)9798350376425

The high usage of storage in cloud, ensuring the privacy and security of information stored in the cloud has come up with a difficult concern. One approach to address this issue is the use of techniques which are encrypted, where data is already encrypted being uploaded to the storage of cloud. However, traditional encryption methods hinder the ability to find and retrieve particular information from the secured information, as the information needs to be decrypted first. This limitation hampers the usability and efficiency of storage of cloud systems. To overcome the challenge, this research says a novel approach for keyword search on secured information in storage of cloud using an AES approach. The algorithm of AES is a maximum adopted symmetric encryption method familiar for efficiency and robustness. By leveraging the AES encryption approach, the solution assures the confidentiality of information stored while enabling efficient keyword-based search operations without the need for decryption. The proposed approach utilizes a combination of cryptographic techniques such as AES encryption and index structures to access efficient information findings on the secured information. A secure index is created during encryption process that enables for potent retrieval of encrypted data related to specific keywords. The encrypted data remains confidential, as only authorized users possess the necessary decryption keys to retrieve the plaintext information. during data outsourcing, data owner can set the access permission based on their user identity. Users should have specific identity with decryption key for accessing data from cloud. Here data manager can also provide particular period for accessing information from cloud. The results demonstrate that the keyword findings on secured information in storage of using the AES approach achieve a balance between data privacy and search functionality. The solution offers an effective means to securely store and retrieve data

关键词： Cloud storage

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A comparative analysis of paddy crop biotic stress classification using pre-trained deep neural networks

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Artificial Intelligence in Agriculture 2022年第1期6卷 167-175页

作者： Naveen N.Malvade Rajesh Yakkundimath Girish Saunshi Mahantesh C.Elemmi Parashuram Baraki Department of Information Science and Engineering Smt.Kamala and Sri.Venkappa M.Agadi College of Engineering and TechnologyLakshmeshwar582116KarnatakaIndia Department of Computer Science and Engineering K.L.E.Institute of TechnologyHubballi580030KarnatakaIndia Department of Computer Science and Engineering Navkis College of EngineeringHassan573217KarnatakaIndia Department of Computer Science and Engineering Smt.Kamala and Sri.Venkappa M.Agadi College of Engineering and TechnologyLakshmeshwar582116KarnatakaIndia Visvesvaraya Technological University Belagavi 590018KarnatakaIndia

The agriculture sector is no exception to the widespread usage of deep learning tools and *** this paper,an automated detection method on the basis of pre-trained Convolutional Neural Network(CNN)models is proposed to identify and classify paddy crop biotic stresses from the field *** proposed work also provides the empirical comparison among the leading CNN models with transfer learning from the ImageNet weights namely,Inception-V3,VGG-16,ResNet-50,denseNet-121 and *** spot,hispa,and leaf blast,three of the most common and destructive paddy crop biotic stresses that occur during the flowering and ripening growth stages are considered for the *** experimental results reveal that the ResNet-50 model achieves the highest average paddy crop stress classification accuracy of 92.61%outperforming the other considered CNN *** study explores the feasibility of CNN models for the paddy crop stress identification as well as the applicability of automated methods to non-experts.

关键词： Paddy crop Stress classification Biotic stress PlantVillage ImageNet Pre-trained CNN models

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Smart Contract-Enabled Blockchain Framework for Electronic Medical Health Records 14

Smart Contract-Enabled Blockchain Framework for Electronic M...

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14th International Conference on Cloud Computing, data Science and engineering, Confluence 2024

作者： Mishra, Amit Kumar Sharma, Rahul Singh, Jagendra Singh, Prabhishek diwakar, Manoj Tiwari, Mohit Department of Computer Science & Engineering Graphic Era Hill University Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Dehradun India Bharati Vidyapeeth's College of Engineering Dept. of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350344837

This article presents a distinctive framework that utilises blockchain technology to enable smart contracts, revolutionising the administration of electronic medical health records (EMHRs). The framework utilises the Ethereum blockchain and contemporary user interfaces to tackle significant concerns in healthcare data management, ensuring enhanced security, transparency, and user engagement. The device employs smart contracts to automate data access and permission management, reducing administrative complexities and enhancing data integrity. Utilising MetaMask and IPFS, user-friendly interfaces provide medical physicians and patients with quick access to EMHRs, promoting enhanced patient care and medical decision-making. The performance benefits of the framework are evaluated by performance evaluation, which utilises many metrics like transaction processing times, data retrieval times, system uptime, data storage efficiency, and user adoption rates. The results pertain to the device's level of responsiveness and durability, highlighting its capacity to facilitate the interchange of healthcare data management. This study establishes the basis for a future in which medical information is effectively exchanged and controlled, resulting in more accurate diagnoses, more efficient patient care, and adherence to regulatory standards. This technology plays an important part in advancing the development of safe and personalised EMHR control, as the healthcare industry continues to progress. © 2024 IEEE.

关键词： Smart contract

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Renewable Energy Generation Prediction using Structure Embedded based Gated Recurrent Unit 1

Renewable Energy Generation Prediction using Structure Embed...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Mudunuri, E C Vidya Sagar Barathi, K. Madhubalan, S. Anita Sofia liz, d.R. Hussein, Ramy Riad Sagi Rama Krishnam Raju Engineering College Department of Electrical and Electronics Engineering Bhimavaram India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India Sona College of Technology Department of Electrical and Electronic Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350352931

The renewable energy provides several environmental and economic benefits that compared to other energy resources such as nuclear and fuel-based energy. The data acquired and utilized for predicting renewable energy generation that includes strong-volatility, significant randomness, and intermittent behavior. However, the variation in data obtained from various energy generation resources lead to significant challenge and results in inaccurate prediction results. To overcome this problem, a Structure Embedded based Gated Recurrent Unit (SE-GRU) method is proposed for predicting the renewable energy generation and consumption accurately. The proposed SE-GRU model solves the issue of variation in data due to factor like climate conditions by capturing long term dependencies and immediate fluctuations that enhanced the prediction results. Initially, the data obtained from renewable resources are preprocessed by using techniques like normalization and filling missing values. These preprocessed data are forwarded to proposed prediction model SE-GRU to predict the energy generation and consumption accurately. The experimental results of proposed SE-GRU method achieved Mean Squared Error (MSE) of 0.19 which is less than existing prediction approaches such as Echo State-Network-Convolutional Neural Network (ESNCNN). © 2024 IEEE.

关键词： Clean energy

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U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate TB lesion Segmentation and Classification 8

U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Karthikeyan, M. Saravana Raj, J. Relin Francis Parvathi, R. Anushya, S. Thanga Krishnan, R. Santhana Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India SaveethaInstitute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Department of Smart Computing Vellore India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350376425

U-Net-Attention-TBNet is an innovative deep-learning model developed for precisely segmenting and classifying tuberculosis (TB) lesions in chest X-ray images. This Model combines the U-Net architecture with an advanced attention mechanism, enhancing feature extraction and boosting detection accuracy. Trained on the CheXpert dataset, U-Net-Attention-TBNet demonstrates superior performance compared to existing models, including denseNet with U-Net, ResNet with U-Net, and VGGNet with U-Net. It achieves significantly higher accuracy, precision, recall, and F1 score, showcasing its effectiveness in distinguishing between primary TB lesions, secondary TB lesions, miliary TB lesions, and calcified granulomas. The attention mechanism refines the Model's ability to focus on pertinent features, leading to improved segmentation and reduced false positives. This progress represents a significant advancement in TB diagnosis, offering enhanced reliability and efficiency in medical imaging and contributing to better management of tuberculosis. © 2024 IEEE.

关键词： Medical imaging

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Optimizing Query Performance in Big data Systems Using Machine learning Algorithms 2

Optimizing Query Performance in Big Data Systems Using Machi...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Thirupurasundari, d.R. Rajesh Kumar, K. Palani, Hari Kumar Ilangovan, S. Senthilvel, P. Gururama Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai India Madha Engineering College Kundrathur Department of Electronics and Communication Engineering TamilNadu Chennai India School of Engineering Ajeenkya D y Patil University India K.L.N. College of Engineering Department of Information Technology Tamilnadu Pottapalayam630612 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India

ISBN: (纸本)9798350369960

The retrieval of information from extensive datasets in an efficient manner is critical in the era of big data. Conventional optimization methods encounter difficulties in managing the multitude of intricate and varied queries that big data systems confront. To optimize query efficacy in big data systems, this article introduces an innovative strategy that uses machine learning (Ml) algorithms. To forecast optimal query execution strategies, our methodology entails training machine learning models using historical query execution data. The methodology consists of two components: First, characteristics are extracted from query plans, encompassing query patterns and statistics pertaining to the underlying data. Furthermore, to forecast the efficacy of a specific strategy, Ml models, including decision trees, neural networks, and gradient-boosting machines, are trained using these characteristics. The findings were persuasive. In particular, for intricate queries, our Ml-based optimizer enhanced query performance by as much as 60 % when compared to conventional optimizers. Changing duties and data distributions were also accommodated by our system, demonstrating its robustness and adaptability. Our research establishes a foundation for an emerging class of adaptive query optimizers capable of acquiring knowledge and adjusting to the dynamic characteristics of data and queries. By integrating machine learning capabilities with big data systems, we present a novel solution that significantly enhances the speed and efficiency of data retrieval. © 2023 IEEE.

关键词： Adaptive Query Planning Big data Machine learning Performance Improvement Query Optimization

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