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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Amudha, S. Satheesh Kumar, M. Amutha Devi, C. Sankar, Sasi Rekha Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India

ISBN: (纸本)9783031689079

To prevent fatal heart failure early detection of Heart attack is essential steps. Heart Disease prediction happened well in advance using Machine learning, Artificial algorithms in Information Communication Technology, and the use of a combination of multiple algorithms can further improve accuracy of predictions. However, it is important to note that the accuracy of algorithms in predicting heart disease depends on the quality and quantity factors. Important privacy concerns and potential biases are also very much essential in data. Reason for heart disease includes improper food habits and an increase in fat content due to less physical work. The diagnosis of heart illness is mainly related to symptoms, and clinical examination of patients. In this study, we try to classify and predict heart diseases at early stages on specifical features by fusing Various Machine Learning Algorithms. Machine learning when being used in health-care is able to detect disease earlier and accurately. This work yielded an accuracy of 88.5% in predicting heart disease using Random Forest Algorithm. To increase the prediction, many algorithms were combined with more tree classifiers to select features with domain-knowledge adding and physician expertise to the prediction models. This can lead to more accurate and reliable predictions, that will ultimately improve patient diagnosis and reduce health-care costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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Supply Chain Planning of Agricultural-Products Under Water Scarcity Using Deep Learning

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Iranian Journal of science and Technology - Transactions of Civil engineering 2025年 1-14页

作者： Sampath, P. Mahariba, A. Jackulin Sathiyamurthi, P. Sridharan, S. Department of Computer Science and Engineering - Cyber Security Easa College of Engineering and Technology Tamil Nadu Coimbatore India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chennai India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Electrical and Electronics Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India

All people on the planet expected agriculture to provide them with food to survive. The process of producing agricultural goods while integrating logistical elements is labor-intensive. Additionally, the lack of water exacerbated several issues with agricultural product production. The researchers did not adequately address the issues surrounding the lack of water over food chains. For supply chain management, this paper presented a novel deep learning based optimization mechanism to address the agricultural production challenges under water scarcity. Hence, this study proposed a Dynamic Salp Swarm Optimization (DSS) algorithm for feature selection. The DenseNet121 based Modified Runge–Kutta Optimization (MRKO) is used to predict the water usage. For supply chain optimization, unique technique called Beetle Swarm Optimization (BSO) proposed. In addition, this reduces production costs and time while increasing profitability. The Python simulator is used to perform simulations and implement the processes. According to the similar study, the proposed strategy outperforms all other methods in terms of supply chain manufacturing cost, time, accuracy, and recall for water consumption predictions. The overall sustainability enhanced and the agricultural supply chain water scarcity impact mitigated with these findings and it describes the potential of proposed work compared to state-of-art. From the comparative study, the proposed work demonstrated 96.81% accuracy, 95.7% precision and 96.8% recall with3.11% MSE, 2.10% MAE, and 3.10% RMSE results. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Agri-products Supply chain Water consumption and DenseNet121 Water scarcity

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

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An Efficient Prevention and Challenges in Wireless Sensor Networks for Energy and Security Concern

An Efficient Prevention and Challenges in Wireless Sensor Ne...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Sumathy, G. Geetha, K. Maheshwari, A. Arunarani, A.R. Samuel, Prithi Srmist Department of Computational Intelligence Kattankulathur Campus Chennai India Periyar Maniammai Institute of Science and Technology Department of Computer Science and Engineering Vallam Thanjavur613403 India

ISBN: (纸本)9798350334357

To have the good quality design of applications for Wireless Sensor Network (WSN), it's is much important for considering the challenges. WSN emerged with some common criteria with wireless network;they have the technical challenges, which is different from the traditional WSNs. The algorithm and protocol, which was proposed for traditional WSN, is not comparably good for the modern WSN. WSN has multiple type of sensors depending on the wide range of parameters. The deployment of nodes in WSN is in a random manner or the nodes will the manually planted in the desired location. The deployment of node in the areas where manual attending is difficult, there are chances of energy deficiency due to regular discharge of battery or by the short circuit. For minimizing these data loss issues, the affected nodes should be detected. The deployment of Sensor Node (SN) makes congestion in network by continuous transmissions by many SN. The other could be by the length of the link physically. Like, two nearby nodes cannot communicate with each other due to the physical interference (whereas distance nodes can be connected). The SN could have larger number of the sensor nodes which has the ability of sense, processing the data and commutation abilities. This paper describes the simulation tool for the implementation. Finally, the performance of Efficient Prevention and Challenges in Wireless Sensor Networks for Energy and Security Concern is provided. From the experimental results, security management system is estimated. © 2023 IEEE.

关键词： Sensor nodes

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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2nd International Conference computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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Solar Panel Power Prediction Using Machine Learning Technique

Solar Panel Power Prediction Using Machine Learning Techniqu...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Sekar, S. Kaviya, V. Vivekanandan, G. Nithya, P. Srm Valliammai Engineering College Department of Information Technology Chennai India Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798350394580

One of the most significant renewable energy sources produced by photovoltaic panels (PV) is solar energy. Only during the day is solar energy available, and it is affected by meteorological elements such sun irradiation, cloud coverage, rainfall, and temperature. Due to these variables, the integration of solar power into the power grid is more challenging than it is for conventional energy sources. Changes in the solar power output result in increased electricity demand and higher operating costs. This leads to predict the power produced by solar PV panels accurately. There are numerous methods for estimating the power produced by PV systems, and the data sources used are either just historical data on PV power or historical data on both PV power and weather. In proposed system the data sources used are previous PV power output, weather data and weather forecasts in order to correctly get the effects of solar panel power prediction and actual operating condition data on the efficiency of power generation by using Support vector machine learning algorithm. The model has been trained and validated using previous year data from a input as year, month, date, time, temperature, humidity, direct radiation, diffuse radiation, direct normal irradiance, wind speed and outputs the power generated which gives higher prediction accuracy. © 2023 IEEE.

关键词： accuracy solar panel support vector machine

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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FADA: Flooding Attack Defense AODV Protocol to counter Flooding Attack in MANET 5

FADA: Flooding Attack Defense AODV Protocol to counter Flood...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Kiruthika, M. Krishnasamy, Lalitha Samuel, Prithi Kanagaraj, Kaavya School of Computing SRM Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India School of Engineering and Technology Christ University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350305449

The intrinsic nature of a Mobile Ad hoc Network (MANET) makes it difficult to provide security and it is more vulnerable to network attacks. Denial of Service (DoS) attack can be executed using Flooding attack, that has the potential to bring down the entire network. This attack works by delivering an excessive number of unwanted packets that consumes too much battery life, storage space, and bandwidth, that eventually lowers the system's performance. In order to flood the network, the attacker injects fake packets into it. Both Control Packet flooding and Data flooding attacks are taken into account in this study. FADA (Flooding Attack Defense AODV) protocol is proposed to counter flooding attack that promotes greater utilization of existing resources. This research identifies the attack-causing node, isolates it and protects the network against flooding attack. Attack Detection Rate, Attack Detection Accuracy, End-to-end delay and Throughput are few metrics used for evaluation of the proposed model. NS-2.35 is used to demonstrate the efficiency of the suggested protocol and the results prove that the proposed model increases system's throughput while decreasing attack. The simulation results have shown that the proposed FADA protocol performs better than the existing models taken into consideration. © 2023 IEEE.

关键词： Mobile ad hoc networks

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Islanding Detection Using Pattern Recognition by HOG Features with Ensemble Classifier 3

Islanding Detection Using Pattern Recognition by HOG Feature...

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3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Roy, Soumya Bhakat, Rishika National Institute of Technology Department of Electrical Engineering Jamshedpur India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Brainware University Department of Computational Sciences West Bengal India Cambridge Institute of Technology Department of Management Ranchi India

ISBN: (纸本)9798350390728

This study presents a novel approach for detecting islanding based upon image categorisation using Ensemble Classifier is proposed. Images are used to derive the histogram of oriented gradient (HOG) characteristics so as to identify non-islanding as well as islanding events. The ever-increasing need for power encourages the growth of Distributed Generation (DG). Islanding is one of the key challenges associated with excessive penetration of DG sources and can cause harm to the clients and their equipment. The islanding needs to be detected in two seconds, according to the IEEE 1547 DG interconnection rules, and the DG must be turned off. In the suggestive technique, spectrogram images are acquired through the time-series signals that are derived from the point of common coupling (PCC). The features are extracted from these images using the histogram of oriented gradient (HOG). The resulting feature vector is then utilised as an input for both the training and testing processes of the ensemble classifier. For deriving the spectrogram image, the rate of change of voltage is utilised as a parameter. In order to evaluate the performance of the ensemble classifier, 5, 10, 20, and 25-fold cross-validations are performed. The findings from the classification analysis indicate that the utilisation of an ensemble classifier in conjunction with the histogram of oriented gradient feature for image classification yielded remarkable outcomes, including an accuracy of 97.45% and a validation error of 0.188. © 2024 IEEE.

关键词： Graphic methods

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Advancing ECT Imaging: Residual UNet Deep Neural Network Approach 1st

Advancing ECT Imaging: Residual UNet Deep Neural Network App...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Deabes, Wael Khalifa, Fahmi Abdel-Hakim, Alaa E. Bouazza, Kheir Eddine Computational Engineering Mathematical Sciences Department Texas A&M University SA San AntonioTX78224 United States Electrical and Computer Engineering Department Morgan State University BaltimoreMD21251 United States Computer Science Department in Jamoum Umm Al-Qura University Makkah Saudi Arabia Computer and Information Science Division Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031823763

Recently, there has been significant progress in Electrical Capacitance Tomography (ECT) image reconstruction, yet there remains room for enhancing both speed and quality in these reconstructions. Deep Learning (DL) offers a promising avenue due to its capacity to handle intricate nonlinear functions. This study introduces DL-based ECT image reconstruction model based on Residual UNet and termed as ECT_ResUNet. A sizable dataset containing permittivity distribution and corresponding capacitance measurements was created to train and evaluate the proposed model. The ECT_ResUNet processes modulated capacitance vectors, representing them as 66×66 images. The model’s effectiveness and practicality were assessed using unseen samples, demonstrating precise image reconstruction with an average Image Error (IE) of 0.0918 and Correlation Coefficient (CC) of 0.9527. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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