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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Ahmad, Salman Khursheed Ikra, K.M. Sharma, Preeti Kaushik, Yogita Choudhary, Amar Tripathi, Pradeep Kumar Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Mangalmay Institute of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science -DS Ghaziabad India Sunder Deep Group of Institutions Department Mca Ghaziabad India Alliance University Department of Electronics and Communication Engineering Bengaluru India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350360165

This study presents a novel IoT-enabled fitness tracking device intended to improve patient care through the capture of real-time physiological records and predictive analytics. The system uses a variety of sensors, including an Arduino UNO, an LM35, a MAX30100 pulse charge and SPo2 sensor, and a stress sensor, to continuously gather health data from ten different patients. Proactive health assessments and early intervention are made possible by the combination of cloud-based analysis and advanced system learning models, such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbours (KNN), and Recurrent Neural Networks (RNN). Tested on real-world datasets, the experimental results show how effective the device is at making accurate and timely predictions. Notably, SVM is the most accurate variant, closely followed by ANN, KNN, and RNN. The confusion matrices ensure accurate and thorough examination of each version's performance, aiding in selecting the most relevant set of rules for real-time health monitoring to be used by clinicians. The present study, as groundbreaking work, demonstrates a major leap in merging IoT with device expertise in the healthcare sector that usages new possibilities in proactive and personalized treatment strategies. The recommended tool's innovative and reliable predictive analytics can potentially revolutionize patient transportation in the healthcare system. © 2024 IEEE.

关键词： Electronic health record

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Machine Learning: An Analytical Approach for Pattern Detection in Diabetes 1

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8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Chauhan, Ritu Goel, Anika Kaur, Harleen Alankar, Bhavya Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Jamia Hamdard Delhi India

ISBN: (数字)9789811998584

ISBN: (纸本)9789811998577

In current times, the amount of data is increasing exponentially but generated data is not transformed into knowledge data which eventually serves no purpose. This issue can be resolved by applying machine learning approaches which prove to play a promising role in the field of prediction. Similarly, in healthcare industries, there is a supply of large amount of data for which it is necessary to extract information for prognosis, diagnosis, and medication development. In this paper, we have focused on diagnosis of diabetes which is a chronic disease with fastest global growth rate. The current study is carried out in two phases where in first phase data is pre-processed to remove the unwanted and missing values and in second phase the data is analyzed by model building using machine learning algorithm. Additionally, we have attempted machine learning techniques in our study such as decision tree classifier and logistic regression which can be used for diagnosis of diabetes, and we attain the accuracy of 75.9% for decision tree classifier and 74.6% for logistic regression. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Investigation on Preserving Privacy of Electronic Medical Record using Split Learning 5

Investigation on Preserving Privacy of Electronic Medical Re...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Kiruthika, M. Kumar, Abhishek Krishnasamy, Lalitha Sarveshwaran, Velliangiri Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Punjab National Bank New Delhi110075 India Department of Computer Science and Engineering School of Engineering and Technology Christ University Karnataka Bengaluru560074 India

Artificial Intelligence is deployed in multiple areas, including healthcare. Utmost research is done in AI enabled healthcare industry because of the demands like accurate result, data security, exact prediction, huge volume of data, etc. In conventional deep learning models, the training happens with the dataset that are stored in a single device. This requires a huge storage space and highly efficient machines to train the data. Usage of big data, demands for innovative models that can be deployed and used in confined storage. Split learning is one such collaborative distributed deep learning model that allows the data to be stored in a split fashion. Split learning supports desirable features like less storage, more privacy to raw data, ability to work with resource constraints, etc., making it suitable for storing electronic medical record of patients. This paper discusses the advantages of using split learning for healthcare, the possible configurations of split learning that supports data privacy in healthcare and finally discusses the open research challenges in implementing split learning for healthcare. © 2024 The Authors. Published by Elsevier B.V.

关键词： Digital storage

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Securing Neural Network in the Cloud: A Systematic Approach of Cloud Security Based on ANN-SVM Model 4

Securing Neural Network in the Cloud: A Systematic Approach ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arunarani, Ar Vijayagopal, V. Kartheesan, L. Dwivedi, Vijay Kumar Rajesh Kumar, A. Nishant, Neerav School of Computing College of Engineering Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Computer Applications Tamilnadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Surguja India N.S.N. College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Karur India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331540364

Many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor Mutual Authentication and Key Agreement (MAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor MAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVM was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVM. © 2024 IEEE.

关键词： Cloud security

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Late and Early Blight Diseases Identification of Potatoes with a Light Weight Hybrid Transfer Learning Model

Late and Early Blight Diseases Identification of Potatoes wi...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Siddique, Abu Zobayer Bin Das, Shoibal Tabassum, Poonam Tasir, All Moon Roy, Shovon Rahman, Md. Saifur Mridha, M.F. Islam, Ashraful Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Independent University Bangladesh Center for Computational and Data Sciences Dhaka Bangladesh

ISBN: (纸本)9798350337617

Potatoes are one of the world's most important commodities, and leaf maladies such as early and late blight can substantially reduce their yield and quality. Hence, both farmers and researchers must prioritize quick and precise illness diagnosis. In our research, we propose a strategy based on transfer learning for classifying toxic and diseased potato leaf tissue. We specifically used our dataset of potato leaf photos to fine-tune the Mobile-Net model, which was a pre-trained convolutional neural network. To enhance the model's functionality, we also added a few more layers. Our study found that, in comparison to other state-of-the-art methods, our methodology outperformed them all by achieving a multi-class classification accuracy of 99%. Our method can be used to detect and monitor potato leaf maladies in real-world situations, which could eventually contribute to enhancing potato productivity and food security. © 2023 IEEE.

关键词： Deep learning

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Leveraging Internet of Things (IoT) Sensors and Deep Learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep Learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep Learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep Learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments 13

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

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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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Building Consensus in Group Decision-Making with Intuitionistic Reciprocal Preference Relations: An Analysis of Various Protocols of Information Granularity Distribution

Building Consensus in Group Decision-Making with Intuitionis...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gonzalez-Quesada, Juan Carlos Cabrerizo, Francisco Javier Herrera-Viedma, Enrique Pedrycz, Witold Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain University of Alberta Department of Electrical & Computer Engineering EdmontonABT6R 2V4 Canada

ISBN: (纸本)9781665410205

On the one hand, to model experts' preferences in group decision-making, intuitionistic reciprocal preference relations have widely been used because they allow for accommodating hesitation degrees, which are inherent to all decision-making processes. On the other hand, an optimization of information granularity distribution has recently been applied to establish consensus during group decision-making processes. Concretely, a symmetric and uniform distribution of information granularity has been considered for intuitionistic reciprocal preference relations. However, there exist other protocols of information granularity distribution that could be used. Therefore, we aim to analyze all the information granularity distribution protocols and determine their effectiveness in building consensus through intuitionistic reciprocal preference relations. The performance of the different protocols is discussed by conducting some numerical experiments that help provide insights into the effectiveness of the protocols to build consensus. © 2024 IEEE.

关键词： Information granules

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Revolutionizing Skin Cancer Detection with Raspberry Pi-Embedded ANN Technology in an Automated Screening Booth 4

Revolutionizing Skin Cancer Detection with Raspberry Pi-Embe...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Srinivasan, S. Indra, G. Saravanan, T.R. Murugan, S. Srinivasan, C. Muthulekshmi, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

In this ground-breaking endeavor, we describe an automated skin cancer screening booth that can transform current approaches to early detection. The merging of Raspberry Pi-controlled technology with cutting-edge Artificial Neural Network (ANN) models forms the basis of our groundbreaking breakthrough. The booth uses high-resolution imaging sensors to acquire detailed photographs of the user's skin, which are then analyzed in real-time by an ANN. The ability to identify potentially malignant anomalies in a timely and precise manner is made possible by this combination. In public places, user contact is facilitated by the use of an intuitive touchscreen interface, which ensures both accessibility and convenience of use. The ANN model, trained on a wide variety of data, is very good at differentiating between normal and malignant skin diseases, with the findings being instantly shown on the interface. Beyond its technological capabilities, our technology bears the prospect of broad application, which will turn screening for skin cancer into a preventative healthcare tool available to everyone. This all-encompassing strategy highlights the significance of early detection and argues for a solution that is both scalable and effective at the crossroads of technology and public health. © 2024 IEEE.

关键词： Neural networks

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