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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Sun, Aohan Lu, Yanrong Liu, Xinyu School of Computer Science and Technology Civil Aviation University of China Tianjin China College of Safety Science and Engineering Civil Aviation University of China Tianjin China

ISBN: (纸本)9798331541699

This paper designed FedPDA (Federated Learning Personalized Data Authentication) a new architecture that combines personalized federated learning and domestic encryption algorithms. The FedPDA enhanced the adaptability and prediction accuracy of specific flight data, -divided the model parameters into global general parameters and personalized specific parameters. In addition, FedPDA uses domestic encryption standards SM2 and SM3 to ensure the security and integrity of flight data. The experimental results show that FedPDA can not only effectively protect user privacy and data security, but also significantly improve the stability and efficiency of the heterogeneous flight data. © 2024 IEEE.

关键词： Data Authentication Data Heterogeneity Hash Algorithm Personalized Federal Learning QAR Data

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Assessment of Electric Vehicle Charge/Swap Station of the Electric Car Based on Hybrid Electrical Vehicle Site Selection Model

Assessment of Electric Vehicle Charge/Swap Station of the El...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Al-Hameed, Mazin Riyadh Mohsin, Sarah Kadhim Diame, Tamarah Alaa Al-Tahee, Mustafa Easa, Haider Khalil Ibrahem, Marwa Almusawi, Muntather National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

Charging is the key factor influencing the development of the electric vehicle (EV) sector. This problem is important for a good system for deciding location and capacity. To calculate the charge of the suggested site, a Hybrid Electrical Vehicle Site Selection Model (HEVSSM) that thoroughly assesses the owners' charging behavior is proposed in this work. A key component of the long-term planning process is the location of the electric vehicle charging station. The method for analyzing electric car level application behavior for charging and assessing an EV charge station distribution plan is presented in this study. As the final plan results from the site and capability collection, the ideal scheme can be chosen while taking into account the two-way expenses of the owners and operators. In addition, the suggested approach has demonstrated that electric vehicle charging stations may be located and are practical and successful in cities. With varying numbers of electric evaluation vehicles, the simulation shows the viability of the proposed method. It compares it favorably to other methods in terms of voltage sensitivity ratio of 96.3%, temperature ratio of 22.6%, energy demand ratio of 97.6%, cost-effective Ratio of 29.6%, the power ratio of 25.3 %, and load ratio of 5.3 %. © 2024 IEEE.

关键词： Charging stations

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An Integrated Approach for Analysis of Electronic Health Records Using Blockchain and Deep Learning

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Recent Advances in computer science and Communications 2023年第9期16卷 1-10页

作者： Singhal, Pooja Gupta, Shelly Deepak Singh, Jagendra ABES Engineering College Ghaziabad Dr. A.P.J. Abdul Kalam Technical University Lucknow India School of Computer Science Engineering and Technology Bennett University Greater Noida India

Blockchain is used to assess health records digitally, preserving the security and immutability of the records. The goal of this study is to make it easier for patients to access their medical records and to send them alert messages about important dates for their check-ups, healthy diet, and appointments. To achieve the above-mentioned objective, an integrated approach using Blockchain and Deep learning is initiated. The first approach is Hyperledger Fabric in Blockchain, i.e., private Blockchain, for storing the data in the medically documented ledger, which can be shared among hospitals as well as Health organizations. The second approach is incorporated with a deep learning algorithm. With the help of algorithms, we can analyse the ledger, after which an alert i.e., consultation, health diet, medication, etc., will be sent to the patient’s registered mobile device. The proposed work uses nine features from the dataset;the features are identification number, age, person gender, disease, weight, consultation date, medication, diagnosis, and diet specification. The study is conducted with several features to give accurate results. The integrated model used in this suggested piece of work automates the patient's alert system for a variety of activities. In terms of precision, recall, and F1 score, testing data demonstrate that the LSTM performs better than the other models. By working together with the calendar software on android mobile devices, alert systems can be improved in the future. © 2023 Bentham science Publishers.

关键词： Blockchain

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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An Intelligent Method for Predicting Skin Disease Using OM-Extreme Learning Machine 4

An Intelligent Method for Predicting Skin Disease Using OM-E...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Suja, G.P. Akshaya, M. Salim, Abu Sarfaraz, Mohd Kathiravan, Mathur Nadarajan Singh Pundir, Aditya Kumar Muslim Arts College Research Centre of Computer Science Tamilnadu Kanyakumari India Department of Cse Tamilnadu Coimbatore India College of Computer Science and Information Technology Jazan University Department of Computer Science Jazan Saudi Arabia College of Computer Science and Information Technology Jazan University Department of Information Technology and Security Jazan Saudi Arabia Dr N. G. P Arts and Science College Department of Biotechnology Tamil Nadu Coimbatore India Arya College of Engineering and Information Technology Department of Electronics and Communication Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350300888

In terms of prevalence, skin disorders rank towards the top. Various organisms, including fungi, bacteria, allergens, viruses, and so on, can trigger skin illnesses. Improved speed and accuracy in the diagnosis of skin diseases has resulted from the development of lasers and Photonics-based medical technology. However, there is now a severe lack of affordable options for such diagnostics. Therefore, at the outset, image processing techniques aid in constructing an automated screening system for dermatology. To aid in the categorization of skin disorders, feature extraction is essential. Several methods exist where computer vision can aid in the diagnosis of skin disorders. Skin infections are frequent in Saudi Arabia because of the country's scorching, desert climate. This proposed method adds to the literature on the early diagnosis of skin diseases. To help diagnose skin conditions, to recommended using image processing. This technique uses digital images of the affected skin to determine the disease kind through the application of image analysis. The method to offer straightforward, quick, and can be done with just a camera and a ***, feature extraction for segmentation, and model performance evaluation make up the suggested method. Preprocessing makes use of a histogram and an RGB to Grayscale conversion. Segmentation is performed with the help of adaptive thresholding. The GLCM, IQA, and complete reference methods are all utilized for feature extraction. Finally, OM-ELM is utilized to assess the model's performance. Comparing the suggested method to ELM and AE-ELM, it fares quite well. © 2023 IEEE.

关键词： Mean square error

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IoT Assisted Fisherman Aid to Detect Borders and Alert System by using Intelligent GPS technology

IoT Assisted Fisherman Aid to Detect Borders and Alert Syste...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Ramesh, A.P. Nithya Paranthaman, R. Porkodi, M. Aruna, V. Rohini, S. Sri Ramakrishna College of Arts and Science Department of ECS Coimbatore India SRM Institute of Science and Technology School of Computing College of Engineering & Technology Department of Networking and Communication Chennai India Sona College of Technology Department of EEE Salem India Panimalar Engineering College Department of ECE Chennai India R.M.K. Engineering College Department of Computer Science Chennai India

ISBN: (纸本)9798350319200

The safety of fisherman will be improved with the use of technology such as the Global Positioning System (GPS) and Wireless Sensor Networks (WSN), which will be employed in this project. The GPS may be used to track exactly where the boat is at any given time. In the event that the fisherman ventures outside of the permitted region, the alarm will sound. The user or owner of the vessel is able to monitor the location of the vessel in reference to known dimensions (latitude and longitude) by making use of the vessel's own electrical equipment and software that has been specifically created for this purpose. If a person has a GPS receiver, they will be able to pinpoint their exact location on a digital map. The relay circuit on the boat will give it the ability to turn around. technology based on wireless sensor networks might be used to transmit communication between the fisherman and the Internet of Things device. The fishermen are alerted when they get close to the first two borders by a loudspeaker (which acts as a siren) and an indicator that has an LCD. In the event that the warning system is unsuccessful, there is a backup option. The boat's engine will automatically turn off when it gets closer to the third border as it travels. Now, both the government of India and the government of Sri Lanka would have access to identical data on the fishermen. © 2023 IEEE.

关键词： Alert System Fisherman Border Detection Intelligent GPS technology Internet of Things IoT Wireless Sensor Network WSN

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Data Privatization and Security using Bit Plane Complexity Segmentation 1

Data Privatization and Security using Bit Plane Complexity S...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Suresh Kumar, S. Chidambaram, G. Vijayalakshmi, S. Computer Science and Engineering PSG College of Technology Coimbatore India

ISBN: (纸本)9781665460842

Advances in digital communication play an important role in our daily lives in today's world. In general, information security systems fall into two categories: encryption and information hiding. The storage and transmission of sensitive personal data have become a partof everyday life for professional and personal well-being. Therefore, the issue of secure storage and transmission of confidential information is interest to many researchers. Steganography is a collective method to hide sensitive data in unobtrusive digital media such as video, audio, and images. One of the biggest challenges in developing steganography systems is finding the right balance between metrices like reliability, security, and data privacy. This project provides information about how the Bit Plane Complexity Steganography (BPCS) technology is applied toboth grayscale and color imaging techniques and assesses performance parameters such as data concealment, security and stability based on the peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE) with various test cases to explain how it works for different files and images. © 2022 IEEE

关键词： Cryptography

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Development of Primary School Mathematics Education technology Resources based on Fuzzy Clustering Algorithm 2

Development of Primary School Mathematics Education Technolo...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Gloria Jeyaraj, Josephine Pon Uthayakumar, P. Muthuraman, M.S. Surendran, S. Bhuvana, R. Sheeba, M.R. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Mathematics PSNA College of Engineering and Technology Tamilnadu Dindigul India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Dept of Computer Science AM JAIN College Tamilnadu Chennai India Department of Computer Science Government Arts and Science College for Women Thoothukudi District Sathankulum India

ISBN: (纸本)9798350305418

India's compulsory primary education emphasizes mathematics. Math helps boost students' logical and rational thinking. It supports our nation's scientific development and modern students' scientific and cultural growth. Students learn elementary school mathematics by facing a lot of data, thinking, and checking results. Teaching materials are not the only educational technology in the primary school mathematics teaching reform. Math teachers have started to focus on the need for more teaching resources and new tools and technology. Due to the lack of complete and systematic teaching materials, primary school mathematics technical resource development has new goals and requirements. Resource mining with fuzzy clustering is great. Fuzzy clustering algorithm can introduce fuzzy mathematics into cluster analysis, use membership function clustering to determine sample affinity, and analyze and aggregate a large number of text resource information into meaningful classes or clusters for primary school mathematics education technology resource mining and development. An optimization strategy for cluster number is given to meet classic fuzzy clustering algorithm initialization requirements. The optimized fuzzy clustering algorithm optimizes the initial clustering center, overcoming its sensitivity to the beginning value. Thus, the data set is fuzzy-divided unsupervised. This article analyzes created resources to inform primary school campus mathematics education development. © 2023 IEEE.

关键词： educational technology Fuzzy clustering algorithm primary school mathematics resource development

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Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing

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Multimedia Tools and Applications 2025年第15期84卷 14359-14386页

作者： Thakur, Rahul Sikka, Geeta Bansal, Urvashi Giri, Jayant Mallik, Saurav Department of Computer Science and Engineering Dr. B R Ambedkar National Institute of Technology Punjab Jalandhar144011 India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi110036 India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Environmental Health Harvard T H Chan School of Public Health BostonMA United States Saveetha University TN Chennai602105 India

Fog computing is an emerging paradigm that extends cloud computing (CC) by providing computation, communication, and storage services at the edge of a network, closer to end devices. It has gained significance due to the rapid development of IoT devices, which generate various types of tasks. Processing these tasks in the cloud can strain its infrastructure and lead to delays in time-sensitive requests. To address this limitation, fog computing (FC) concepts were introduced in 2012 by Cisco. FC is not meant to replace CC but rather to complement and extend its capabilities. One of the challenges in FC is efficiently assigning tasks to appropriate resources to minimize makespan, energy consumption (EC), and increase the number of deadline-satisfied tasks. In this work, the improvement of semi-greedy algorithm has been done by incorporating fuzzy logic (FL). By leveraging FL, the aim is to enhance the algorithm's decision-making process and make it more adaptive to varying conditions and uncertainties in the fog environment. The use of FL allows more nuanced and flexible task scheduling (TS) decisions based on fuzzy sets and fuzzy rules. The simulation experiments demonstrate that the proposed algorithm outperforms PSG (Priority-aware Semi-Greedy) and PSG-M (PSG with multistart), which were identified as the best scheduling algorithms (Algos) in the literature review. The algorithm exhibits better performance in terms of reducing makespan, EC, and increasing the percentage of deadline-satisfied tasks compared to PSG and PSG-M. The inclusion of FL further enhances the algorithm's effectiveness in handling complex scheduling scenarios in a FC environment. To evaluate the performance of the proposed algorithm, different simulation experiments have been conducted using a selected simulator after a systematic review of existing simulators. The experiments involved 300 and 500 random and static tasks, as well as 60 fog nodes in the fog environment. All simulations were impl

关键词： Fog computing

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