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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： El-Shafai, Walid Naguib, Ahmed Shokair, Mona Prince Sultan University Security Engineering Lab Computer Science Department Riyadh11586 Saudi Arabia Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt 6 October University Faculty of Electrical Engineering Egypt

ISBN: (纸本)9798350323511

The security of healthcare and telemedicine systems stands as a matter of paramount importance, necessitating extensive research. In the upcoming years, the medical industry envisions widespread adoption of advanced telemedicine applications. With healthcare smart devices becoming increasingly interconnected through the Internet, enabling convenient access from any location, the protection of sensitive and private information, such as personal medical images, emerges as a critical focal point. As a result, the encryption of medical images assumes a pivotal role within the domain of telemedicine and healthcare applications. This article introduces a highly efficient cryptographic system designed to enhance the protection of medical images by harnessing the power of cellular automata techniques in conjunction with hyper-chaotic procedures. In the proposed medical image encryption technique, a layered cellular automata approach is embraced, and a pseudorandom sequence derived from a four-dimensional hyper-chaotic system is employed as the cryptographic key. The study encompasses both grayscale and color images. Several assessment criteria, including the Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), average entropy, correlation coefficients, Structural Similarity (SSIM), Feature Similarity (FSIM), and Peak Signal-to-Noise Ratio (PSNR), are employed to evaluate the suggested cryptosystem. These evaluations vividly underscore the robust security performance exhibited by the proposed approach. © 2023 IEEE.

关键词： Entropy

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An Exploration of the Effectiveness of Machine Learning Algorithms for Text Classification

An Exploration of the Effectiveness of Machine Learning Algo...

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

作者： Agarwal, Ankita Aravindan Shrivastava, Manish Sathya, S. Verma, Devika A. Sohal, Jagmeet Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Centre of Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350329773

Text type is an important project in natural language processing (NLP), with programs in facts retrieval, sentiment evaluation, and report categorization. With the exponential increase of virtual information, the call for automatic text category has multiplied. Conventional text category techniques along with rule-based totally structures and statistical methods have obstacles in managing complex and massive datasets. System studying (ML) has emerged as a promising technique to text type, leveraging algorithms which can automatically learn from information and make correct predictions. This paper gives an exploration of the effectiveness of different machine getting to know algorithms for text category responsibilities. We evaluate and compare the performance of famous algorithms consisting of decision bushes, aid vector machines (SVM), ok-nearest pals (KNN), and deep learning fashions which include convolutional neural networks (CNN) and recurrent neural networks (RNN). © 2023 IEEE.

关键词： Recurrent neural networks

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NextGen Robot: An Efficient and Effortless Wall Painting Robot Design Based on Artificial Intelligence (AI) Principles

NextGen Robot: An Efficient and Effortless Wall Painting Rob...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Mary, S. Jeya Sajithra, S. Divya, G. Lakshmi, N. Giri, Pallavi Al-Qawasmi, K. Einstein College of Engineering Department of Ece Tirunelveli India R.M.K. Engineering College Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India S.A. Engineering College Department of Master of Computer Applications Chennai India Laxminarayan Innovation Technological University Department of Civil engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

A fully autonomous wall-painting robot is the focus of this paper's development, expansion, and implementation efforts. Research efforts have not communicated much about interior wall painting. Painters' eyes and respiratory systems are particularly vulnerable to the toxic chemicals used in the industry. Additionally, the monotony, time commitment, and physical exertion inherent in the painting process are hard to resist. The entire construction process may be enhanced and time and labor saved by using robots and human workers appropriately into building jobs. The Dynamic Smart Painting Robo Model (DSPRM) is a novel robot that this study introduces for intense wall painting. To test its efficacy, it is crossvalidated with the usual technique, the usual Painting Bot (CPB). Furthermore, it would provide an opportunity to lessen or eliminate human exposure to challenging and dangerous conditions, which would address most of the issues, provide protection, and make it happen more often. An autonomous robotic painting system is being developed as a result of the wall painting robot. © 2024 IEEE.

关键词： Machine design

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Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

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computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

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Integrated Fuzzy Logic and Data Mining Approach for Assessing Groundwater Vulnerability to Sea-Water Intrusion 16th

Integrated Fuzzy Logic and Data Mining Approach for Assessin...

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16th International Conference on Application of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools, ICAFS 2023

作者： Sadikoglu, Fahreddin Nourani, Vahid Najafi, Hessam Maleki, Sana Paknezhad, Nardin Jabbarian Department of Computer Engineering Near East University Nicosia Cyprus Department of IT Odlar Yurdu University Baku Azerbaijan Center of Excellence in Hydroinformatics Faculty of Civil Engineering University of Tabriz Tabriz Iran College of Environmental Science and Engineering Nankai University Tianjin China

ISBN: (纸本)9783031762826

The GALDIT model is used for the assessment of the aquifer vulnerability of Groundwater (GW), but it relies on expert judgment that contains uncertainty and is one of its weaknesses. To tackle the challenge of managing uncertainty and identifying specific vulnerabilities, this study employed a combination of Mamdani Fuzzy Logic (MFL) and data mining techniques. Then the obtained results were compared with the traditional GALDIT method. According to the Electrical Conductivity (EC) values, the Heidke Skill Score (HSS) and Total Accuracy (TA) criteria were applied to assess the outcomes of the applied methods. The surrounding aquifers of Urmia Lake was case study in this paper. The findings indicated that the GALDIT index varied from 2 to 8. The TA and HSS values in the MFL modeling were determined to be 0.59 and 0.48, respectively, therefore MFL led to more accurate and reliable results for evaluating GW vulnerability compared to the traditional GALDIT method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Salt water intrusion

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BD-ECG: Identification of Myocardial Infarction in ECG via Behavior Coupling 22

BD-ECG: Identification of Myocardial Infarction in ECG via B...

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6th International Conference on Information System and Data Mining, ICISDM 2022

作者： Iqbal, Uzair Wah, Teh Ying Ur Rehman, Muhammad Habib Bilal, Muhammad Ahmed, Adeel Department of Computer Science National University of Computer and Emerging Sciences Pakistan Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya Malaysia Division of Imaging Sciences and Biomedical Engineering Kings College London United Kingdom Department of Computer Science Quaid-e-Azam University Pakistan

ISBN: (纸本)9781450396257

In the cardiovascular diseases, early detection and identification of the relationship between different diseases are still open problems for cardiologists. In this paper, we propose a novel scheme for behavioral detection in electrocardiography data named as Behavioral detection-Electrography. The Behavioral detection-Electrography is used for early detection of abnormalities in electrography especially myocardial infarction. The Behavioral detection-Electrography embeds the two-tier architecture in which we integrate the behavioral relationship concepts with myocardial detection algorithm. In future the highlighted integral scheme will help us to identify the nature of cardiovascular diseases either it's normal or abnormal © 2022 ACM.

关键词： Diseases

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Forecasting EV Market Trends in India: A Deep Learning Approach for Two/Three-Wheelers 12

Forecasting EV Market Trends in India: A Deep Learning Appro...

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12th International Electrical engineering Congress, iEECON 2024

作者： Mishra, Rishav Dev Kumar Dash, Santanu Chudjuarjeen, Saichol Korkua, Suratsavadee School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Vellore Institute Of Technology TIFAC-CORE Vellore India Rajamangala University Of Technology Krungthep Faculty Of Engineering Dept. Electrical Engineering Bangkok Thailand School Of Engineering And Technology Walailak University Thailand

ISBN: (纸本)9798350383591

This research paper presents a comprehensive analysis of machine learning models for predicting future sales of electric vehicles (EVs) in the Indian Market. With a specific focus on 2 and 3 wheeler sales. The study compares different machine learning approaches, including Linear Regression, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) Neural Network, Through an evaluation of their respective errors, LSTM Neural Network method demonstrates superior prediction accuracy, making it the preferred model for forecasting EV sales, specifically 2 wheeler and 3 wheeler sales over the next five years. The paper aims to present the forecasted sales figures, in hopes of providing valuable insights into the future trajectory of EV sales and serving as a vital resource for stakeholders in the electric vehicle industry. © 2024 IEEE.

关键词： Long short-term memory

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An Optimal Scheme for WSN Based on Compressed Sensing

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computers, Materials & Continua 2022年第7期72卷 1053-1069页

作者： Firas Ibrahim AlZobi Ahmad Ali AlZubi Kulakov Yurii Abdullah Alharbi Jazem Mutared Alanazi Sami Smadi Department of Information Systems and Networks Faculty of Information TechnologyThe World Islamic Sciences&Education UniversityJordan Computer Science Department Community CollegeKing Saud UniversitySaudi Arabia Department of Computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic InstituteUkraine

Wireless sensor networks(WSNs)is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks,bio-medical engineering,agriculture,industry and many *** has been used in the internet-of-things(IoTs)applications.A method for data collecting utilizing hybrid compressive sensing(CS)is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network *** cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes,and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head ***,each ordinary node joins the cluster that is nearest to *** greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head *** simulation results demonstrate that when the compression ratio is set to ten,the data transfer volume is reduced by a factor of *** compared to clustering and SPT without CS,it is reduced by 75%and 65%,*** compared to SPT with Hybrid CS and Clustering with hybrid CS,it is reduced by 35%and 20%,*** and SPT without CS are compared in terms of node data transfer volume standard *** with Hybrid CS and clustering with Hybrid CS were both reduced by 62%and 80%,*** compared to SPT with hybrid CS and clustering with hybrid CS,the latter two were reduced by 41%and 19%,respectively.

关键词： Compressed sensing computer networks sensor networks ad hoc networks

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Space Ants: Episode II - Coordinating Connected Catoms 38

Space Ants: Episode II - Coordinating Connected Catoms

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38th International Symposium on Computational Geometry, SoCG 2022

作者： Bourgeois, Julien Fekete, Sándor P. Kosfeld, Ramin Kramer, Peter Piranda, Benoît Rieck, Christian Scheffer, Christian FEMTO-ST Institute University of Bourgogne Franche-Comté CNRS Montbeliard France Department of Computer Science TU Braunschweig Germany Faculty of Electrical Engineering and Computer Science Hochschule Bochum Germany

ISBN: (纸本)9783959772273

How can a set of identical mobile agents coordinate their motions to transform their arrangement from a given starting to a desired goal configuration? We consider this question in the context of actual physical devices called Catoms, which can perform reconfiguration, but need to maintain connectivity at all times to ensure communication and energy supply. We demonstrate and animate algorithmic results, in particular a proof of hardness, as well as an algorithm that guarantees constant stretch for certain classes of arrangements: If mapping the start configuration to the target configuration requires a maximum Manhattan distance of d, then the total duration of our overall schedule is in O(d), which is optimal up to constant factors. © Julien Bourgeois, Sndor P. Fekete, Ramin Kosfeld, Peter Kramer, Benot Piranda, Christian Rieck, and Christian Scheffer;licensed under Creative Commons License CC-BY 4.0

关键词： Motion planning

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Multi-objective performance prediction based on a self-constructed Al-Si alloy dataset 11

Multi-objective performance prediction based on a self-const...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Hu, Dan Liu, Yingli Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

ISBN: (纸本)9798350342406

The combination of data-driven approaches and machine learning techniques has changed the design approach for new materials, propelling materials science research into the realm of the fourth paradigm. Researchers in the field of materials science have increasingly focused on utilizing machine learning methods to uncover the underlying nonlinear relationships within material data. However, there is currently limited research in the field of aluminum-silicon alloys regarding the prediction of alloy performance based on the composition-process-structure relationship. Additionally, there is a lack of data sharing in the aluminum-silicon alloy domain. Therefore, this study proposes a methodology. Firstly, an aluminum-silicon alloy dataset is constructed by mining relevant information from materials literature. Then, high-dimensional features in the dataset are subjected to feature selection techniques for dimensionality reduction. Subsequently, a multilayer perceptron model is applied to investigate the relationship between input features such as alloy composition, preparation process, and microstructural parameters, and output features such as yield strength, ultimate tensile strength, and elongation, enabling performance prediction. © 2023 IEEE.

关键词： Feature Selection

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