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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Manivannan, S.K. Kavididevi, Venkatesh Muthukumaran, D. Nandhini, R. Vakula, V. Srinivasan, C. Srm Institute of Science and Technology Faculty of Management Kattankulathur Tamil Nadu Chennai India Vardhaman College of Engineering Department of Information Technology Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Optimizing the allocation of healthcare resources has never been easier than with the help of Graph Neural Networks (GNNs). In healthcare systems, where complications abound, GNNs may help with the allocation of resources in a way that improves both patient outcomes and operational efficiency. The goal is to use GNNs' graph-modelling capabilities to describe healthcare networks, with hospitals, doctors, and other medical resources acting as nodes and potential transfers of patients or shared resources as edges. Using both past and present data, the goal is to optimize resource allocation by forecasting the most effective distribution patterns. The decision-making process is improved by using important methods including attention mechanisms, message-passing algorithms, and node embeddings. The accuracy and scalability of healthcare resource management are both enhanced by GNNs, which capture spatial and temporal dynamics. The general efficacy of healthcare delivery systems is improved by this method, which permits more precise forecasts and efficient use of resources. The results highlight how GNNs can change healthcare resource allocation models. From GitHub repository data the short interfering RNAs (siRNAs) and microRNAs (miRNAs) data with efficacy is calculated. Out of the 8 data collected as samples for various siRNAs the efficacy minimum value is 0.07 and the maximum value is 0.946 and in other data samples collected it is 0.386 and 0.777. For miRNAs it is 0.934 and 0.394 and in other data sample collected it is 0.738 and 0.432. © 2024 IEEE.

关键词： Graph neural networks

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Coronary Artery Disease Prediction Using Enhanced Multi Layer DCNN 10

Coronary Artery Disease Prediction Using Enhanced Multi Laye...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, electronics and computer engineering, UPCON 2023

作者： Malik, Varun Sharma, Smita Mittal, Ruchi Rao, A. Kakoli Martin, R. John Khan, Akhilesh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity School of Engineering and Technology Amity University Department of Electronics and Communication Engineering Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India Jazan University Faculty of Computer Science and Information Technology Saudi Arabia Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

Coronary Artery Disease(CAD) is a prevalent and life-threatening cardiovascular condition affecting millions of individuals globally. Early detection and accurate prediction of CAD are vital for timely intervention and prevention of severe cardiac events. Convolutional Neural Networks (CNNs) and other deep learning approaches have recently demonstrated amazing effectiveness in a number of medical applications, including illness prediction. This research proposes an innovative approach, Enhanced Multi-Layer Deep Convolutional Neural Network (EML-DCNN), for the prediction of Coronary Artery Disease. The proposed EML-DCNN architecture integrates advanced deep learning concepts, feature engineering, and data augmentation techniques to enhance the model's predictive performance. The multi-layer structure of EML-DCNN enables the automatic extraction of intricate patterns and features from medical imaging data, specifically focusing on coronary artery images. Additionally, the model incorporates attention mechanisms and residual connections, enhancing its ability to capture subtle details and relationships within the data. To validate the efficacy of the proposed EML-DCNN model, extensive experiments were conducted on a large and diverse dataset comprising coronary artery images obtained from various medical sources. The results demonstrate superior predictive accuracy, sensitivity, and specificity compared to existing CAD prediction models. Moreover, the model's interpretability is enhanced through visualization techniques, providing valuable insights into the features influencing the prediction process. © 2023 IEEE.

关键词： Coronary Artery Disease deep learning disease prediction Enhanced Multi-Layer Deep Convolutional Neural Network

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Optimized Control of IoT-Monitored Microgrid Systems Using Genetic Algorithm

Optimized Control of IoT-Monitored Microgrid Systems Using G...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Vijayalakshmi, N. Yuvaraj, S. Baskar, V. Vijaya Krishnaswamy, R. Sasikala, K. Srinivasan, C. Government Polytechnic College Deputed from Annamalai University Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India University College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Ariyalur India Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350357769

Microgrid (MG) systems have become a good way to make power generation and distribution more efficient, especially when Distributed Energy Resources (DERs) are introduced. The inverter is a critical part of MGs since it regulates the voltage output to the grid. A unique method is proposed to tune the settings of a Proportional-Integral-Derivative (PID) controller for the inverter using Genetic Algorithm (GA). The main aim is to use GAs to improve the inverter control in an MG that is monitored by the Internet of Things (IoT). GA's are very useful for resolving difficult optimization issues. Optimization of voltage regulation, stability, and power transmission to the grid may be achieved by using GA-based tuning of PID controller settings. From the simulation results, the GA-based methodology provides better control performance than conventional tuning approaches, providing exact voltage regulation and improving the MG's stability and dependability. © 2023 IEEE.

关键词： Genetic algorithm and Optimization technique Microgrid Renewable energy Voltage control

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Early Detection of Black Fungus Using Deep Learning Models for Efficient Medical Diagnosis 4

Early Detection of Black Fungus Using Deep Learning Models f...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Hassan, Esraa Saber, Abeer El-Kenawy, El-Sayed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial intelligence Information technology Dept. Damietta34517 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387063

Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%. © 2024 IEEE.

关键词： COVID-19

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Integrating Sustainability in engineering with a Double Degree Program Case Study 65

Integrating Sustainability in Engineering with a Double Degr...

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65th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2024

作者： Arhun, Shchasiana Hnatov, Andrii Kunicina, Nadezhda Trunova, Iryna Ulianets, Olha Daleka, Vasyl Kharkiv National Automobile and Highway University Automobile Faculty Vehicle Electronics Department Kharkiv Ukraine Institute of Industrial Electronics and Electrical Engineering Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia O. M. Beketov National University of Urban Economy in Kharkiv Department of electric transport Kharkiv Ukraine

ISBN: (纸本)9798350365771

This article presents an innovative analysis of the development and implementation of double degree programs in higher education, focusing on the emerging fields of electric vehicles and automotive electronics. The study examines the 'Electric Vehicles and Automotive electronics' educational program, highlighting the critical skills and competencies that students acquire to meet the evolving demands of the modern job market. An algorithm for establishing educational partnerships has been developed, leading to the proposal of a pioneering double degree program between Kharkiv National Automobile and Highway University (KhNAHU) and Riga Technical University (RTU), supported by the Erasmus+ DIGITRANS project. The results show that such collaborative educational frameworks significantly enrich engineering education, giving students a competitive edge in the global workforce. This research provides valuable insights for higher education and international educational initiatives, emphasizing a scalable model for developing interdisciplinary and international programs. © 2024 IEEE.

关键词： Students

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Proposed Biometric Security System Based on Deep Learning and Chaos Algorithms

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computers, Materials & Continua 2023年第2期74卷 3515-3537页

作者： Iman Almomani Walid El-Shafai Aala AlKhayer Albandari Alsumayt Sumayh S.Aljameel Khalid Alissa Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School of Information TechnologyThe University of Jordan11942Jordan Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Applied CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Science Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Nowadays,there is tremendous growth in biometric authentication and cybersecurity ***,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private ***,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be *** work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and *** proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their *** employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric *** obtained features are utilized to generate two random chaos *** first random chaos matrix is used to permute the pixels of biometric *** contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)*** assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were *** assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing *** example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.

关键词： Biometric security deep learning AE network 2D CLM cybersecurity and authentication applications feature extraction unsupervised learning

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Analysis and Design of the Detection and Control System of Authorized and Unauthorized Users using Internet of Things and Machine Learning

Analysis and Design of the Detection and Control System of A...

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International Conference on Control, Instrumentation, and Automation

作者： Negar Sarshar Ali Nasiri Department of Electronics Engineering Faculty of Electrical & Computer Engineering Qom University of Technology Qom Iran Department of Electronics Engineering Faculty of Electrical & Computer Engineering Tarbiat Modares University Tehran Iran

In this article, a system has been designed using the Internet of Things and machine learning to detect equipment theft, monitor the entry and exit of people, as well as monitor people's activities to prevent equipment theft and provide security to the environment against possible theft. By using the motion detection sensor and the push buttons installed on the entrance doors, the entry and movement of people are monitored in real-time. Then, with machine vision and image processing algorithms, known and unknown people are cleaned and if necessary, a warning message including the video of the person's presence in the environment along with the person's image is sent to the relevant official. Also, the security doors will be locked until the relevant official confirms the safe situation.

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A Rail-To-Rail Transconductance Amplifier Based on Current Generator Circuits

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2023年第10期31卷 1624-1628页

作者： Akbari, Meysam Hussein, Safwan Mawlood Hashim, Yasir Khateb, Fabian Tang, Kea-Tiong University of Kurdistan Department of Electronics and Communication Engineering Sanandaj66177-15175 Iran Tishk International University Faculty of Engineering Department of Computer Engineering Erbil44001 Iraq Department of Microelectronics Brno University of Technology Brno601 90 Czech Republic Brno University of Defense Department of Electrical Engineering Brno66210 Czech Republic Czech Technical University in Prague Faculty of Biomedical Engineering Kladno3105 Czech Republic National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

In this brief, two current generator circuits are used to design a self-biasing transconductance amplifier. The current generators are configured using two n-channel and p-channel cascode current mirrors by which a high input dynamic range is achieved. Since such a topology creates positive feedback, the transconductance of the circuit is also increased causing higher performance. To ensure the stability of the circuit, constant current sources can be paralleled with the current mirror topologies, which of course are implemented using input drivers. Therefore, two n-channel and p-channel input differential pairs are added to the current generator circuits by which not only a rail-To-rail operation is achieved but also the amplifier is stabilized. The proposed circuit was fabricated in the TSMC 0.18-μm CMOS process with a silicon area of μm54.1× 71 μm. Under a 1.8-V supply voltage, the experimental results showed a high input common-mode range (ICMR), while a gain bandwidth (GBW) of 83.9 MHz was measured for a capacitive load of μm2×6μm pF. In addition, a dc gain and a slew rate (SR) of 68.4 dB and 71.7 V/ μm , respectively, were achieved. © 1993-2012 IEEE.

关键词： Operational amplifiers

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Smart Ports Solutions for Cargo Container Tracking and Vessel Traffic Counting Systems using IoT and Cloud Computing 10

Smart Ports Solutions for Cargo Container Tracking and Vesse...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Priya, S. Sairam, A. Azath, H. Manivannan, S.K. Mohankumar, N. Vedasundara Vinayagam, P. Deemed To Be University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Bhopal India Faculty of Management Srm Institute of Science and Technology Chennai Tamil Nadu Kattankulathur India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research examines current port operations that use Internet of Things (IoT) technologies for cargo container monitoring and vessel traffic counts. Effective port management is crucial for smooth commodities movement and congestion reduction as global commerce grows. Addressing these issues with IoT solutions is promising. It analyzes cargo container IoT sensors and devices for real-time location, condition, and security monitoring. These IoT-enabled containers provide port status data for better logistical planning and security. IoT devices are used to count and control vessel traffic in the research. Sensors and data analytics at important port locations provide real-time vessel tracking and traffic monitoring. This data improves safety and optimizes vessel traffic flow, lowering congestion and wait times. The pros and downsides of using IoT in port operations include data privacy, security, and infrastructure needs. Smart port technologies may enhance marine efficiency, cost savings, and sustainability. The IoT can revolutionize port operations, notably cargo container monitoring and vessel traffic counts. Port capacity, security, and the environmental and economic viability of worldwide trade may all be improved with the help of the IoT. © 2024 IEEE.

关键词： Internet of things

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