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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Thumu, Muni Balaji Balajiraja, N. Yousoof, Muhammed Selvaraju, Sivamani PG and Research Department of Computer Science Pudukkottai India Pudukkottai India Dhofar University Salalah Oman College of Engineering and Technology Univarsity of Technology and Applied Sciences-Salalah Engineering Department Salalah Oman

ISBN: (纸本)9798331540067

Diabetes impacts food-to-energy conversion and is chronic. Diabetics store glucose in their blood instead of using it for energy, causing major health issues. The WHO reports that diabetes is rising worldwide, and early detection and treatment are crucial to preventing complications. Artificial intelligence (AI) and machine learning (ML) techniques have the potential to revolutionize the diagnosis of diabetes by making it possible to identify people at risk earlier and diagnose it more accurately. This work introduces a supervised learning approach in the artificial neural network (ANN) to improve the accuracy of diabetes diagnosis. Eight input variables from the National Institute of Diabetes and Digestive and Kidney Diseases dataset were used, including 2000 patients with 65.8% healthy and 34.2% diabetic. Altering the feedforward backpropagation neural network architecture improved the ANN's accuracy. Changes were made to hidden layers (1 to 3) and neurons (20 to 100) using R2 and MSE. The ANN model suited the dataset well with R2 and MSE of 99.1% and 7.38× 10-6 in the optimal architecture. These results imply that the suggested method might quickly detect diabetes. © 2024 IEEE.

关键词： Neural network models

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Towards Underwater Sustainability with AIoT: Underwater Trash Management System Comprised of YOLOv8 with IoT-applied Segmentation 2

Towards Underwater Sustainability with AIoT: Underwater Tras...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Paneru, Biplov Poudel, Buddhi Sagar Shah, Krishna Bikram Paneru, Bishwash Chhetri, Sanjog Poudel, Yam Krishna Nepal Engineering College Pokhara University Department of Electronics and Communication Bhaktapur Nepal Nepal Engineering College Pokhara University Department of Computer Science and Engineering Bhaktapur Nepal Institute of Engineering Tribhuvan University Department of Applied Science & Chemical Engineering Lalitpur Nepal Nepal Research and Collaboration Center Department of Research and Development Kathmandu Nepal

ISBN: (纸本)9798331517984

Effective techniques for managing rubbish underwater are essential due to growing environmental concerns about contamination under the sea. In this research, we offer a novel method for efficient underwater trash detection and management that makes use of the Internet of Things (IoT) and the YOLOV8 object detection model. Using YOLOV8, the suggested system seeks to identify and categorize underwater garbage in real-time. It then uses IoT devices for segmentation and management. We outline the suggested system's design, talk about how YOLOV8 integrates with Internet of Things gadgets, and provide experimental results to validate the system's efficacy. Our findings suggest that the suggested strategy has great potential to advance efforts to manage undersea waste and manage pollution as well as segment using 6-DOF Robotic Arm. The IoT based system allows remote monitoring of trash density and location along with the raspberry-pi server-based video feed allows the users to suitably monitor aquatic conditions remotely. The fusion of IoT and deep learning on YOLO v8 model opens gateway for water sustainability and enhances further opportunities in employing robotic arm-based trash collection system. © 2024 IEEE.

关键词： Robotic arms

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Automatic classification of normal/AD brain MRI slices using whale-algorithm optimized hybrid image features

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14237-14248页

作者： Kadry, Seifedine Jessy, V. Elizabeth Rajinikanth, Venkatesan Crespo, Rubén González Department of Applied Data Science Noroff University College Kristiansand4612 Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Innovation Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Technology Universidad Internacional de La Rioja La Rioja Logroño Spain

In recent years, the prevalence of Age-Related Illnesses (ARL) has been increasing among older individuals, and early recognition and treatment will result in better living conditions. It is well known that Alzheimer's Disease (AD) is among the ARD, and severe cases may result in dementia as well. It is the purpose of this study to propose a technique for distinguishing normal/AD brain MRI slices with improved accuracy utilizing the T2-modality. This scheme consists following phases: (i) Brain MRI collection and preprocessing, (ii) Deep feature extraction with the chosen scheme, (iii) Handcrafted feature extraction, (iv) Whale Algorithm (WA) based feature reduction and serial integration, and (v) binary classification using five-fold cross-validation. A total of 2000 MRI slices (1000 normal and 1000 AD class) are examined during this task using images collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI). This study confirms that the proposed scheme provides a classification accuracy of > 98% when applied with the K-Nearest Classifier. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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Graph Theoretic Approach to IOT Applications

Graph Theoretic Approach to IOT Applications

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Venkataraman, Yegnanarayanan Angelina, Jeyaraj Jane Rubel Subhashini, Sottallu Janakiram Vadlamudi, Venkata Naveen Nagaraj, P. Kalasalingam Academy of Research and Education Department of Mathematics Virudhunagar Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar Krishnankoil India

ISBN: (纸本)9798350348217

Certain difficulties in the use of computing through fog and edge clubbed with the IoT controllers in the sense of performance and memory size. So, demand increases for computing at higher levels due to the increase in the number of IoT-oriented procedural systems required for negotiating the problem. The transformation from the cloud poses major worries about the recognition of location proper for nodes at the corner and the within distribution of services. It is concerned with the placement of the selected nodes to act as hosts for the security services. This task can be visualized as a graph where vertices stand for the devices and the edges stand for the connections between devices. Direct access from a given vertex to a security vertex implies that chosen vertices become a dominating set of minimal cardinalities and support heavier nodes. Here we discuss some greedy procedures that find for a given graph with allotted weights, a dominating set obeying certain optimization criteria, and the quality of the graph partition solution. © 2023 IEEE.

关键词： Graph theory

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First thrust measurements in ionic multi-propeller rotational engines

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Propulsion and Power research 2023年第1期12卷 44-58页

作者： Adrian Ieta Marius Chirita Electrical and Computer Engineering Department SUNY OswegoOswegoNY 13126-3599USA Applied Physics/Condensed Matter Department National Institute for Research and Development in Electrochemistry and Condensed MatterTimisoaraRO-300224Romania

Rotary ionic engines(RIEs)with multi-coaxial contra-rotating propellers(12.6 cm diameter)and their axial thrust are investigated in laboratory conditions,in air at atmospheric pressure,for the first *** goal is to evaluate more advantageous configurations that may be scaled up and further help with development of rotary ionic *** propellers are designed with regular pin-emitter electrodes placed coaxially inside a cylindrical collector *** high voltage is applied,propellers spin generating conventional axial thrust which is measured with an electronic scale in a“see-saw”*** to 40 mN thrust was obtained in single propeller RIE at 2600 rpm,0.34 mA,37.5 kV,and an optimal collector electrode diameter of 17 *** thrust can be obtained with two and three propellers at constant current per propeller but at a decreased *** the axial propeller-separation showed that propeller-interaction is minimal above 5 ***-to-power and thrust-tocurrent ratios were calculated and *** experimentally confirm here for the first time that within certain limits,the thrust-to-power variation can be assessed by the propeller kinetic energy-to-power ratio and also by the impedance of the gap(voltage-to-current ratio).A comparison of RIE arrays performance with one,two,and three coaxial propellers and the same total number of propellers per array(six)is also *** arrays with multi-coaxial propellers can provide larger thrust densities than single-propeller ***,arrays employing two coaxial propeller unit may be more weight effective.

关键词： Ionic wind EHD pump Electrohydrodynamics Corona discharge Thrust-to-power Propeller Ion propulsion Plasma thrusters Electroaerodynamic thrusters Ionocraft

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Cultivating Cluster Enactment in Wireless Sensor Networks Through Hybrid Metaheuristics Intended for Energy Optimization

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SN computer Science 2025年第3期6卷 1-12页

作者： Kushwaha, Ajay Goswami, Anjali Sharada, K. Sharma, Manmohan Berar, Gagandeep Edara, Deepak Chowdary Department of Computer Science and Engineering Rungta College of Engineering and Technology R1 CG Bhilai India Department of Mathematics and Statistics College of Science and Theoretical Studies Saudi Electronic University Riyadh 13323 Saudi Arabia Department of Computer Science and Engineering GITAM (Deemed to be University) AP Visakhapatnam India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of CSE Vignan’s Foundation for Science Technology and Research Vadlamudi AP Guntur India

Though traditional search methods shine when faced with local goals and a plethora of decision factors, they could struggle when faced with engineering design limitations. On the other hand, metaheuristic approaches promise to find global optima in real-world optimization problems, even in nonlinear and multimodal settings. In order to improve cluster head selection in Wireless Sensor Networks (WSNs), this research presents a new metaheuristic method that combines the principles of Cuckoo Search method (CSA) and Selection by Optimization (SBO). When choosing a Chief Cluster Head (CCH), the CSA algorithm takes hop count into account, whereas SBO finds the best cluster heads by considering communication range. By comparing the suggested approach to both stand-alone SBO and CSA methods, we can see how well it performs in terms of throughput, latency, energy usage, delivery ratio, and packet overhead. The combined SBO-CSA method improves cluster performance, simplifies cluster head selection in WSNs, and increases network efficiency overall, according to the results. To prove its efficacy in enhancing network performance, the suggested solution outperforms standalone SBO and CSA approaches across a range of performance parameters, such as throughput, latency, energy consumption, and packet overhead. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CCH CH CSA Metaheuristic methods SBO

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Correction to: Innovative numerical method for solving heat conduction using subdivision collocation (Journal of applied mathematics and Computing, (2025), 10.1007/s12190-025-02429-9)

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Journal of applied mathematics and Computing 2025年 1-2页

作者： Malik, Safia Tehmina Ejaz, Syeda Rezapour, Shahram Inc, Mustafa Mustafa, Ghulam Department of Mathematics The Government Sadiq College Women University Bahawalpur Bahawalpur Pakistan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Department of Mathematics Firat University Elazig23119 Turkey Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai602105 India Department of Computer Engineering Biruni University Ghulam Mustafa Istanbul34010 Turkey Department of Mathematics The Islamia University of Bahawalpur Bahawalpur63100 Pakistan

Typesetter introduced error in author Ghulam Mustafa affiliation and needs to be correctly read as: department of mathematics, The Islamia University of Bahawalpur, 63,100, Pakistan. Original article has been correcte...

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Temperature Effects on Microalgae Cell Growth in an Outdoor HLTP 2nd

Temperature Effects on Microalgae Cell Growth in an Outdo...

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2nd International Conference on Mathematical Analysis and Applications in Modeling, ICMAAM 2023

作者： Nahar, Nurun Monir, Mohammad Iftekhar Islam, Md. Rashedul Deb, Ujjwal Kumar Department of Mathematics Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Mathematics Premier University Chattogram Bangladesh Jamal Nazrul Islam Research Center for Mathematical and Physical Sciences University of Chittagong Chittagong Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh

ISBN: (纸本)9789819791934

Biofuel from microalgae is a promising alternative that can substitute fossil fuels because they are renewable and eco-friendly. The growth of microalgae is influenced by numerous culture parameters, and temperature is considered one of the vital growth factors among them. In this study, we developed a computational growth model related to irradiance related temperature for an outdoor operated Horizontal Loop Tubular Photobioreactor (HLTP) for a specific geographic location. The effects of direct and diffuse solar radiation on the Photobioreactor (PBR) temperature are considered in this model. The meteorological data associated with the geographic location has been collected for the simulation. We simulated the model by COMSOL Multiphysics and ob-served a temperature fluctuation between 24.85 ∘C and 38.45 ∘C throughout the PBR domain. The significant issue of temperature control is also suggested from this study. Over the simulated period, a uniform cell growth has been observed. The hydrodynamic behaviors of the suspension flow inside the reactor, radial and axial temperature distribution are also analyzed in this study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Microalgae

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Energy-Efficient Cloud Computing Through Reinforcement Learning-Based Workload Scheduling

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 645-656页

作者： Malipatil, Ashwini R Paramasivam, M.E. Gulyamova, Dilfuza Saravanan, Aanandha Ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Ghodhbani, Refka Department of Computer Science and Engineering BNM Institute of Technology Bangalore India Department of ECE Sona College of Technology Salem India Computer Engineering Department University of Information Technologies in Tashkent Tashkent Uzbekistan Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. Data center power consumption, however, has skyrocketed because of demand increases, raising operating costs and their footprint. Traditional workload scheduling algorithms often assign performance and cost priority over energy efficiency. This paper proposes a workload scheduling method utilizing deep reinforcement learning (DRL) that adjusts dynamically according to present cloud situations to ensure optimal energy efficiency without compromising performance. The proposed method utilizes Deep Q-Networks (DQN) to perform feature engineering to identify key workload parameters such as execution time, CPU and memory consumption, and subsequently schedules tasks smartly based on these results. Based on evaluation output, the model brings down the latency to 15 ms and throughput up to 500 tasks/sec with 92% efficiency in load balancing, 95% resource usage, and 97% QoS. The proposed approach yields improved performance in terms of key parameters compared to conventional approaches such as Round Robin, FCFS, and heuristic methods. These findings show how reinforcement learning can significantly enhance the scalability, reliability, and sustainability of cloud environments. Future work will focus on enhancing fault tolerance, incorporating federated learning for decentralized optimization, and testing the model on real-world multi-cloud infrastructures. © (2025), (Science and Information Organization). All Rights Reserved.

关键词： Deep reinforcement learning

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Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

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computer Modeling in engineering & Sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani Farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

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