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IEEE International Conference on systems, Man and Cybernetics

作者： Tyree Lewis Rupal Agarwal Marvin Andujar Department of Computer Science and Engineering University of South Florida University of South Florida United States Department of Computer Science and Engineering Tampa FL USA Faculty of Computer Science and Engineering Tampa FL USA

The rise of security concerns has spurred on-going research into Brain-computer Interfaces (BCI) based authentication. These applications utilize electroencephalogram (EEG) signals, due to their properties that can enhance security systems. In previous studies, EEG data has been incorporated into various authentication systems to compare the performance of new and existing classification methods. However, using EEG data to compare the performance of distance metrics in a P300-based BCI authentication system has not been explored yet. In this study, EEG data is used to determine the most effective distance metric for authenticating users in a closed-loop system. To accomplish this task, we conducted a longitudinal study to evaluate three distance metrics (Cosine, Correlation and Chebyshev) while participants interacted with our BCI authentication system. Our results indicated that the Cosine similarity outperformed all other distance metrics for each user.

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Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

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computers, Materials & Continua 2022年第12期73卷 5473-5489页

作者： Mesfer Al Duhayyim Areej A.Malibari Marwa Obayya Mohamed K.Nour Ahmed S.Salama Mohamed I.Eldesouki Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important *** typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or *** BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical ***,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(ISBAAC-MDL)*** presented ISBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification *** attain this,the presented ISBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract *** by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet *** last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is *** experimental evaluation of the ISBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct *** experimental outcomes reported the better performances of the ISBAACMDL model over recent approaches with maximum accuracy of 0.9920.

关键词： Biomedical images bone age assessment age prediction computer vision deep learning image classification

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Continuous Output Feedback Stabilization for a Class of Nonlinear systems with Unknown Measurement Sensitivity 42

Continuous Output Feedback Stabilization for a Class of Nonl...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Daoyuan Lin, Dong Guangzhou Xinhua University Department of Artificial Intelligence and Data Science Guangdong Guangzhou510520 China Liaoning Technical University Faculty of Electrical and Control Engineering Liaoning Huludao125100 China

ISBN: (纸本)9789887581543

This paper addresses the problems of output feedback stabilization for a class of nonlinear systems with a kind of truncation protocol. Based on the sampled measurements, an output feedback controller is proposed to globally stabilize the nonlinear systems. Then, sufficient conditions are given to ensure that the estimation errors are globally uniformly ultimately bounded. At last, an illustrative example is given to show the effectiveness of the proposed methods. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Nonlinear systems

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Prototype Proposal of an 18 kW Non-Isolated Bidirectional Converter for Battery Energy Storage System

Prototype Proposal of an 18 kW Non-Isolated Bidirectional Co...

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Power Electronics (Ee)

作者： Andrej Brandis Kristian Knol Denis Pelin Danijel Topić Department of Electromechanical Engineering Faculty of Electricity Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Power Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

In this paper, a brief overview of converter topologies used in stationary battery energy storage systems is given. A simulation model of converter was developed in MATLAB Simulink. A simulation is conducted to provide a preliminary understanding of the converter's bidirectional nature. The results indicate that the state of charge (SOC) increases during battery charging and decreases during discharging. The topology of non-isolated two-stage bidirectional converter is presented for proposed real-world prototype, including the high-level electrical schematic and mechanical layout. An 18-kW prototype converter for stationary battery energy storage systems was developed and presented. The converter housing with all installed ancillary components for proper operation is also described. At the time of writing this paper, the actual converter is still in the development phase.

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An Unsupervised Machine Learning Approach for Heart Disease Prediction

An Unsupervised Machine Learning Approach for Heart Disease ...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Wing Son Loh Yu Jiun Lim Lloyd Ling Ren Jie Chin Chen Kang Lee Ming Jie Lee Department of Mathematical and Engineering Sciences Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia Department of Civil Engineering Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia Department of Internet Engineering and Computer Science Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Cardiovascular diseases (CVDs) persist as a primary cause of mortality on a global scale, necessitating effective prediction methods. This study presents a novel modelling approach utilizing the Self-Organizing Map (SOM), an unsupervised machine learning approach, for CVD prediction. The SOM model employed to analyze and cluster patient data based on intrinsic patterns without predefined labels, enabling the identification of high-risk individuals. Results demonstrated that the SOM model effectively differentiates between healthy and at-risk patients, offering a robust approach for early detection. The SOM presented to be the best predictive model for the CVD data prediction with the highest accuracy (84.44%); precision (89.13%); recall (82.00%); F1-score (85.42%). The U-Matrix of SOM as a visualization tool provided insightful representations that highlighted two discernible clusters, effectively illustrating the health status of individuals with similar health conditions concerning CVD. This facilitates a better understanding and management of CVD. The research findings suggested the potential of integrating SOM into clinical workflows, offering a potent tool for healthcare professionals in the fight against CVD.

关键词： Self-organizing feature maps Accuracy Machine learning Predictive models Prediction algorithms Data models Robustness Particle swarm optimization Optimization Genetic algorithms

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Mobile Medical Assistant System for Laboratory Report Analysis and Medical Drug Identification 4

Mobile Medical Assistant System for Laboratory Report Analys...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Warnakulasuriya, Deshani Dewangi, Tharushi Sewwandi, Navodya Rathnayake, Minoli Kodagoda, Nuwan Suriyawansha, Kushanra Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

It is quite common for medical drugs and prescriptions to be misidentified by hospitals and after drugs are being dispensed to the patients. Misidentification of medical drugs is more common among elderly and visually impaired patients. In hospital organizations, the leading medical error is adverse drug events. Another most common issue patients face is keeping track of medical lab reports. Our proposed mobile medical assistant system uses image processing to identify drugs with or without packaging, identifying prescription and medical lab reports. Furthermore, the mobile application will identify the trends of medical lab reports and predict next month's results of the medical lab report of the patient using machine learning. © 2022 IEEE.

关键词： Hospitals

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Acquisition and Monitoring of Environmental Parameters from High-altitude UAV Flight

Acquisition and Monitoring of Environmental Parameters from ...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Marius Dima Dan Popescu Loretta Ichim Petrișor Valentin Pârvu Faculty of Automatic Control and Computer Science National University of Science and Technology Politehnica Bucharest Bucharest Romania Faculty of Aerospace Engineering National University of Science and Technology Politehnica Bucharest Bucharest Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Unmanned aerial vehicles can improve short- term weather forecasting by acquiring information from weather sensors and other sensors. With this information, there is the possibility of making relevant maps like solar radiation, pollen, emissions, particles, and others. Another advantage of this acquisition system is the high rate of flights, compared to the classic measurements made with the weather balloon, which is launched twice a day. In addition to the advantages listed above, we discuss the multiplication factor of the acquired data, these systems being able to operate in various geographical locations.

关键词： Weather forecasting Data integration Autonomous aerial vehicles Prediction algorithms Particle measurements control systems Sensors Solar radiation Monitoring Payloads

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Bioinformatics Analysis of Oral Squamous Cell Carcinomas and Their Interaction to Identify Molecular Signatures 1st

Bioinformatics Analysis of Oral Squamous Cell Carcinomas and...

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1st International Symposium on Artificial Intelligence, ISAI 2022

作者： Sarker, Bandhan Rahaman, Md. Matiur Khan, Suman Sinha, Jayashri Deb Barman, Subhabrata Department of Statistics Faculty of Science Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj-8100 Dhaka Bangladesh Department of Computer Science and Engineering Bengal Institute of Technology and Management West Bengal Santiniketan721657 India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia721657 India

ISBN: (纸本)9783031224843

Squamous epithelium is the origin of solid tumor oral squamous cell carcinoma (OSCC). Every year, nearly 400,000 OSCC patients are added to the cancer database. Presently, chemo-radiotherapy is the main important adjuvant treatment for OSCC;nevertheless, clinical resistance (drug resistance) to chemotherapy still leads to a poor prognosis of OSCC patients. Identification of potential genes and drugs might be a significant lead for the analyses of OSCC research. We aimed to identify molecular signatures for the diagnosis of OSCC patients. Statistical methods (ANOVA, limma and SAM) were used to identify differentially expressed genes (DEGs) from two datasets GSE111585 and GSE115119. Considering the cutoff values less than 0.05 and |log FC| greater than 1, we obtained 27 up-regulated and 25 down-regulated common DEGs. Protein-protein interaction (PPI) network determined hub genes (AR, ETS1, MET, PDGFB and VAV3) using STRING database. Other biomolecules: Reporter transcription factors (HIF1A, MYC, FOXP3, E2F4, WT1, PURA, ZEB1 and USF2), microRNAs (hsa-miR-589-3p, hsa-miR-155-5p and hsa-miR-301b-3p) associated with hub-genes were determined. For the hub genes, we also performed GO and KEGG enrichment analysis. We constructed gene-drug interaction using the DGIdb database and identify targeted drugs (IMATINIB, BROMOCRIPTINE, NIFEDIPINE, PIRETANIDE and TRIAMTERENE) for OSCC. Our study suggested that the biomarkers (hub-genes, TFs and miRNAs) and the discovered drugs might be a therapeutic target of OSCC. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Transcription factors

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule 4

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Altigani, Abdelrahman Ghalib, Muhammad Rukunuddin Elshoush, Huwaida T. Elsadig, Muawia A. Alrefai, Nashat Naserelden, Shiraz Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates School of Engineering & Computing De Monfort University Dubai United Arab Emirates University of Khartoum Faculty of Mathematical Sciences and Informatics Computer Science Department Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam Saudi Arabia College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Department of Mathematics Dammam Saudi Arabia Faculty of Science Universiti Teknologi Malaysia Department of Mathematical Sciences Johor Malaysia

ISBN: (纸本)9798350395914

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption. © 2024 IEEE.

关键词： Ciphertext

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IoT-Based Smart Farmland Using Deep Learning

IoT-Based Smart Farmland Using Deep Learning

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Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： J Uma M Shobana G Abhiniti R Rahul S P Kavinkumar S Hirithik Roshan Electrical and Electronics Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

The IoT based Smart farmland using deep learning is a system for tracking animals on agricultural land combines surveillance cameras, drones, an Arduino controller, IR sensors, and an LCD display to detect and tally the animal's presence. Upon detection, the system utilizes AI methods to categorize the animal type and send immediate SMS notifications to farmers through a GSM module. It incorporates an animal-repellant buzzer and a gate control mechanism. Through the use of transfer learning with CNN models, it accurately identifies four specific animal species: elephants, cows, goats, and pigs. Continuous updates and training with new data maintain its precision, serving the agricultural and wildlife protection domains. This technology significantly aids farmers in safeguarding their crops, offering crucial insights into intrusions and amplifying crop yield. Its primary purpose revolves around simplifying the identification of animals on agricultural land.

关键词： GSM Training Smart agriculture Surveillance Wildlife Transfer learning Logic gates

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