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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： K. Sundravadivelu P. Gururama Senthilvel N. Duraimutharasan Hannah Rose Esther T Rajesh Kumar. K Department of Computer Science School of Information Technology Madurai Kamaraj University Madurai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of Information Technology AMET University Kanathur Tamil Nadu Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai Department of Electronics and Communication Engineering Chendu College of Engineering Madurantakam Tamil Nadu India

A person with normal vision can readily read and differentiate between different banknotes, while a person with visual impairment or blindness would have a far more difficult time doing the same. Any person who is blind or visually impaired must have the ability to recognize and identify banknotes in real time since money is so central to our daily lives and is necessary for any business transaction. To do this, deep learning systems were integrated with the Internet of Things (IoT) model. In particular, this study has investigated the feasibility of applying pre-trained deep learning models, namely CNN and CNNXGB for currency categorization and fake currency detection. Pre-trained deep learning models perform well as they require less data to run when compared to newly-trained models. After testing the method on about 4002 photos representing four different denominations of Indian rupees (10, 50, 100, and 500), this study found that the proposed method has performed well. Deep Learning (DL) has recently shown remarkable performance in solving the image classification challenges. Several performance metrics are analyzed to determine the accuracy of the proposed method. The experimental findings result in a training accuracy of 97.12% and a validation accuracy of 96.34%.

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Hybrid WDM and Optical-CDMA over Multi-Mode Fiber Transmission System based on Optical Vortex 5

Hybrid WDM and Optical-CDMA over Multi-Mode Fiber Transmissi...

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5th International Conference on Electronic Design, ICED 2020

作者： Ghazi, Alaan Aljunid, S.A. Zulkarnain Syed Idrus, Syed Endut, R. Rashidi, C.B.M. Ali, N. Al-Dawoodi, Aras Fakhrudeen, Ahmed M. Fareed, Alaa Sharma, Teena School of Human Development and Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Networks Department College of Computer Science and Information Technology Kirkuk University Iraq Computer Technical Engineering Alkitab University Iraq School of Computing University Utara Malaysia Malaysia Centre of Excellence Universiti Malaysia Perlis Malaysia Universiti Malaysia Perlis Perlis Malaysia Department of Electronics and Communication Engineering Malviya National Institute of Technology Jaipur India

The optical vortex has recently attracted scholars to implement it in optical tweezers, microscopy, optical communications, quantum information processing, optical trapping, and laser machining. Optical vortex beam applied classically that can be transferred to the transverse amplitude of a heralded single-photon, and optical vortex possesses a helical wavefront and carries orbital angular momentum. In this study, Optical vortex is applied in optical-CDMA (optical code-division multiple-access) in conjunction with WDM (wavelength division multiplexing). This mechanism aims to increase the capacity and security in optical communication significantly. The implementation of Laguerre-Gaussian (LG) modes with optical vortex based on one dimension zero cross-correlation (ZCC) code shows that mode coupling reduces effectively. Consequently, a positive increase in channel performance and response. Accordingly, the LG modes based on the 1D-ZCC code are investigated and propagated over multi-mode fiber (MMF) based on an optical vortex, which also substantially reduces channel effects. Consequently, all these attributes combined will result in a hybrid WDM-Optical-CDMA with an optical vortex system over MMF. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Optical tweezers

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Leveraging Machine Learning Algorithms to Optimize Diet Maintenance Based on User Food Intake and Fitness data

Leveraging Machine Learning Algorithms to Optimize Diet Main...

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Sustainable Communication networks and Application (ICSCNA), International Conference on

作者： K. Clarinda N. Deepa R. Dhamotharan R. Santhana Krishnan J. Relin Fransis Raj J. V. Anchitaalagammai Department of Management Studies SCAD College of Engineering and Technology Cheranmahadevi India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The increasing prevalence of lifestyle-related health issues has necessitated the development of innovative approaches to diet management. This research introduces the Adaptive Eating Pattern Predictor (AEPP), a novel machine learning algorithm designed to optimize diet maintenance by analyzing user food intake and fitness data. The AEPP combines the predictive capabilities of Artificial Neural networks (ANN) with the probabilistic modeling of Markov Chains, enabling accurate forecasting of future eating patterns based on historical dietary and activity data. A comprehensive simulation analysis was conducted to evaluate the performance of the AEPP against existing algorithms, including traditional recommendation systems and basic machine learning models. Various simulation metrics, such as accuracy, precision, recall, and F1 score, were employed to compare the effectiveness of the AEPP in providing personalized dietary recommendations. The results demonstrate that the AEPP significantly outperforms existing algorithms, highlighting its potential as a valuable tool for individuals seeking to maintain a balanced diet in alignment with their fitness goals. This study contributes to the field of nutritional science by showcasing the advantages of integrating advanced machine learning techniques in personal diet management.

关键词： Adaptation models Machine learning algorithms Accuracy Adaptive systems Collaboration Artificial neural networks Predictive models Prediction algorithms Maintenance Wearable devices

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Cloud-Powered Healthcare Appointment Optimization with Reinforcement Learning for Efficiency

Cloud-Powered Healthcare Appointment Optimization with Reinf...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： S. Mohan Kumar John Benito Jesudasan Peter S. Kolangiammal Azath Mubarakali S. Karthik S. Sujatha Professor and Director Research and Innovations CMR University Bangalore India Cyber | Data Science & Engineering Capital One Services LLC Richmond Virginia USA Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India College of Computer Science King Khalid University Saudi Arabia Department of Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Improving efficiency and patient satisfaction through better appointment scheduling is a problem for healthcare systems across the globe. To improve healthcare appointment scheduling procedures, this research proposes a new method that makes use of cloud computing and reinforcement learning (RL) algorithms. to maximize healthcare provider efficiency while minimizing patient wait times, resource usage, and operational costs by dynamically learning and adapting scheduling rules using RL, taking use of the scalability and computing capacity of cloud infrastructure. To train RL agents, create a simulation environment that mimics real-world healthcare conditions. Our findings show that RL-based scheduling strategies are more efficient at scheduling appointments than conventional techniques, and we prove it via rigorous testing and assessment. Additionally, we demonstrate how our strategy can handle dynamic healthcare contexts with different patient numbers and resource restrictions, demonstrating its flexibility and resilience. The results show that RL approaches run by the cloud can change healthcare appointment scheduling for the better, leading to healthcare systems that are more responsive and flexible. Utilizing the complementary strengths of cloud computing and RL, our method provides a data-driven, scalable solution to the hard problem of healthcare schedule optimization, which improves both patient care and operational efficiency

关键词： Cloud computing Technological innovation Schedules Job shop scheduling Processor scheduling Medical services Optimal scheduling Reinforcement learning Dynamic scheduling Testing

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Improving Mall Traffic Flow with IoT and Reinforcement Learning Techniques in Footfall Analytics

Improving Mall Traffic Flow with IoT and Reinforcement Learn...

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Visual Analytics and data Visualization (ICVADV), International Conference on

作者： S. Muthumarilakshmi A. Sairam N. Mohankumar D. Hemalatha S. Velmurugan T. R. GaneshBabu Department of CSE Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Artificial Intelligence & Data Science Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering T.J.S. Engineering College Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

An innovative method for improving mall traffic flow utilizing IoT devices and reinforcement learning (RL) methods in footfall analytics is presented. Inefficiencies, congestion, and less-than-ideal consumer experiences are common outcomes of the conventional wisdom about managing mall foot traffic. Our proposed system uses Internet of Things (IoT) sensors to track shoppers' flow and density in real time. This data is then input into an RL model to optimize and predict traffic flow patterns. Aiming to enhance the shopping experience and distribute foot traffic more equitably, the system automatically adjusts parameters, including digital signage, escalator functioning, and store promotions. Simulation and pilot deployment results show substantial improvement in lowering congestion and boosting mall visitors' time spent there. By integrating IoT with powerful machine learning (ML) algorithms, this integrated technique demonstrates how smarter and more responsive retail environments may be created. IoT and RL improves mall traffic flow by analyzing footfall trends, optimizing routing, mitigating congestion, and enhancing customer experience.

关键词： Technological innovation Smart cities Visual analytics Scalability Reinforcement learning Routing Real-time systems Sensor systems Internet of Things Intelligent sensors

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Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment

arXiv

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arXiv 2023年

作者： Li, Gongyang Liu, Zhi Zhang, Xinpeng Lin, Weisi Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of Communication and Information Engineering Shanghai University Shanghai200444 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Recently, relying on convolutional neural networks (CNNs), many methods for salient object detection in optical remote sensing images (ORSI-SOD) are proposed. However, most methods ignore the huge parameters and computational cost brought by CNNs, and only a few pay attention to the portability and mobility. To facilitate practical applications, in this paper, we propose a novel lightweight network for ORSI-SOD based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, an edge self-alignment module (ESAM) for low-level features, and a portable decoder for inference. First, the high-level features are compressed into semantic kernels. Then, semantic kernels are used to activate salient object locations in two groups of high-level features through dynamic convolution operations in DSMM. Meanwhile, in ESAM, cross-scale edge information extracted from two groups of low-level features is self-aligned through L2 loss and used for detail enhancement. Finally, starting from the highest-level features, the decoder infers salient objects based on the accurate locations and fine details contained in the outputs of the two modules. Extensive experiments on two public datasets demonstrate that our lightweight SeaNet not only outperforms most state-of-the-art lightweight methods but also yields comparable accuracy with state-of-the-art conventional methods, while having only 2.76M parameters and running with 1.7G FLOPs for 288×288 inputs. Our code and results are available at https://***/MathLee/SeaNet. © 2023, CC BY-NC-SA.

关键词： Optical remote sensing

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Detection of Speech Signal for Speaker Recognition in Biometric Systems Using Optical Scanning Holography with Double Random Phase Encoding

Detection of Speech Signal for Speaker Recognition in Biomet...

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Mobile networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Mohammed I. Habelalmateen Jagadevi N. Kalshetty Cindhamani. J G. M. Gayathri S. Kanmani Jebaseeli Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai India Department of Humanities Bannari Amman Institute of Technology Erode India Department of Information Technology New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In recent years, the speaker recognition technology is significant system for securing the biometric authentication and enabling remote logins for telephone banking and enterprise applications. Whereas Speech Signal Detection ensure with accurate speaker identification and verification for restricted access and sensitive data protection. The combination of speaker recognition technology with speech signal detection has accompanied in a new era of biometric authentication, providing unparalleled security and accuracy in verifying identities. However, Vulnerability to voice spoofing attacks leads to imitation of speaker such as mimicry or synthetic speech, compromise system security. To overcome these drawbacks, an Optical Scanning Holography with Double Random Phase Encoding (OSH-DRPE) is proposed using Texas Instruments Massachusetts institute of Technology (TIMIT) dataset. After collecting the data, the data is preprocessed through normalization approaches for normalize the data. Then, a Mel-Frequency Cepstral Coefficients (MFCC) is employed for feature extraction to extract relevant features, which produces low computational complexity. At last, OSH-DRPE is used as classifier to detect speech signal and speaker recognition with higher accuracy, efficient computation and flexibility. From the results, it clearly shows that the proposed OSH-DRPE obtained higher values in the context of precision, F1 score and accuracy when compared to existing Hidden Markov Model (HMM).

关键词： Accuracy Biomedical optical imaging Hidden Markov models Speech recognition Holography Feature extraction Holographic optical components Speaker recognition Mel frequency cepstral coefficient Signal detection

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft Computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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DeepMind Trainer: Harnessing Cognitive Neural networks on Raspberry Pi for Advanced Cognitive Enhancement Games

DeepMind Trainer: Harnessing Cognitive Neural Networks on Ra...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： P. Abirami D. Antony Joseph Rajan T. Vivekanandan V. Saillaja N. Mohankumar M. Rajmohan Department of Artificial Intelligence and Data Science Velammal Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

The presentation summarizes a groundbreaking cognitive training program designed specifically for those who suffer from cognitive impairments or neurodevelopmental abnormalities. The work creates a dynamic platform for immersive and adaptable cognitive training games by combining cutting-edge Convolutional Neural networks (CNNs) with Raspberry Pi technology. Memory, focus, and ability to think creatively are all qualities directly targeted by these games. The CNN model, trained on a broad dataset, interprets real-time user inputs, delivering quick feedback and altering game complexity depending on an individual’s performance. The incorporation of Raspberry Pi guarantees a solution that is both affordable and easily accessible. The capacity of the system to redefine cognitive rehabilitation by providing a solution that is individualizable, interactive, and broadly deployable is the potential source of the system’s influence. The goal of the work is to make a substantial contribution to the field, which will eventually be to the advantage of users, caregivers, and healthcare professionals and ultimately improve the cognitive well-being of those coping with cognitive issues.

关键词： Training Psychology Medical treatment Games Software Real-time systems Complexity theory

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Deep Reinforcement Learning Based Scheduling Strategy in Blockchain Payment Channel networks

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IEEE/ACM Transactions on Networking 2024年

作者： Ren, Zhe Wang, Zihao Li, Xinghua Miao, Yinbin Li, Zhuowen Liu, Ximeng Han, Lei Deng, Robert H. Xidian University State Key Laboratory of Integrated Services Networks The School of Cyber Engineering Xi'an710071 China Ministry of Education Engineering Research Center of Big Data Security Xi'an710071 China Fuzhou University College of Mathematics and Computer Science Fuzhou350108 China Fuzhou University Fujian Provincial Key Laboratory of Information Security of Network Systems Fuzhou350116 China Beijing Institute of Computer Technology and Application Beijing100000 China Singapore Management University School of Information Systems Singapore178902 Singapore

With the popularity of blockchains, low transaction throughput has become a significant bottleneck in applications such as cryptocurrencies. Payment channel networks (PCNs) have received attention as a way to improve throughput. However, due to the difficulty of predicting future transactions for nodes, the transactions are prone to failure when the channel balances do not meet required conditions. It has been shown that increasing buffers (queues) in PCNs can increase the success rate of transactions and throughput. Nevertheless, there is no effective transaction scheduling strategy in buffers when transaction values are flexible and variable. To solve this problem, we first formulate the Scheduling Problem in PCNs (named PSP), and then prove it is NP-hard. We design a neural network solver based on the Sequence to Sequence (Seq2Seq) architecture and train the solver using the reinforcement learning method. With the solver, we first give two scheduling strategies to maximize transaction throughput, and then design a PCN simulator for performance evaluation. Extensive experiments are conducted to show the superiority and various performances of our proposal and illustrate that our proposal can get a significant advantage in terms of the transaction throughput compared to the existing works. © 2024 IEEE.

关键词： Deep reinforcement learning

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