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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Moorthi, K. Asokan, Anju Sri Sathya, K.B. Chellammal, P. Kalpana, V. Rastogi, Ravi Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chengalpattu India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Tamilnadu Coimbatore India Kpr Institute of Engineering and Technology Computer Science and Engineering Tamilnadu Coimbatore India J J College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Trichy India R. M. K. College of Engineering and Technology Tamilnadu India Nielit Gorakhpur Uttar Pradesh India

ISBN: (纸本)9781665456302

The ability of humans to effectively communicate through the use of hand signs has a wide range of practical applications. People with speech problems around the world have embraced them due to their intuitive design. Around 1% of Indians are in this group, which is a quite high percentage. It is for this reason that the incorporation of a framework familiar with Indian Sign Language would have such a profoundly positive effect on the lives of the people of India. A median filter is used to an input image to remove unnecessary details and improve clarity. Feature extraction is performed using principal component analysis (PCA), and the YCbCr color space is used for hand segmentation. The model is then trained through Regularized Extreme Learning. Using regularization to achieve peak structural performance for precise prediction, RELMs are a subclass of ELMs. This method exceeds popular alternatives like the support vector machine (SVM), Extreme Learning Machine (ELM), and CNN in terms of accuracy (around 97.8%). © 2023 IEEE.

关键词： Principal component analysis

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M-VMT-PC: Metaverse-Enabled Virtual Medical Training and Patient Care Framework Underlying 5G

M-VMT-PC: Metaverse-Enabled Virtual Medical Training and Pat...

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2024 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Kumari, Aparna Obaidat, Mohammad Panchal, Shikhar Raval, Nikhil Kumar, Prasun Kumar Yadav, Dileep Nirma University Department of CSE Ahmedabad India University of Science and Technology Beijing China School of Computing SRM University Department of Computational Intelligence SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Nirma University Department of Computer Science and Engineering Ahmedabad India SSET Sharda University Department of Biotechnology Greater Noida India Bennett University Department of Computer Science and Engineering SCSET Greater Noida India

ISBN: (纸本)9798350349832

As technology advances, merging virtual environments, blockchain, and 5G networks opens new possibilities, especially in medical training. This paper introduces a framework that combines these technologies to create an innovative medical training system. In this paper, we proposes Metaverse-enabled Virtual Medical Training and Patient Care framework, i.e., M-VMT-PC. In the proposed framework, metaverse comprises a dig-ital space where users can interact in 3D, offers exciting opportunities for medical training. It allows for realistic and interactive simulations, where trainees can practice medical scenarios and procedures in a virtual setting. More, the M-VMT-PC uses blockchain technology within this framework to ensure that the data is securely stored, enhancing its reliability. Next, 5G technology is used to make this framework effective. With ultra-reliable, low-latency communication (uRLLC) and high-speed data transfer (eMBB), 5G supports real-Time interactions and smoothly handles large volumes of data. This technology ensures the training environment is responsive and can handle extensive data needs, essential for ongoing updates and improvements. Our proposed framework M-VMT-PC addresses the current challenges in medical training by providing a scalable, secure, and engaging learning environment. It improves the medical professionals training and sets the stage for future advancements in medical education and patient care. © 2024 IEEE.

关键词： Virtual environments

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Enhancing identity and access management using Hyperledger Fabric and OAuth 2.0: A block-chain-based approach for security and scalability for healthcare industry

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 49-67页

作者： Sutradhar, Shrabani Karforma, Sunil Bose, Rajesh Roy, Sandip Djebali, Sonia Bhattacharyya, Debnath Department of Computational Sciences Brainware University West Bengal India Department of Computer Sciences The University of Burdwan West Bengal India Department of Computer Science & Engineering JIS University West Bengal India Pôle universitaire Léonard De Vinci Research Center Paris La Défense 92 916 France Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh522302 India

Block-chain-based Identity and access management framework is a promising solution to privacy and security issues raised during the exchange of patient data in the healthcare industry. This technology ensures the confidentiality and integrity of sensitive information by providing a decentralized and immutable ledger. In our research, we propose an identity and access management system that employs Hyper-ledger Fabric and OAuth 2.0 for improved security and scalability. This combination allows for transparency and immutability of user transactions and minimizes the risk of fraud and unauthorized access. Additionally, Hyper-ledger Fabric's privacy, security, and scalability features enable granular access control to sensitive information, while OAuth 2.0 authorizes only trusted third-party applications to access specific data on the Fabric network. The proposed approach can handle large volumes of data and support multiple applications, thus providing a secure and scalable solution for managing access to the Fabric network. Moreover, our solution employs Role-based access control based on the patient's role, ensuring privacy and confidentiality. Our statistical analysis demonstrates that the proposed approach can efficiently and securely manage patient identity and access, potentially transforming the healthcare industry by enhancing data interoperability, reducing fraud and errors, and improving patient privacy and security. Furthermore, our solution can facilitate compliance with regulatory requirements such as HIPAA and GDPR. © 2023 The Authors

关键词： Scalability

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Game Strategies for Data Transfer Infrastructures Against ML-Profile Exploits

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 925-938页

作者： Rao, Nageswara S. V. Ma, Chris Y. T. He, Fei Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak RidgeTN37831 United States The Hang Seng University of Hong Kong Department of Computer Science Siu Lek Yuen Hong Kong Texas A&M University-Kingsville Department of Mechanical and Industrial Engineering KingsvilleTX78363 United States

Data transfer infrastructures composed of Data Transfer Nodes (DTN) are critical to meeting distributed computing and storage demands of clouds, data repositories, and complexes of supercomputers and instruments. The infrastructure's throughput profile, estimated as a function of the connection round trip time using Machine Learning (ML) methods, is an indicator of its operational state, and has been utilized for monitoring, diagnosis and optimization purposes. We show that the inherent statistical variations and precision of throughput profiles estimated by ML methods can be exploited for unauthorized use of DTNs' computing and network capacity. We present a game theoretic formulation that captures the cost-benefit trade-offs between an attacker that attempts to hide under the profile's statistical variations and a provider that attempts to balance compromise detection with the cost of throughput measurements. The Nash equilibrium conditions adapted to this game provide qualitative insights and bounds for the success probabilities of the attacker and provider, by utilizing the generalization equation of ML-estimate. We present experimental results that illustrate this game wherein a significant portion of DTN computing capacity is compromised without being detected by an attacker that exploits the ML estimate properties. © 2023 CCBY.

关键词： Data transfer

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Practical Phase Retrieval Using Double Deep Image Priors 21

Practical Phase Retrieval Using Double Deep Image Priors

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IS and T International Symposium on Electronic Imaging: 21st computational Imaging, COIMG 2023

作者： Zhuang, Zhong Yang, David Hofmann, Felix Barmherzig, David A. Sun, Ju Electrical & Computer Engineering University of Minnesota Minneapolis United States Department of Engineering Science University of Oxford Oxford United Kingdom Center for Computational Mathematics Flatiron Institute New York City United States Computer Science & Engineering University of Minnesota Minneapolis United States

Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes. We identify the connection between the difficulty level and the number and variety of symmetries in PR problems. We focus on the most difficult far-field PR (FFPR), and propose a novel method using double deep image priors. In realistic evaluation, our method outperforms all competing methods by large margins. As a single-instance method, our method requires no training data and minimal hyperparameter tuning, and hence enjoys good practicality. Our paper is also available at: https: // arxiv. org/ abs/ 2211. 00799 . © 2023, Society for Imaging science and Technology.

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Crop Monitoring System with Water Moisture Level using Arduino 5

Crop Monitoring System with Water Moisture Level using Ardui...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Sowmia, K.R. Prithi, S. Vijay, K. Berna, I. Eugene Bhuvaneswaran, B. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computational Intelligence School of Computing Srm Ist Kattankulathur Campus India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9781665474672

Many factors-including sunlight, water, soil, and pest control-need to be considered when cultivating crops. The farmer has other responsibilities besides keeping an eye on the seedlings. In this way, the productivity of farming is enhanced by a machine that tends to all the plants with minimal human intervention. The amount of water a plant receives should be proportional to the amount of sunlight it gets. This research work employs the idea of IoT to connect the monitored plant to the network and transmit data. The microcontroller is an Arduino UNO, and components include a photoresistor for light detection and a moisture sensor for measuring the amount of moisture in the soil. The information is transmitted over a network, and the result is viewed using a mobile application. The data that has been collected is displayed in the app. It warns the user when the plant is in danger of drying out due to a lack of water or sunlight. If the crop is dry, a motordriven water pump is used to saturate the soil, and if it's too wet, a motor-operated shutter opens to let the water out. This project intends to make farming as mechanized and labor-free as possible. © 2023 IEEE.

关键词： Crops

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Actual Facial Mask Recognition Utilizing YOLOv3 and Regions with Convolutional Neural Networks 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Thilagavathy, A. Naveen Raju, D. Priyanka, S. RamBalaji, G. Gopirajan, P.V. Sureshkumar, K. Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India Department of Information Technology Saveetha Engineering College Chennai India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

With the Coronavirus Disease (COVID-19) outbreak in 2019, it appears as though time has stopped. The World Health Organization (WHO) determined that wearing a face mask was essential for limiting virus transmission to combat pathogen transmission. Nevertheless, physically choosing how often individuals wear face masks in public is a momentary operation. The need to track people using respirators required creating an automated process. Nowadays, different deep and machine learning techniques can be used productively. All of the requirements for this type of model have been addressed in this work. The necessity and architectural layout of the suggested method have been thoroughly examined, followed by a rigorous inspection of several methodologies and their related comparison analyses. We investigate optimum settings for the sequential convolutional neural network model to accurately identify the existence of masks avoiding overfitting. Face identification categorically deals with distinguishing a specific group of entities, i.e., face. It has numerous applications, such as autonomous driving, education, surveillance. The prevalent practice reconfigures the images before fitting them into the network to surmount the inhibition. Some face collections are head turn, tilt, and slant with multiple faces in the frame and different types of masks with different colors. The proposed method consists of a cascade classifier and a pre-trained Convolutional Neural Network (CNN), which contains two 2D convolution layers connected to layers of dense neurons. As grayscale rationalizes the algorithm and diminishes the computational requisites, it is utilized for extracting descriptors instead of working on color images instantaneously. It has categorical representation, and the data is converted to categorical labels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Tackling the Qubit Mapping Problem with Permutation-Aware Synthesis 4

Tackling the Qubit Mapping Problem with Permutation-Aware Sy...

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Liu, Ji Younis, Ed Weiden, Mathias Hovland, Paul Kubiatowicz, John Iancu, Costin Argonne National Laboratory Mathematics and Computer Science Division United States Lawrence Berkeley National Laboratory Computational Research Division United States University of California Department of Electrical Engineering and Computer Science Berkeley United States

ISBN: (纸本)9798350343236

We propose a scalable, hierarchical qubit mapping and routing algorithm that harnesses the power of circuit synthesis. First, we decompose large circuits into subcircuits small enough to be directly resynthesized. For each block, we pre-synthesize them for all permutations of its input and output qubits. Following this offline step, we employ a permutation-aware, block-based generalization of the popular SABRE mapping algorithm. This mapping step stitches together blocks by choosing an input-output permutation that minimizes intrablock gate count and required inter-block communication (SWAP and bridge gates). Our approach has a twofold advantage: 1) circuit synthesis may eliminate more two-qubit gates than other optimizing compilers;2) considering all permutations of input and output qubits eliminates communication operations transparently. In contrast, other mapping algorithms can only introduce communication operations. We show that we can produce better-quality circuits than commercial compilers: shorter by up to 68% (18% on average) fewer gates than Qiskit, up to 36% (9% on average) fewer gates than Tket. We outperform BQSkit, a permutation-unaware, synthesis-based compiler, by up to 67% (21% on average) fewer gates. We also exceed experimental optimal mappers such as OLSQ in quality (10.7% shorter circuits) and time to solution. Our scalable, heuristic approach can be seamlessly integrated into any quantum circuit compiler or optimization infrastructure, and it applies well to any qubit technology, such as superconducting and trapped ions. © 2023 IEEE.

关键词： Conformal mapping

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Design of a sequestration-based network with tunable pulsing dynamics 63

Design of a sequestration-based network with tunable pulsing...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Nakamura, Eiji Samaniego, Christian Cuba Blanchini, Franco Giordano, Giulia Franco, Elisa University of California Department of Mechanical and Aerospace Engineering Los AngelesCA United States Carnegie Mellon University Department of Computational Biology PittsburghPA United States University of Udine Department of Mathematics Computer Science and Physics Italy University of Trento Department of Industrial Engineering Italy

ISBN: (纸本)9798350316339

Incoherent feedforward networks exhibit the ability to generate temporal pulse behavior. However, exerting control over specific dynamic properties, such as amplitude and rise time, poses a challenge and is intricately tied to the network's implementation. In this study, we focus on analyzing sequestration-based networks capable of exhibiting pulse behavior. By employing time-scale separation in the fast sequestration regime, we approximate the temporal dynamics of these networks. This approach allows us to establish a mapping that elucidates the impact of varying the kinetic rates and pulse specifications, including amplitude and rise time. Furthermore, we introduce a positive feedback mechanism to regulate the amplitude of the pulsing response. © 2024 IEEE.

关键词： Time varying networks

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