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Green Industrial electronics and Sustainab.e.Technologies (GIeST), Ieee International Conference on

作者： Thirumangai S Thilagan P Anitha N B.E. Computer Science Engineering Kumaraguru College of Technology Coimbatore India B.E. Mechatronics Engineering Kumaraguru College of Technology Coimbatore India Mathematics School of Foundational Sciences Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

This study is centred on the development of a methodology for remote health surveillance of patients undergoing physical therapy, employing pose detection technology. The approach utilizes advanced deep learning algorithms to assess patients’ body positions during physical exercises, offering instantaneous feedback to both patients and therapists. This method of remote monitoring coupled with personalized feedback aims to enhance patient outcomes, decrease healthcare expenditures, and bolster patient engagement. by integrating pose detection technology, the system ensures precise tracking of patients’ advancements, facilitating tailored therapy interventions and optimizing treatment effectiveness.

关键词： Deep learning electric potential Accuracy Telemedicine Surveillance Medical treatment Wearab.e.devices Wearab.e.sensors Research and development Remote monitoring

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Optimal Design of a Planning and Contracting Framework to enab.e.Vehicle to building and Grid Services via Demand Response

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Ieee Transactions on Transportation electrification 2025年第3期11卷 7542-7556页

作者： Abd elaziz elsayed, Ahmed Saxena, Shivam essa Zidan Farag, Hany York University Lassonde School of Engineering Electrical Engineering and Computer Science Department Keele Campus TorontoONM3J 1P3 Canada University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B5A3 Canada

bidirectional electric vehicle (eV) charging enab.e. stored energy to reduce peak loads for vehicle to buildings (V2bs) and the vehicle to grid (V2G). However, building owners investing in V2b infrastructure while generating revenue from V2G services face challenges in planning and coordinating with eV owners due to uncertainties in their schedules and profit-sharing expectations. Additionally, misaligned building and grid peak times can create conflicts between V2b and V2G goals, which may negatively impact the building electricity bill. Unlike previous studies that used a contract-free approach for aggregating V2b and V2G, resulting in inconsistent participation, this article proposes a novel planning and contracting framework that enab.e. building owner to determine the optimal contract parameters with eV owners. These parameters include minimum participation time in DR events, minimum arrival state of charge (SoC), and permitted emergency departure hours. The framework supports V2b aggregation with on-site distributed energy resources (DeRs) for DR and V2G services, ensuring transparency and fairness through shared profits and performance-based penalties, while compensating building electricity bills due to V2G activities. The framework is composed of a tri-stage optimization process that uses Monte Carlo simulations to generate eV owner profit assessments, select optimal eV candidates based on charger availability, estimate contract parameters with profit/penalty sharing, and assign contracts between power system operators (PSOs), building owners, and eV owners under multiple virtual scenarios. Simulation results validate the contract parameter assessment and its performance via a 3-day case study with real-world datasets that demonstrates net revenue generation of {\} 209 for each eV owner and {\} 950 for building owners during DR events with a return on investment (ROI) of 134.5% and 130.7% for the eVs and building owners, respectively. © 2015 Ieee.

关键词： Vehicle-to-grid

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blockchain and Federated Reinforcement Learning for Vehicle-to-everything energy Trading in Smart Grids

IEEE Transactions on Artificial Intelligence

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Ieee Transactions on Artificial Intelligence 2024年第2期5卷 839-853页

作者： Moniruzzaman, Md Yassine, Abdulsalam benlamri, Rachid Department of Electrical and Computer Engineering Lakehead University Thunder BayONP7B 5E1 Canada Department of Software Engineering Lakehead University Thunder BayONP7B 5E1 Canada University of Doha for Science and Technology Doha24449 Qatar

The proliferation of electric vehicles (eVs) and the advancement of vehicle-to-everything energy trading systems are expected to play a crucial role in alleviating the stress on the electric grid during peak hours. However, the wide adoption of these paradigms requires intelligent mechanisms that protect the security and privacy of eV users. This article proposes a novel federated reinforcement learning system combined with blockchain technology to maximize eV users' utility while preserving the security and privacy of trading transactions. Furthermore, we develop the concept of proof of state of charge as a consensus mechanism to determine the winning eVs and reward them as block miners in the blockchain. The proposed system is validated through comprehensive simulation experiments utilizing a real-world dataset. The model is implemented on the Avalanche blockchain platform to demonstrate its real-world feasibility. The test results show that the proposed scheme improves eV users' utility significantly compared to the existing studies. The obtained simulation results indicate the effectiveness and robustness of the proposed system. © 2023 Ieee.

关键词： Reinforcement learning

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An Interactive Alphabet and Number learning system using OpenCV and CNN

An Interactive Alphabet and Number learning system using Ope...

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2021 Asian Conference on Innovation in Technology, ASIANCON 2021

作者： Tayal, Anmol Thapar University B.E. Computer Science Patiala India

ISBN: (纸本)9781728185835

The advent of technology and easy access to mobile devices in rural areas of India has opened a vast landscape of opportunities for education and teaching using tech. Children with learning disorders like Dyslexia find it extremely hard to learn Alphabet and numbers(AN), and it can hinder a child's ability to interact with others. It is estimated that total 10 percent children in India suffer from dyslexia. Deep learning is being studied nowadays to overcome challenges in human health, and this paper proposes a pragmatic approach to leverage Deep learning Algorithms in teaching Numbers and Alphabets to Children. The Application, which was developed using the methodology described in this paper, introduces a smart way to have two-way interaction between the digital device and the student;the student gets Rewarded with points by drawing a similar Number or Alphabet as displayed on the screen using a blue, red-colored ob.e.t;it also introduces different levels which seeds compulsion loop in the student. The Model harnesses the open-source OpenCV library to track blue, red coloured ob.e.ts(brob.e.t) using Webcam and interprets the track produced using CNN to predict the AN associated by implementing appropriate accuracy methodology. It utilizes Reward-based learning to trigger a compulsion loop in children, which could help make learning more interactive. © 2021 Ieee.

关键词： Students

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Detection and Categorization of Conflict Flows Within SDN environments using Machine Learning Approach

Detection and Categorization of Conflict Flows Within SDN En...

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emerging Trends in Information Technology and engineering (ic-eTITe), Conference on

作者： Kousalya S Kanthimathi Sundararajan I. Jaichitra Vasudevan L. Jaba Sheela Sarunya Thyagarajan M.E. Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India B.E. Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Dept of Computer Science and Engineering Panimalar Engineering college Chennai Tamil Nadu India

Software-Defined Networking (SDN) is known for its remarkab.e.versatility and adaptability. SDN applications encompass a wide range of network management and control functionalities, including load balancing, access control, and routing. However, SDN can encounter issues stemming from conflicting traffic patterns that may degrade network performance in relation of efficiency and optimization. These conflicts arise when factors such as priority and action settings are adjusted. Furthermore, IP spoofing, commonly employed for anonymity and amplification, frequently leads to widespread distributed denial-of-service (DDoS) attacks. We've established a sufficient condition based on the duration and frequency of DDoS attacks. Notably, our sophisticated attack method generates legitimate traffic within the SIP network using reflection techniques. “As a result, it has the ability to bypass security measures such as blacklists, IP blocking, rate limiting based on packet counts or session/transaction volumes, and automated systems designed to detect unnatural message generation. These security mechanisms are commonly employed in advanced security setups, but the system or technique in question can effectively avoid detection or restriction when interacting with digital environments.”

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A Transmission and Transformation Fault Detection Algorithm based on Improved YOLOv5

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computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign ob.e.ts such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separab.e.convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-excitation Networks(SeNet)attention model is fused into the algorithm framework and a new detection scale suitab.e.for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SeNet

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***: A pilot study for a personality-based friend recommendation system 7

***: A pilot study for a personality-based friend recommenda...

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7th Ieee International conference for Convergence in Technology, I2CT 2022

作者： Mody, Vidhi Mody, Vrushti Parekh, Soham Mistry, Kamal Narsee Monjee Institute of Management Studies Computer Engineering India D.J. Sanghvi College of Engineering B.E. Computer Engineering India Narsee Monjee Institute of Management Studies India

ISBN: (纸本)9781665421683

Social networks have become very popular in today's age. As a result, Friend Recommendation Systems (FRSs) have gained immense importance. Teenagers today are often depressed and feel lonely. Social Networks, if used wisely can help a user connect to the right set of friends which can help to solve the above-mentioned challenges. Most popular social media platforms rely on homophily-based FRSs for their friend recommendations. That is, they use the user's social graph to match him/her with other users. Users now are concerned about how their personal data is being used and its privacy. Therefore, most users do not reveal their personal information on social networks. Also, there is no evidence to the accuracy of user information. Therefore, the existing recommendation systems in social media prove to be inaccurate in a lot of cases. Furthermore, the main purpose of these traditional recommendation systems is to connect pre-existing friends online rather than suggesting users that could be potential friends. To connect users to a new set of people, there is a need to design a friend recommendation system that matches users based on something other than existing connections. In this paper, we propose a system which uses personality analysis along with hybrid filtering to make personalized friend recommendations along with other filtering options for age range, location, gender, etc. This system would solve the cold start prob.e. which occurs because in the present scenario people don't like spending time to specify their preferences for friend recommendations. The filtering options are of further interest to the user as there is a possibility of connecting to a new bunch of friends in person too. © 2022 Ieee.

关键词： Recommender systems

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Real-time Sign Language Recognition using Machine Learning and Neural Network

Real-time Sign Language Recognition using Machine Learning a...

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2022 International Conference on electronics and Renewab.e.Systems, ICeARS 2022

作者： Matlani, Roshnee Dadlani, Roshan Dumb.e. Sharv Mishra, Shruti Tewari, Mrs. Abha B.E. Computer Engineering Vivekanand Education Society's Institute of Technology Mumbai India Vivekanand Education Society's Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9781665484251

Languages based on gestures have recently garnered a lot of interest. In sign language, they are one of the nonverbal communication modalities. It is most commonly used by deaf and dumb individuals with vocal or hearing issues in order to convey and talk with one other and normal people without disabilities. The motion, form, and location of hands, along with the body gestures and facial expressions, are used for conveying the meaning in American Sign Language(ASL). ASL translator also aids in the communication between signers and non-signers. The most efficient way in which they can convey and communicate is through using gestures and that is particularly known as Sign language. Hearing-impaired persons find it very complex and a prob.e. to communicate with the normal people that are in the rest of the world, and as a result, they are isolated, that is the humans with speech impediments communicate via gestures, which makes it difficult for them to mix with the ab.e.bodied. Hence, an interpretive system is required to serve as a link between them and individuals who do not understand their sign language. To assist them overcome these obstacles, we provide a project that will be beneficial to them. This project proposes a Machine Learning-based method for recognising hand gestures and translating them into speech/text, as well as the reverse, converting speech/text to *** introduces a conceptual framework that can interpret sign language autonomously, allowing deaf and dumb individuals to convey more readily with one another and with regular people. Recognition systems and motion tracking are two important and crucial areas of study and are still in their infancy. © 2022 Ieee.

关键词： Audition

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An Intelligent Thyroid Diagnosis System Utilizing Multiple ensemb.e.and explainab.e.Algorithms with Medical Supported Attributes

IEEE Transactions on Artificial Intelligence

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Ieee Transactions on Artificial Intelligence 2024年第6期5卷 2840-2855页

作者： Sutradhar, Ananda Al Rafi, Mustahsin Ghosh, Pronab Shamrat, F M Javed Mehedi Moniruzzaman, Md. Ahmed, Kawsar Azad, A.K.M. bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada University of Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK S7N 5A9 Canada Imam Mohammad Ibn Saud Islamic University Faculty of Science Department of Mathematics and Statistics Riyadh13318 Saudi Arabia Charles Stuart University AI and Cyber Futures Institute BathurstNSW2795 Australia

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used as classifiers on the training data set. Third, a local interpretab.e.model-agnostic explanations (LIMe) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a partial dependence plot (PDP). Finally, the proposed system is validated with extensive experiments, where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector. © 2020 Ieee.

关键词： Neural networks

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Unlocking efficiency in Agricultural Supply Chains: A Secure and Transparent Approach through blockchain Technology

Unlocking Efficiency in Agricultural Supply Chains: A Secure...

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2024 Ieee International Conference on emerging Technologies in computer science for Interdisciplinary Applications, ICeTCS 2024

作者： Lohith, J.J. Shreya, S. Hamsa Priya, J.L. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India D.S.C.E Dept. of Information Science and Engineering Bangalore India B.M.S. College of Engineering Dept. of Electronics and Telecommunications Bangalore India

ISBN: (纸本)9798350372502

Supply Chain Management (SCM) involves orchestrating various processes that extend from acquiring raw materials to delivering finished products to end consumers. The key ob.e.tives of a proposed supply chain in agriculture, emphasizes information-driven processes, cost reduction, and customer impact. It creates a versatile system capab.e.of managing the supply chain for diverse commodities within a country, while also addressing the crucial need for increased transparency and traceability in data throughout the chain of activities. In our proposed approach to agricultural supply chain management (SCM), the focus is on creating a secure, transparent, and traceab.e.SCM. To achieve this, we leverage cutting-edge technologies like the ethereum blockchain and the Interplanetary File System (IPFS). The resulting software is a Decentralized Application (DApp) that orchestrates a series of activities within the agricultural industry, using cryptocurrencies to establish a seamless and efficient supply chain management system. This software platform is fortified by the secure algorithms of blockchain, providing an added layer of reliability. Additionally, our application incorporates transparency and traceability, ensuring user-friendly functionality and making it more accessib.e.for users. © 2024 Ieee.

关键词： blockchain

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