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Methodology for Development of Artificial Intelligence based Model for Ayurved Tongue Examination 9

Methodology for Development of Artificial Intelligence based...

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9th IEEE international conference for convergence in technology, I2CT 2024

作者： Yenishetti, Swapna Karajkhede, Ganesh Panat, Lakshmi Centre For Development of Advanced Computing HPC-MBA Group Pune India

ISBN: (纸本)9798350394474

Artificial intelligence is emerging technology impacting our lives, especially the healthcare sector. Ayurved is a personalised medical system which involves dynamic analysis for diagnosis of diseases and treatment of ailments. AI will assist Ayurved doctor during diagnosis of diseases and treatment of ailments, improving the outcome of intervention. Darśana parikā (Visual Examination) is an important modality in clinical examination, useful in evaluation of signs-symptoms related to Tongue, Skin, Urine, Stool etc. Objective of this paper is to describe the methodology of development of Artificial Intelligence based model for Ayurved Tongue examination (jihvā parikā). The steps involved are identification of usecases for application of AI, Dataset collection and organization, AI modelling, Prediction and Re-Modeling. Readers can adopt similar methodology for the development of AI based model for darśana parikā of Skin, Urine, Stool, Sputum, Teeth, Nails, Hair, Eye, Wounds, which will go a long way in transforming Ayurved based healthcare by providing objective tool for *** Statement - Use of AI in indigenous system of medicine is vital but inspiring task. Ai will aid in development of non-invasive technologies for early diagnosis of diseases-conditions using Ayurveda core principles. Using real life clinical data more refined, reliable and clinically validated models can be developed. Proposed research paper will generate curiosity about use of AI in Ayurveda amongst young intelligent minds. This paper will guide readers about methodical systematic design for development of AI based models based on fundamentals of Ayurveda. © 2024 IEEE.

关键词： Diagnosis

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EEMS - Examining the Environment of the Job Metaverse Scheduling for Data Security 4th

EEMS - Examining the Environment of the Job Metaverse Schedu...

引用

4th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2023

作者： Bandaru, Venkata Naga Rani Visalakshi, P. Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai603202 India

ISBN: (纸本)9783031488870

Job scheduling is one of the main barriers to achieving resource efficiency and cost-effective execution in the cloud computing environment. Finding a nearly perfect solution in a fair quantity of time is challenging when it comes to work scheduling. Consequently, the delayed convergence and local minimums are still existing. This EEMS - examining the environment of the job metaverse schooling for data security proposed method describes a new secure job scheduler to meet the challenge of scheduling jobs in a cloud computing environment. This method is based on secure differential evolution, and Cloud Sim has been used in numerous tests to show that EEMS works. This innovative method lays a heavy emphasis on upholding data security and integrity throughout the scheduling process in addition to attempting to maximize resource usage and save costs in cloud computing settings. This combination of secure differential evolution and EEMS principles offers a strong solution that looks to address remaining issues with cloud-based task scheduling. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Cost effectiveness

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FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks 18th

FedDCT: A Dynamic Cross-Tier Federated Learning Framework in...

引用

18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Xian, Youquan Gan, Xiaoyun Yao, Chuanjian Li, Dongcheng Wang, Peng Liu, Peng Zhao, Ying Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin54104 China School of Computer Science and Engineering Guangxi Normal University Guilin54104 China School of Business Guilin University of Electronic Technology Guilin54104 China

ISBN: (纸本)9783031714634

Federated Learning (FL), as a privacy-preserving machine learning paradigm, trains a global model across devices without exposing local data. However, resource heterogeneity and inevitable stragglers in wireless networks severely impact the efficiency and accuracy of FL training. In this paper, we propose a novel Dynamic Cross-Tier Federated Learning framework (FedDCT). Firstly, we design a dynamic tiering strategy that dynamically partitions devices into different tiers based on their response times and assigns specific timeout thresholds to each tier to reduce single-round training time. Then, we propose a cross-tier device selection algorithm that selects devices that respond quickly and are conducive to model convergence to improve convergence efficiency and accuracy. Experimental results demonstrate that the proposed approach under wireless networks outperforms the baseline approach, with an average reduction of 54.7% in convergence time and an average improvement of 1.83% in convergence accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Quantum Modular Multiplication: A New Frontier in Quantum computing 8

Quantum Modular Multiplication: A New Frontier in Quantum Co...

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8th IEEE international conference for convergence in technology, I2CT 2023

作者： Teja, Mahankali Mani Ravindra, J.V.R. Reddy, Sunku Rohan Sanjay Yashwanth, Irugurala Vardhaman College of Engineering Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350334012

Quantum modular multipliers are a promising development in the field of quantum computing that aim to perform modular multiplication faster and more efficiently than classical algorithms. This is achieved by encoding the numbers being multiplied into quantum bits (qubits) and using quantum gates to manipulate them in a way that represents the calculation. Since quantum physics principles like superposition and entanglement dramatically speed up processing compared to conventional computing, many calculations may be run simultaneously. Quantum modular multipliers have the potential to have a significant impact in a range of fields, including cryptography and beyond, since they offer a faster and more efficient way to do modular multiplications. Several fields, including simulation, optimization, and cryptography, can benefit from the use of quantum *** of the crucial operations in many of these applications is modular multiplication, a sort of multiplication that requires obtaining the remainder of the product of two numbers when divided by a third number. In this paper, we provide a novel modular multiplication method based on a quantum algorithm. To examine its complexity and compare it to that of current adders, we employ a novel form of adder that is intended to not retain the final carry. This adder's decrease percentage in terms of gates and depth is roughly 70%. Our method takes advantage of the special qualities of qubits to complete the computation significantly more quickly than conventional algorithms. It is based on the fundamental ideas of quantum physics, such as superposition and entanglement. © 2023 IEEE.

关键词： Adders

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Building Trust: IoT Security and Blockchain Integration 18

Building Trust: IoT Security and Blockchain Integration

引用

18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Reegu, Faheem Ahmad Ayoub, Shahnawaz Dar, Arshad Ahmad Hussain, Gousiya Gulzar, Yonis Fatima, Ummatul Jazan University College of Computer Science and Information Technology Saudi Arabia Glocal University Glocal School of Science and Technology Mirzapur India Mewar University Department of Computer Sciences Chittorgarh India King Faisal University College of Business Administration Department of Management Information Systems Saudi Arabia Aligarh Muslim University Faculty of Science Department of Statistics and Operations Research Aligarh India

ISBN: (纸本)9789380544519

Blockchain technology has captured considerable attention in recent years due to its unique potential to reshape the financial industry. This innovative breakthrough is increasingly gaining prominence, especially within finance, where it has demonstrated efficacy in streamlining processes and addressing cost concerns. It simplifies the authentication and tracking of intricate, multi-step actions requiring verification and traceability. Furthermore, blockchain finds applications in managing voting platforms, designations, and activities, offering a reliable means of validating official assertions. The Internet of Things (IoT) represents a significant paradigm shift, integrating millions and billions of smart devices endowed with processing, sensing, and actuation capabilities. The convergence of IoT with social networking principles has spawned the concept of Social IoT, reflecting the societal integration of these technologies. While IoT primarily revolves around connecting various smart gadgets, its infrastructure remains vulnerable to security and privacy threats due to the lack of foundational security measures. This study endeavors to delve into the challenges inherent in IoT and investigate how these obstacles can be mitigated through the integration of blockchain technology. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Attacks blockchain Data Sharing Internet of things Security

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Complex Double Integration Enhanced ZNN with Rejection of Linear Noise for computing Time-Dependent Sylvester Equation 25

Complex Double Integration Enhanced ZNN with Rejection of Li...

引用

25th IEEE international conference on Industrial technology, ICIT 2024

作者： Xu, Jinsha Hua, Cheng Li, Shuai Liao, Bolin College Of Computer Science And Engineering Jishou University Jishou China University Of Oulu Faculty Of Information Technology And Electrical Engineering Oulu Finland

ISBN: (纸本)9798350340266

Recurrent neural networks (RNN) are highly effective in solving the inverse problem of time-dependent matrices. However, in real-world engineering applications, noise interference is inevitable. The zeroing neural network (ZNN) is a special class of RNN that exhibit strong noise tolerance in solving the time-dependent Sylvester equation (TDSE). Despite the usefulness of the previously proposed original ZNN (OZNN) model and integral enhanced ZNN (IEZNN) model, they lack the ability to suppress linear and quadratic noise when solving the Sylvester equation. In this paper, a complex double integration enhanced ZNN (CDIEZNN) model is introduced for the first time to address the problem of TDSE. The theoretical basis for the convergence and robustness of the double integral model is described in detail. Two experiments are conducted to verify the convergence and robustness of the CDIEZNN model, and the digital simulation results demonstrate that in solving the TDSE, the residual error of the CDIEZNN model rapidly and stably converges to 3.82 × 10-6in a linear noise environment, and even in complex quadratic noise environments, the residual error can still converge to 1.99 × 10-2• In contrast, the OZNN and IEZNN models have weak suppression of linear noise and the residual errors cannot converge in a quadratic noise environment and the results show divergence. © 2024 IEEE.

关键词： Neural network models

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Research on communication mechanism optimization based on distributed graph computing environment 4

Research on communication mechanism optimization based on di...

引用

4th international conference on Big Data Economy and Information Management, BDEIM 2023

作者： Lin, Jianchao Cao, Chunna Ren, Chunhui State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd Beijing China

ISBN: (纸本)9798400716669

In recent years, with the rapid development of the Internet of Things, the Internet, and social networks, the storage of data in the network is growing at an explosive rate and is becoming more and more closely related to the real natural world. Driven by large-scale data mining and machine learning applications, distributed graph computing models that use graph data structures to describe data and relationships between data have been increasingly widely used. Therefore, this paper studies the optimization of communication mechanisms based on a distributed graph computing environment. Firstly, a BSP model based on a pure message-passing communication mechanism of a distributed graph computing system is established. Secondly, the optimization model is evaluated from two aspects: data communication and convergence condition judgment. Finally, large-scale data sets are used to test and evaluate performance optimization. The results show that this method can greatly improve the efficiency of graph parallel computing. © 2023 ACM.

关键词： BSP model Distributed Graph Computation Distributed system Graph processing Parallel computing

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Approximation Properties of Modified-Bernstein Operators Having Szász Weight Functions 7th

Approximation Properties of Modified-Bernstein Operators Ha...

引用

7th international conference on Soft computing: Theories and Applications, SoCTA 2023

作者： Sonker, Smita Priyanka Department of Mathematics National Institute of Technology Kurukshetra Haryana Kurukshetra136119 India

ISBN: (纸本)9789819720309

The present work introduces a modification of the modified-Bernstein operator using Szász weight function. We discuss the rate of convergence of proposed operators via continuity modulus and Peetre’s K-functional for Hölder’s class of functions. Further, we establish a Vornovskaya-type asymptotic formula. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Asymptotic formula Bernstein operators Szász basis function

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Gait Phase-Free Neural Network-Based Controller for Cable-Driven Assistive Suits 15

Gait Phase-Free Neural Network-Based Controller for Cable-Dr...

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15th international conference on Information and Communication technology convergence, ICTC 2024

作者： Koo, Young-Jun Lee, Jeong-Woo Kim, Bumho Lee, Seong-Ho Electronics and Telecommunications Research Institute Artificial Intelligence Computing Research Laboratory Daejeon Korea Republic of

ISBN: (纸本)9798350364637

Human assistive systems such as exoskeleton robots and soft suits have been introduced to aid rehabilitation and prevent fatigue by assisting skeletal muscles. The performance of these systems depends on the timing and amount of force generation by the controllers. Designing phase-dependent controllers is challenging due to variations in human movement patterns. Recently, deep reinforcement learning (DRL) has shown potential in resolving control problems using deep neural network-based controllers. The objective of this study was to develop a phase-free controller for a cable-driven assistive suit using forward dynamics simulation. A three-dimensional full-body human model and a cable-driven suit model were prepared in the MuJoCo. A human controller and a suit controller were trained using DRL. To recognize gait states, maintaining a minimum tensional force of 10 N was included in the reward function during suit controller training. The mechanical work of the hip decreased when the assistive forces of the cable-driven suit were applied. The positive work during walking was 0.53 J/kg with the suit on and 0.95 J/kg with the suit off. This study demonstrated the effectiveness of a neural network-based suit controller in assisting hip joint power during the stance phase of walking without relying on a gait phase detection method. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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A Novel and Accurate Method of Diagnosis via AI and ML for Smart HC System 4

A Novel and Accurate Method of Diagnosis via AI and ML for S...

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4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Elizabeth, J. Ruby Emmanuel, Mathews Aswini, E. Pathak, Shriram Bhuvaneswari, M. Choubey, Abhishek Nehru Institute of Engineering and Technology Department of Computer Science and Business System Tamil Nadu T.M.Palayam India Sathyabhama University of Science and Technology chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering India Ies Insititute of Technology and Management Ies University Department of Mechanical Engineering M.P. Bhopal India Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore India Sreenidhi Institute of Science and Technology ghatkesar Hyderabad Yamnampet India

ISBN: (纸本)9798350360165

Recent breakthroughs in the internet of Issues (IoT), computing through the cloud, and the use of AI have changed the standard healthcare system, making a case for smart health care. By combining key technologies including IoT and AI, there is a major improvement in medical services. The merging of IoT and AI offers multiple possibilities within the healthcare industry. This study piece presents a new artificial intelligence- and internet of things convergence-based framework for disease detection in smart health care systems, emphasizing especially on cardiovascular diseases and diabetes. The suggested model includes separate steps, including data collection, preparation, classification, and parameter modification. Utilizing Internet of Things devices like smartwatches along with sensors enables smooth collection of information, while AI methods harness this data for the detection of illnesses. The technique presents a new approach, applying to the Crowd Search Optimizer algorithm-based Flowed Long and Short-Term Memory (CSO-CLSTM) approach to disease detection. CSO is applied to improve both 'weights' while 'bias' factors of the CLSTM framework, while the isolation Fg (iForest) method is implemented to remove outliers. Through testing with health care information, the CSO-LSTM model got excellent accuracy ratings of 96.16% when 97.26% in identifying heart disease while diabetes, respectively. Consequently, the suggested CSO-LSTM model appears as a solid and reliable method of disease evaluation in digital healthcare systems, showing its potential for broad application. © 2024 IEEE.

关键词： Electronic health record

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