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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Fariborz Lohrabi Pour Harrison Williams Matthew Hicks Dong Sam Ha Bradley Department of Electrical and Computer Engineering Multifunctional Integrated Circuits and System (MICS) Group Virginia Tech Blacksburg Virginia USA Department of Computer Science Virginia Tech Blacksburg Virginia USA

This paper presents an investigation into practical considerations in the minimization of Age of Information (AoI) for the system architectures with and without updating feedback. To study the impact of the critical characteristics of the energy harvesters on the computing accuracy of the average AoI, an RF energy harvester with a half-wavelength patch antenna array along with a 3-stage voltage rectifier is designed and prototyped with the center frequency of 2.655 GHz. A cryptography algorithm is also implemented on a $\mu$ -controller unit to imitate the behavior of a practical processing unit. The paper shows measurement results and discusses the impact of the non-idealities in the energy harvester on the average AoI of the system. It also shows that the nonlinear power conversion profile of the energy harvester can increase the average AoI to over 300% compared to an ideal and linear energy harvester.

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Multi Technology Ensembled Optimized Enterprise Architecture for Retail Industry 1

Multi Technology Ensembled Optimized Enterprise Architecture...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Chakraborty, Sangita Jyothi, N.M. Lekhi, Sushil Dutta, Usha Mestry, Sachin Dasrath Prasad, Ritesh Koneru Lakshmaiah Education Foundation Department of CSE Vaddeswarm AP India Lovely Professional University Intelligent System-1 Jalandhar India Tata Consultancy Services Departmento of IT West Bengal Kolkata India Ruparelia Group Department of IT Kampala Uganda Brainware University Dept. of Computer Science Engineering West Bengal Kolkata India

ISBN: (纸本)9798350339727

This research aims to provide the Enterprise Architecture (EA) ensembled with multiple advanced technologies like Artificial Intelligence (AI), Internet of Things (IoT), Microservices, Blockchain, Cloud technology and Big Data and to meet the trending challenges of the retail industry from the perspective of Business to Business (B2B) and Business to Consumer (B2C). The paper proposes novel enterprise architecture and discusses the integration, challenges of technologies in EA and advantages of Enterprise Architecture framework for innovative strategies in retail business. It aims to bring the importance of practice of EA in retail industry to reinforce and understand retail business management and applications in better way using ensemble architecture for obtaining maximum returns on the investment by the retailers and obtaining maximum customer satisfaction. © 2023 IEEE.

关键词： Artificial intelligence

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Energy Efficient Hybrid Routing for Different Energy Levels Using Fuzzy Logic

Energy Efficient Hybrid Routing for Different Energy Levels ...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Kalpana Singh Prashant Pandey Firoj Ahamad Department of Computer Science Engineering Rajkiya Engineering College Sonbhadra Uttar Pradesh India Department of Electronics Engineering Rajkiya Engineering College Sonbhadra Uttar Pradesh India School of Computer and System Sciences Jawaharlal Nehru University New Delhi Delhi India

Collection of sensor nodes and base station called as Wireless Sensor Networks. While, communicating with other nodes within network this type of network uses huge amount of energy. And, node has limited life time due to battery power. For efficiently utilizing these nodes energy and power, clustering was considered as a best approach. In proposed paper, we work on heterogeneous node, in such environment nodes are differ by their energy level, power, computation, communication etc. The protocol proposed for Energy Efficient Hybrid Routing in different energy levels using fuzzy logic provides three different network for three different initial energy level of nodes such that. 3J,. 4J and. 5J under same area (500*500) m 2 in which super node region uses multipath routing and clustering approach uses for transmitting packet/ data to base station and normal type nodes will uses single path routing transmitting packet/ data to Base station. The proposed protocol discussed different energy levels for finding best initial energy value of node in network and improved throughput, stability, lifetime of network on. 4J energy level as compare to. 3J and. 5J. To optimize the cluster formation we used predetermined technique i.e. inverted binary tree. This technique helps in balancing the load of super nodes region. The proposed model simulated in MATLAB, result shows better performance at 0.4J initial energy.

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Agus Pratondo Astri Novianty Budi Laksono Putro Siska Komala Sari Department of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia Department of Computer Science Education Indonesia University of Education Bandung Indonesia Diploma Program of Information System School of Applied System Telkom University Bandung Indonesia

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice.

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A Way of Making Smart Health Through Collaborating Machine Learning with the Bibliometrics

A Way of Making Smart Health Through Collaborating Machine L...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： T Vignesh P. S Sruthi T. Mohanraj Gaurav Kumar Akshay Varkale M.Sahaya Sheela Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Computer Science and Business System Nehru Institute of Technology T.M. Palayam Tamil Nadu India Department of Computer Science Engineering Karpagam Academy of Higher Education Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Computer Science & Engineering (CS and E) IES Institute of Technology and Management IES University Bhopal M.P India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

It datasets are widely utilized, shared, and repurposed without providing enough context for understanding the decision-making processes that led to their creation. It is necessary to modify system creation and use procedures to handle the concrete effects of how knowledge for model building is envisioned and used in real-world applications as AI systems assume more and more important roles. Documents are created at every phase of the data generation lifecycle to improve communication, aid in decision-making, and highlight the need of careful data work. By putting out this paradigm, we want to shed light on the choices and duties that are often disregarded while creating datasets, which is a crucial first step in bridging the duty of care gap in AI. This framework provides an essential and important resource for guaranteeing ethical AI activities, and it is in line with current developments in auditing procedures.

关键词： Ethics Decision making Smart healthcare Production Planning Maintenance Stakeholders

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Mathematical Modelling of Nonlinear Heat Conduction with Relaxing Boundary Conditions 1

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10th International Conference on Numerical Methods and Applications, NMA 2022

作者： Filipov, Stefan M. Faragó, István Avdzhieva, Ana Department of Computer Science Faculty of Chemical and System Engineering University of Chemical Technology and Metallurgy Sofia Bulgaria Department of Differential Equations Institute of Mathematics Budapest University of Technology and Economics & MTA-ELTE Research Group Budapest Hungary Faculty of Mathematics and Informatics "St. Kl. Ohridski" University of Sofia Sofia Bulgaria

ISBN: (数字)9783031324123

ISBN: (纸本)9783031324116

This paper considers one dimensional unsteady heat condition in a media with temperature dependent thermal conductivity. When the thermal conductivity depends on the temperature, the corresponding heat equation is nonlinear. At one or both boundaries, a relaxing boundary condition is applied. It is a time dependent condition that approaches continuously, as time increases, a certain time independent condition. Such behavior at the boundaries arises naturally in some physical systems. As an example, we propose a simple model system that can give rise to either Dirichlet or convective relaxing boundary condition. Due to the dependence of the thermal conductivity on the temperature, the convective condition is nonlinear. For the solution of the problem, we propose a new numerical approach that first discretizes the heat equation in time, whereby a sequence of two-point boundary value problems (TPBVPs) is obtained. We use implicit time discretization, which provides for unconditional stability of the method. If the initial condition is given, we can solve consecutively the TPBVPs and get approximations of the temperature at the different time levels. For the solution of the TPBVPs, we apply the finite difference method. The arising nonlinear systems are solved by Newton’s method. A number of example problems are solved demonstrating the efficiency of the proposed approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Boundary conditions

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Diabetic Foot Ulcer Diagnosis using Machine Learning

Diabetic Foot Ulcer Diagnosis using Machine Learning

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

作者： Monu Sharma Ayesha Mukthar Meenu Gupta Neha Gupta Rakesh Kumar Valley Health System Winchester Virginia USA King Abdullah International Medical Research Center AlAhsa King Saud bin Abdulaziz University for Health Sciences Saudi Arabia Department of Computer Science & Engineering Chandigarh University Punjab India School of Computer Science & Engineering Geeta University Naultha Panipat

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Diabetic Foot Ulcers (DFUs) are a serious diabetes complication, which mostly results in infection, amputation, and increased mortality if not addressed timely. Proper classification of DFUs is critical for early treatment and improved clinical outcomes. In the context of DFU image classification, this study compares the effectiveness of a Convolutional Neural Network (CNN) with traditional Machine Learning (ML) models Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). The CNN model is shown to outperform other models with much higher accuracy since it can extract pertinent features automatically from the images, while ML models depend on manually designed features and have comparatively poorer performance. The research also points to some major gaps in DFU classification, such as excessive intra-class variability, small dataset size, and class imbalance. The results have real-world applicability in the healthcare field by yielding AI-based DFU classification for timely diagnosis and intervention. Future work can build on improving model performance by increasing the dataset size, using transfer learning, and real-time deployment in clinics. The proposed model achieved 90.5% accuracy, 87.2% precision, 85.8% recall, and an F1-score of 86.78%.

关键词： Support vector machines Radio frequency Accuracy Transfer learning Medical services Nearest neighbor methods Feature extraction Real-time systems Diabetes Convolutional neural networks

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Sale Forecast and Analysis of Public's Attitude of EV Base on Combination of BP and LSTM Network and Decision Tree 2

Sale Forecast and Analysis of Public's Attitude of EV Base o...

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2nd International Conference on Machine Learning and Intelligent systems engineering, MLISE 2022

作者： Guan, Qinglan Ma, Yining Yang, Siqi The Ohio State University College Of Engineering Department Of Computer Science And Engineering ColumbusOH43210 United States Sichuan University College Of Mathematics Chengdu610065 China Zhongnan University Of Economic And Law Information Management And Information System Wuhan China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

Because new energy vehicles are not fully popularized, the data on the monthly sales of new energy vehicles are scattered, and nobody has taken the responsibility to organize it regularly until 2015, thus the recording period is short. To collect customer wishes, this study also carefully designed a questionnaire and spent a lot of time collecting answers for all ages. In this paper, this study mainly focusses on the field of new energy vehicle market forecast and analyze the key factors that affect people's purchase of new energy vehicles. To Figure out the most significant factor influencing the sale of the new vehicle market, this paper uses the least square regression, ridge regression, and the k nearest neighbor regression to build models and compare the coefficient of each independent variable. The accuracy of the normal Back Propagation (BP) model cannot meet the actual demand, this paper propose a working-well BP-LSTM model which is a combination of the BP model and long short-term memory (LSTM) model. First, this study decomposes the sales data into the non-random fluctuation series and the random fluctuation series. Then, this study builds models for each series by BP and LSTM network respectively. This paper also proposes a decision tree model based on the result of our questionaries to gain the importance score of different attributes. The experimental results show that the total number of charging piles plays the most important role in the sales of new energy vehicles and the BP-LSTM model performs well and the mean square error is 2.525. Reasons for buying the new energy car affect the willingness of people's purchase most and the distance influences which type of new energy vehicles would people buy. © 2022 IEEE.

关键词： Long short-term memory

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Advancing Healthcare IoT: Blockchain and Federated Learning Integration for Enhanced Security and Insights

Advancing Healthcare IoT: Blockchain and Federated Learning ...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Rida Malik Atta ur-Rehaman Hamza Razzaq Chandradeep Bhatt Keshav Kaushik Inam Ullah Khan Department of Information Security Riphah Institute of System Engineering Islamabad Pakistan Department of Data Science Riphah Institute of System Engineering Islamabad Pakistan Department of CSE Graphic Era Hill University Uttarakhand India Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Electronic Engineering SEAS Isra University Islamabad Campus Pakistan

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Fast-growing IoT devices in healthcare have ushered in a new era of data-driven patient care and treatment. These gadgets capture too much sensitive medical data, a major issue. We need to boost data analysis, security, and privacy. This research uses cutting-edge concepts like federated learning and blockchain technology to improve Healthcare IoT analytics and security. This study examines blockchain and federated learning's numerous integration aspects. It begins with data security, employing blockchain's immutability and decentralization to prevent manipulation. Federation learning was utilized to solve privacy concerns while adhering to tight healthcare data security regulations, providing strong data analysis without releasing raw, patient-specific information. This study examines how federated learning and blockchain technologies enhance healthcare IoT systems' analytics. This connection allows safe, collaborative analysis among healthcare professionals and devices, offering more insights for research, diagnostics, and tailored patient care. How to integrate these cutting-edge technologies into healthcare IoT networks is also explored.

关键词： Training Data privacy Technological innovation Federated learning Data security Collaboration Training data

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VENUS: A Geometrical Representation for Quantum State Visualization

arXiv

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arXiv 2023年

作者： Ruan, Shaolun Yuan, Ribo Guan, Qiang Lin, Yanna Mao, Ying Jiang, Weiwen Wang, Zhepeng Xu, Wei Wang, Yong School of Computing and Information System Singapore Management University Singapore Department of Computer and Information Sciences University of Delaware United States Department of Computer Science Kent State University United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Computer and Information Science Department Fordham University United States Electrical and Computer Engineering George Mason University United States Computational Science Initiative Brookhaven National Laboratory United States

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

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