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Towards AI-assisted digital twins for smart railways: preliminary guideline and reference architecture

Journal of Reliable Intelligent Environments 2023年第3期9卷 303-317页

作者： De Donato, Lorenzo Dirnfeld, Ruth Somma, Alessandra De Benedictis, Alessandra Flammini, Francesco Marrone, Stefano Saman Azari, Mehdi Vittorini, Valeria Department of Electrical Engineering and Information Technology University of Naples Federico II Via Claudio 21 Naples80125 Italy Department of Computer Science and Media Technology Linnaeus University Växjö Sweden School of Innovation Design and Engineering Mälardalen University Eskilstuna Sweden

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow. © 2023, The Author(s).

关键词： Cyber Physical System

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Developing an Emotional Interaction design for Social Anxiety Disorders (SAD) 25th

Developing an Emotional Interaction Design for Social Anxiet...

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25th International Conference on Human-computer Interaction, HCII 2023

作者： Chien, HsiangTing Huang, Hsin-Yi Chang, Teng-Wen Shih, Chi-Chi Li, Jia-Rong Wang, Zih-Syuan Chang, Ching-Chih Department of Digital Media Design National Yunlin University of Science and Technology Yunlin Douliu Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Douliu Taiwan

ISBN: (纸本)9783031359910

This study focuses on using books in libraries as an interactive medium, aiming to help individuals with social anxiety disorder alleviate their anxiety and establish positive social experiences. The research background and literature review explore the relationship between social anxiety disorder and books, utilizing narrative frameworks and mirroring psychotherapy techniques to design an experience related to books, book reviews, and social experiences. Through case studies, needs exploration, scenario mapping systems, and interactive process design, we provide an innovative approach for individuals with social anxiety disorder who desire socialization, allowing them to find like-minded partners and engage in social interactions with reduced anxiety interference. Finally, this study summarizes system design, interface design, and user testing and evaluation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reviews

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Implementing Secure Key Management and Distribution in Cloud Environment 2

Implementing Secure Key Management and Distribution in Cloud...

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2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Lamsal, Aman Paul, Aniket Buchade, Amar Nalavade, Jagannath Bidwe, Ranjeet MIT Art Design and Technology University Dept. of Computer Science and Engineering Pune India Vishwakarma Institute of Information Technology Pune Savitribai Phule Pune University Pune India Department of Computer Engineering Pune India

ISBN: (纸本)9798350333763

Key management is considered as an essential aspect of securing cloud-based systems and data. As more organizations move their operations to cloud, the need for secure and efficient key management solutions becomes increasingly important. En-suring security of cloud computing is essential as many users often store their sensitive information with the cloud providers. Cloud service providers offer Key Management Services (KMS) to manage cryptographic keys for their customers, but there are still several challenges related to key management in cloud environments, including key storage, access control, and key rotation. Our research project aims to address these challenges by developing a secure and efficient key management solution for cloud-based systems. We propose a system that uses a combination of various cloud services and encryption algorithms to generate, store, and manage cryptographic keys securely. Our system provides granular access controls, and strong encryption to ensure the confidentiality and integrity of the keys. We believe that our proposed solution will help cloud service providers and organizations to manage their cryptographic keys more efficiently and securely, reducing the risk of unauthorized access and data breaches. This research paper provides a method to effectively manage, store and share the secret key between the parties using the concept of Threshold Cryptography. © 2023 IEEE.

关键词： Access control

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An Efficient Method For Skin Cancer Detection Using Convolutional Neural Network

An Efficient Method For Skin Cancer Detection Using Convolut...

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International Conference on Advances in Information Technology (ICAIT)

作者： R Rajagopal Karthik Kumar G V Senbagavalli M P. Divya Sai Priya Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India Department of Computer Science & Information Technology Alliance College of Engineering and Design Alliance University Bangalore India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Skin cancer poses a significant health risk, often overlooked despite its prevalence. Manual detection by clinicians may sometimes be inadequate, leading to the necessity of automated systems utilizing deep learning. In this study, we propose a computerized solution employing Convolutional Neural Networks (CNNs) to detect skin diseases from imaging data. By leveraging the Kera's Sequential API, we design and train various neural network architectures, analyzing their performances in skin cancer detection. Our custom CNN model achieves an accuracy of approximately 80%. Subsequently, we explore the efficiency of transfer learning models such as VGG11 to improve accuracy and for comparative analysis. Notably, the vgg16 architecture achieves the highest accuracy of 86% making it a competitive solution for skin cancer detection.

关键词： Accuracy Transfer learning Neural networks computer architecture Manuals Skin Convolutional neural networks Lesions Information technology Skin cancer

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Post-layout simulation driven analog circuit sizing

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science China(Information sciences) 2024年第4期67卷 289-300页

作者： Xiaohan GAO Haoyi ZHANG Siyuan YE Mingjie LIU David Z.PAN Linxiao SHEN Runsheng WANG Yibo LIN Ru HUANG School of Integrated Circuits Peking University School of Computer Science Peking University Department of Electrical and Computer Engineering University of Texas at Austin Institute of Electronic Design Automation Peking University

Post-layout simulation provides accurate guidance for analog circuit design, but post-layout performance is hard to be directly optimized at early design stages. Prior work on analog circuit sizing often utilizes pre-layout simulation results as the optimization objective. In this work, we propose a post-layoutsimulation-driven(post-simulation-driven for short) analog circuit sizing framework that directly optimizes the post-layout simulation performance. The framework integrates automated layout generation into the optimization loop of transistor sizing and leverages a coupled Bayesian optimization algorithm to search for the best post-simulation performance. Experimental results demonstrate that our framework can achieve over 20% better post-layout performance in competitive time than manual design and the method that only considers pre-layout optimization.

关键词： analog EDA transistor sizing Bayesian optimization post-layout simulation

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An Empirical Comparison of Classification Machine Learning Models Using Medical Datasets 4th

An Empirical Comparison of Classification Machine Learning M...

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The 4th International Conference on Data science, Machine Learning 2022

作者： Saketha Rama, B.V. Suryanarayana, G. Ansari, Mohd Dilshad Begum, Ruqqaiya Department of Computer Science Engineering Indian Institute of Information Technology Design and Manufacturing Tamil Nadu Kancheepuram India Department of Information Technology Vardhaman College of Engineering Telangana State Hyderabad India Guru Nanak University Hyderabad India Department of Computer Science Engineering Vardhaman College of Engineering Telangana Hyderabad India

ISBN: (纸本)9789819920570

Classification is a supervised learning model where the class labels are accurately identified for future samples. Medical data is an important source for understanding and improving health outcomes and classification algorithms are often used to analyze these data. Learning models give significant experiences into the situational needs of patients. Various hypotheses have been carried out on different datasets yet it is truly challenging to track down which model is suitable. Proposed work compares the performance of classification models like LR, DT, SVM, NB, KNN, and RF on various datasets. SVM classifier yields accuracy of 0.59 for the Diabetic dataset as it considers individual model opinion, while RF classifier surpassed them both with accuracy 0.9974 for the breast cancer Wisconsin dataset since it is an ensemble approach that takes majority opinions. These findings highlight the need for careful consideration of the choice of classification model when analyzing medical data and provide valuable insights for researchers and practitioners working with these data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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A Comprehensive Review on Artificial Intelligence for Human Brain Disease

A Comprehensive Review on Artificial Intelligence for Human ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Vishal Kumar Choudhary Adesh Pawar Saurabh Suman Gulshan Kumar Abhishek Kumar Praveen Kumar Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Wardha Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology Wardha Maharashtra India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The brain is one of the most complicated human organs, and diseases associated with the brain are still hard to diagnose, treat and manage. The development of devices such as Artificial Intelligence technology has made wonders in understanding the causes, diagnosing, and offering treatment to brain abnormalities. In neurology, the use of AI to process medical data to understand, classify, and prognose diseases can avail new details about disorders such as Alzheimer's disease, Parkinson disease, stroke, and cancerous lesions of the brain. The review analyses the role of AI in the diagnosis and treatment of human brain diseases with an emphasis on how it is currently used, what benefits it may bring and what drawbacks are connected with this. We emphasize new informational technologies and outline strategies for the incorporation of AI among other technologies in the management of patients in neurological disorders, as well as in the development of the field as a whole. Artificial Intelligence (AI) application in health-related activities has changed how brain diseases of man are diagnosed, treated and managed. A detailed study here seeks to examine how recent relevant developments especially AI methods including ML, DL and NLP have been beneficial in understanding more neurological disorders. It notes how holistically, AI is applicable in diseases involving the brain, such as Alzheimer's stroke, and brain tumors.

关键词： Neurological diseases Deep learning Neurology Reviews Magnetic resonance imaging Medical services Artificial intelligence Alzheimer's disease Long short term memory Diseases

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Machine Learning Analysis of Radio Data to Uncover Community Perceptions on the Ebola Outbreak in Uganda

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ACM Journal on Computing and Sustainable Societies 2024年第3期2卷 1-28页

作者： Joyce Nakatumba-Nabende Jonathan Mukiibi Tobius Saul Bateesa Sudi Murindanyi Andrew Katumba Chodrine Mutebi Computer Science Makerere University Kampala Uganda Computer Science Makerere University College of Computing and Information Sciences Kampala Uganda Electrical and Computer Engineering Makerere University College of Engineering Design Art and Technology Kampala Uganda

Radio is vital for people, especially in rural areas, to share their concerns through interactive talk shows. Understanding public perceptions of pandemics is crucial because they influence people’s attitudes and health-seeking behaviors. This study used machine learning to analyze English and Luganda radio broadcast data to understand public perceptions and perspectives on the Ebola outbreak in Uganda. Our findings revealed three main speaker categories: media personalities, community guests and listeners, and government officials. The government made the most significant effort to educate the public about the Ebola outbreak. The analysis showed that the community was hesitant to use Ebola vaccines, believing that they had not been tested on other populations where the Ebola virus had originated. The community was also concerned about the effects of the lockdown measures imposed during the COVID-19 pandemic. The analysis of the radio broadcast data revealed differences in the timing and content of the conversations between male and female speakers. These experiences can inform population-specific policies for handling ongoing and future pandemics.

关键词： Speech recognition Ebola outbreak perceptions radio data

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AI Virtual Hardware

AI Virtual Hardware

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2022 IEEE Pune Section International Conference, PuneCon 2022

作者： Sravya, Tummala Bhargava, Sakshi Narendra Shravani Bodke, Rugveda Kulkarni, Nilima Mit School of Engineering Mit Arts Design and Technology University Department of Computer Science and Engineering Pune412201 India

ISBN: (纸本)9781665498975

The computer is one of the wonderful and fascinating inventions of technology and has come to significant use to humans in every sector. The existing computer technology is already advanced and modern. Even so, this proposed system will provide better ease of technology to humans. The proposed system is an Artificial Intelligence (AI) application with three combined features that are AI Virtual Mouse, Keyboard and Painter These three features (AI Virtual Mouse, Keyboard and Painter) use a common hand tracking module. Hand tracking module is a python file which has a class name Hand Detector and it contains 4 member functions that are findDistance, findHands, findPosition and fingerUp. Using this module the three features work successfully. The proposed system opens with a main window which is a GUI screen made with the help of module Tkinter. This main GUI page contains all the three features (AI Virtual Mouse, Keyboard and Painter) combined. The common libraries used for system execution are OpenCV, CVZone, numpy, autopy, mediapipe, etc. © 2022 IEEE.

关键词： Graphical user interfaces

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A Review on the Use of Smart Agriculture Systems to Protect Crops

A Review on the Use of Smart Agriculture Systems to Protect ...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Gulshan Kumar Palash Gourshettiwar Saurabh Suman Shravan Kumar Dhiraj Kumar Rahul Raushan Department of Computer Science and Design Faculty of Engineering and Technology Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. Control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture Technological innovation Space technology Crops Production Soil Communications technology Internet of Things Remote sensing Farming

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