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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Tanaka, Tomoaki Kato, Michiya Osana, Yasunori Miyoshi, Takefumi Tada, Jubee Tanaka, Kiyofumi Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University 2-39-1 Kurokami Kumamoto Chuo-ku860-8555 Japan WasaLabo LLC. K-2 Buransyu 6-5-20 Minaminaruse Tokyo Machida-shi194-0045 Japan Graduate School of Science and Engineering Yamagata University 1-4-12 Kojirakawa-machi Yamagata Yamagata-shi990-8560 Japan Japan Advanced Institute of Science and Technology 1-1 Asahidai Ishikawa Nomi923-1292 Japan Division of Advanced Information Technology Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan

ISBN: (纸本)9783031856372

In recent years, demand for data-parallel processing has been growing, and this parallelism often appears in AI processes. One method to accelerate these processes is using DSA, domain-specific architecture. A common data transfer method on DSA is DMA, which is direct memory access. There are several studies on DMA-based accelerators. However, few studies focus on data transfer methods. In this paper, a vector register-sharing mechanism has been proposed as a new data transfer method. Our proposed mechanism is named "SHAVER". In this mechanism, a part of vector registers is directly shared with an accelerator. An open-source RISC-V vector co-processor is used to evaluate the mechanism’s potential. It has been implemented on an FPGA and a simulator for the evaluations. The results indicate the possibility of the proposal mechanism to achieve a maximum of 7.13% speedup over DMA transfer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Vectors

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Applications of Machine Learning to Skin Cancer Detection 5

Applications of Machine Learning to Skin Cancer Detection

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5th International Conference on Contemporary Computing and informatics, IC3I 2022

作者： Prakash, Anju J. Satpathy, Gargibala Raman, Ramakrishnan Manikandan, E. Buddhi, Dharam Kapila, Dhiraj Sree Buddha College of Engineering Department of CSE Pattoor Alappuzha India Sree Balaji Medical College and Hospital Bharath Institute of Higher Education and Research Chrompet Department of Biotechnology Tamil Nadu Chennai India Symbiosis Institute of Business Management Maharashtra Pune India Thiruvalluvar University Thennangur Solid-State Nanoscale Laboratory Department of Physics TUCAS Campus Vellore India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350398267

Owing to shortage of awareness regarding signs and measures for prophylaxis, skin cancer (SC), among the most severe kinds of carcinoma (Ca), is actively destroying far more people than it used to. Consequently, in order to halt the progression of Ca, timely identification during an early phase is crucial. Melanoma, baseline-cell, as well as squamous cellular Ca are the deadliest subtypes of SC. Under this research, distinct SC kinds will be identified and classified utilizing machine learning and image processing techniques. Dermoscopy imaging are taken into consideration as input during the pre-processing phase. All undesirable hairs on skin lesion are removed using dull razor technique, and the picture is then smoothed using a Gaussian filtering. Median filter is employed to reduce noisiness and protect lesion's borders. Colour-based k-means grouping is carried out in the segmentation process because colour is a key element in determining the kind of malignancy. With the help of Asymmetric, Boundary, Colour, Diameter (ABCD) and Gray Level Cooccurrence (GLC) matrix, statistic and textural retraction of features is carried out. The ISIC 2019 datasets, which consists of 8 distinct kinds of dermoscopy pictures, is used for investigation conducted. Multi-class Support Vector Machine (SVM) was used for classifying with an accurateness of roughly 96.25 percent. © 2022 IEEE.

关键词： Color

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Explainable AI chatbots towards XAI ChatGPT: A review

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Heliyon 2025年第2期11卷 e42077页

作者： Kovari, Attila Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eszterhazy ter 1 Eger 3300 Hungary Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Tancsics M. 1/A Dunaujvaros 2400 Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki u. 10 Kecskemét 6000 Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Óbuda University Bécsi street 96/B Budapest 1034 Hungary

Advances in artificial intelligence (AI) have had a major impact on natural language processing (NLP), even more so with the emergence of large-scale language models like ChatGPT. This paper aims to provide a critical review of explainable AI (XAI) methodologies for AI chatbots, with a particular focus on ChatGPT. Its main objectives are to investigate the applied methods that improve the explainability of AI chatbots, identify the challenges and limitations within them, and explore future research directions. Such goals emphasize the need for transparency and interpretability of AI systems to build trust with users and allow for accountability. While integrating such interdisciplinary methods, such as hybrid methods combining knowledge graphs with ChatGPT, enhancing explainability, they also highlight industry needs for explainability and user-centred design. This will be followed by a discussion of the balance between explainability and performance, then the role of human judgement, and finally the future of verifiable AI. These are the avenues through which insights can be used to guide the development of transparent, reliable and efficient AI chatbots. © 2025 The Author

关键词： AI chatbots ChatGPT Controllable AI Explainable AI (XAI) Natural language processing (NLP) Transparency

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Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review

arXiv

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arXiv 2023年

作者： Azad, Reza Kazerouni, Amirhossein Heidari, Moein Aghdam, Ehsan Khodapanah Molaei, Amirali Jia, Yiwei Jose, Abin Roy, Rijo Merhof, Dorit Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Electrical Engineering Shahid Beheshti University Tehran Iran School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://***/mindflow-institue/Awesome-Transformer. © 2023, CC BY.

关键词： Deep neural networks

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Predictive Analytics for Hospital Readmissions Using Logistic Regression and IoT Sensor Data

Predictive Analytics for Hospital Readmissions Using Logisti...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Kiruthiga B Arshadh Ariff Mohamed Abuthahir Riazulhameed Rajapriya. M R. Sarasu N. Mohankumar A. Kasthuri Department of Management Studies SRM Valliammai Engineering College Chennai Tamil Nadu India Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Department of Management Studies Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Inayaka Mission's Research Foundation (DU) Salem Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

Hospital readmissions affect healthcare systems globally, increasing costs and morbidity. Readmissions must be predicted and prevented to improve patient outcomes and save healthcare costs. This research leverages the Internet of Things (IoT) sensor data and Logistic Regression (LR) to reduce hospital readmissions using predictive analytics. Patient health data from IoT devices during and after hospitalization is used in the paper. Heart rate, blood pressure, and oxygen saturation are constantly monitored. LR is used to examine this dataset and create a prediction model. The LR algorithm trains us to recognize IoT sensor data patterns and correlations that indicate readmission risks. Our methodology provides developed risk estimates incorporating patient-specific parameters, including age, multiple illnesses, and medical history. The temporal data permits dynamic predictions, considering patient health changes over time. This research detected hospital readmission risk with promising accuracy. These predictions allow healthcare practitioners to proactively intervene and deliver targeted treatments, including prescription modifications, lifestyle advice, and follow-up consultations to decrease readmission. This proactive strategy may improve patient outcomes and save healthcare expenses.

关键词： Logistic regression Costs Hospitals Transforms Predictive models Internet of Things Predictive analytics

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Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review

arXiv

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arXiv 2024年

作者： Lopez-Ramos, Luis M. Leiser, Florian Rastogi, Aditya Hicks, Steven Strümke, Inga Madai, Vince I. Budig, Tobias Sunyaev, Ali Hilbert, Adam Holistic Systems department Simula Metropolitan Center for Digital Engineering Oslo Norway Department of Economics and Management Karlsruhe Institute of Technology Germany Department of Neuroradiology University Hospital Bonn Germany Department of Computer Science Faculty of Informatics Norwegian University of Science and Technology Trondheim Norway QUEST Center for Responsible Research Charité - Universitätsmedizin Berlin Berlin Germany School of Computing and Digital Technology Birmingham City University Birmingham United Kingdom ETH Zurich Switzerland School of Computation Information and Technology Technical University of Munich Germany Charité - Universitätsmedizin Berlin Germany

The joint implementation of Federated learning (FL) and Explainable artificial intelligence (XAI) will allow training models from distributed data and explaining their inner workings while preserving important aspects of privacy. Towards establishing the benefits and tensions associated with their interplay, this scoping review maps those publications that jointly deal with FL and XAI, focusing on publications where an interplay between FL and model interpretability or post-hoc explanations was found. In total, 37 studies met our criteria, with more papers focusing on explanation methods (mainly feature relevance) than on interpretability (mainly algorithmic transparency). Most works used simulated horizontal FL setups involving 10 or fewer data centers. Only one study explicitly and quantitatively analyzed the influence of FL on model explanations, revealing a significant research gap. Aggregation of interpretability metrics across FL nodes created generalized global insights at the expense of node-specific patterns being diluted. 8 papers addressed the benefits of incorporating explanation methods as a component of the FL algorithm. Studies using established FL libraries or following reporting guidelines are a minority. More quantitative research and structured, transparent practices are needed to fully understand their mutual impact and under which conditions it happens. © 2024, CC BY.

关键词： Federated learning

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Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-based Classifier

Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-b...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Supantha Das Arnab Hazra Soumen Kumar Pati Soumadip Ghosh Saurav Mallik Suharta Banerjee Ayan Mukherji Aimin Li Zhongming Zhao Department of Information Technology Academy of Technology Hooghly West Bengal India Department of Computer Science and Engineering Future Institute of Technology Kolkata West Bengal India Department of Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal Haringhata Nadia West Bengal India Department of Computer Science and Engineering Sister Nivedita University New Town Kolkata West Bengal India Department of Environmental Health Harvard T H Chan School of Public Health Boston MA USA Department of Computer Science and Engineering Meghnad Saha of Institute of Technology Kolkata West Bengal India Department of Computer Science and Engineering Pailan College of Management & Technology Kolkata West Bengal India School of Computer Science & Engineering Xi’an University of Technology Xi’an Shaanxi China Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center Houston TX USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Chronic kidney disease (CKD) is as severe as cancer in today s world. It may even lead to the permanent failure of kidney. The initial detection of this disease is needed for timely cure. Our work presents a classifier (named ANFIS) in accordance with the notion of neuro-fuzzy in order to detect the existence of chronic kidney disease. We use blood test results of several patients for our research study. We compare our proposed classifier with some conventional classifiers such as Multi-layer Perceptron, Support Vector Machine, Logistic Regression and Decision Tree. Experimental results indicates that our proposed neuro-fuzzy rule-based classifier performs better than the other classifiers used here. ANFIS has given 3% to 4% better accuracy compared to the other classifiers.

关键词： Support vector machines Chronic kidney disease Bioinformatics Regression tree analysis Blood Logistics Cancer

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An Efficient Authentication Technique using Convolution Chebyshev Chaotic Maps for TMIS

An Efficient Authentication Technique using Convolution Cheb...

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2022 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2022

作者： Meshram, Chandrashekhar Obaidat, Mohammad S. Imoize, Agbotiname Lucky Bahkali, Ismail Tambare, Parkash Hsiao, Kuei-Fang Jayawanti Haksar Government Post Graduation College Department of Post Graduation Studies and Research in Mathematics M.P. Betul460001 India Computer Science Department Cybersecurity Center University of Texas-Permian Basin OdessaTX79762 United States KASIT The University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Lagos100213 Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum44801 Germany King Abdulaziz University Department of Information Science Jeddah21589 Saudi Arabia WRAM Research Lab Pvt. Ltd. Nagpur440027 India University of Texas-Permian Basin Computer Science Department OdessaTX79762 United States

ISBN: (数字)9781665486170

ISBN: (纸本)9781665486170

Authentication mechanisms for Telecare Medicine Information Systems (TMIS) have been extensively researched to safeguard the privacy and integrity of sensitive user data, such as telephone numbers, health information, medical records, etc. This paper presents a new efficient authentication technique for TMIS leveraging convolution Chebyshev chaotic maps. Based on the findings of this investigation, the suggested technique is secure against password guessing, privileged insider, replay, stolen verifier, and man-in-the-middle attacks. Generally, the computational costs of the proposed authentication technique outperform other competing techniques in the existing literature. © 2022 IEEE.

关键词： Convolution

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Efficient Visual Recognition:A Survey on Recent Advances and Brain-inspired Methodologies

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Machine Intelligence research 2022年第5期19卷 366-411页

作者： Yang Wu Ding-Heng Wang Xiao-Tong Lu Fan Yang Man Yao Wei-Sheng Dong Jian-Bo Shi Guo-Qi Li Applied Research Center Laboratory Tencent Platform and Content Group Shenzhen 518057China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi′an Jiaotong UniversityXian710049China School of Artificial Intelligence Xidian UniversityXi'an 710071China Division of Information Science Nara Institute of Science and TechnologyNara 6300192Japan Peng Cheng Laboratory Shenzhen 518000China Department of Computer and Information Science University of PennsylvaniaPhiladelphia PA19104-6389USA Institute of Automation Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Visual recognition is currently one of the most important and active research areas in computer vision,pattern recognition,and even the general field of artificial *** has great fundamental importance and strong industrial needs,particularly the modern deep neural networks(DNNs)and some brain-inspired methodologies,have largely boosted the recognition performance on many concrete tasks,with the help of large amounts of training data and new powerful computation *** recognition accuracy is usually the first concern for new progresses,efficiency is actually rather important and sometimes critical for both academic research and industrial ***,insightful views on the opportunities and challenges of efficiency are also highly required for the entire *** general surveys on the efficiency issue have been done from various perspectives,as far as we are aware,scarcely any of them focused on visual recognition systematically,and thus it is unclear which progresses are applicable to it and what else should be *** this survey,we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches,including efficient network compression and dynamic brain-inspired *** investigate not only from the model but also from the data point of view(which is not the case in existing surveys)and focus on four typical data types(images,video,points,and events).This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

关键词： Visual recognition deep neural networks(DNNS) brain-inspired methodologies network compression dynamic inference survey

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Practical limitations on robustness and scalability of quantum Internet

arXiv

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arXiv 2023年

作者： Sadhu, Abhishek Somayajula, Meghana Ayyala Horodecki, Karol Das, Siddhartha Raman Research Institute Karnataka Bengaluru560080 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Gachibowli Telangana Hyderabad500032 India Department of Computer Science Engineering G Narayanamma Institute of Technology and Science Telangana Hyderabad500104 India Institute of Informatics National Quantum Information Centre Faculty of Mathematics Physics and Informatics University of Gdańsk Wita Stwosza 57 Gdańsk80-308 Poland International Centre for Theory of Quantum Technologies University of Gdańsk Wita Stwosza 63 Gdańsk80-308 Poland

The realization of a desirably functional quantum Internet is hindered by fundamental and practical challenges such as high loss during transmission of quantum systems, decoherence due to interaction with the environment, fragility of quantum states under measurement, etc. We study the implications of these constraints by analyzing the limitations on the scaling and robustness of the quantum Internet. Considering quantum networks, we present practical bottlenecks for secure communication, delegated computing, and resource distribution among end nodes. In particular, we show that the device-independent quantum key distribution (DI-QKD) network of isotropic states with parameter p has a finite diameter depending on p. Motivated by the power of abstraction in graph theory (in association with quantum information theory), that allows us to consider the topology of a network of users spatially separated over long distances around the globe or separated as nodes within the small space over a processor or circuit within a same framework, we consider graph-theoretic quantifiers to assess network robustness and provide critical values of communication lines for viable communication over quantum Internet. We consider some quantum networks of practical interest, ranging from satellite-based networks connecting far-off spatial locations to currently available quantum processor architectures within computers, and analyze their robustness to perform quantum information processing tasks. Some of these tasks form primitives for delegated quantum computing, e.g., entanglement distribution and quantum teleportation, that are desirable for all scale of quantum networks, be it quantum processors or quantum Internet. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph theory

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