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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： CH. N. V. Jyothirmai G.R. Hemalakshmi N.B. Prakash Vijay Jagdish Upadhye Harshal Patil Syed Noeman Taqui Department of CSE Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering & Technology Andhra Pradesh India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electrical and Electronics Engineering National Engineering College Kovilpatti India Department of Microbiology Research and Development Cell Parul Institute of Applied Sciences Parul University Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Maharashtra India Department of VLSI Microelectronics. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Therefore, Convolutional Neural Networks (CNNs) are capable of predicting or extrapolating from identified patterns to unknown data, enabling the analysis and extraction of knowledge from large images, in order to produce consistent outputs for inputs not previously presented. On the downside, the training process takes a long time as it is computationally expensive. Aiming to reduce the computational time to perform the training of the neural network (NN), and the analysis of images during the testing process, as well as taking advantage of the natural distributed characteristic of ANN, it was decided to transfer the processing-intensive parts of the tool from CPU (Central Processing Unit) to a GPU (Graphics Processing Unit). Finally, simulator tests were carried out, with consequent verification of the effectiveness of diagnoses on ultrasound images of the liver. The accuracy and total processing time for carrying out the analyses proposed during this research were then evaluated, making a comparison between the network training and testing times obtained on the CPU and GPU. It can also be seen - in both Tr&Tt cases - that GPU had a higher gain than CPU (about 10.6 times faster), even when dealing with a small database, with 120 images of 202500 pixels each.

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A Lightweight Feature Selection Method Based on Rankability

SSRN

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SSRN 2023年

作者： Kong, Lingping Silva, Juan Domingo Velasquez Perfilieva, Irina Pant, Millie Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Industrial Engineering University of Chile Santiago Chile Santiago Chile University Hospital Ostrava Czech Republic Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India Instituto Sistemas Complejos de Ingeniería Santiago Chile Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India

Feature selection, as one of the essential dimensionality reduction techniques, hasbecome one popular yet challenging area in the field, such as data mining andmachine learning. Unlike feature extraction (such as principle component analysis andnon-negative matrix factorization), preserving the entire information but losing thefeature relevance. Data processing in feature selection will lose information, leading tothe need to develop lightweight, efficient, and practical methods that preserve the datainformation as much as possible while performing dimensional reduction. In this paper,we propose a rankability-based feature selection method. The rankability concept wasproposed in 2019, similar to the entropy concept, and has not been studied widely *** proposed method is lightweight in terms of complexity, which requires no iterativeoptimization or auxiliary estimator *** experimented with sixteen datasets and compared our results with four otheralgorithms. The results show that our rankability-based feature selection methodoutperforms the fuzzy entropy-based method on five datasets in eight, and the averageaccuracy increased by 0.1482, 0.1078, and 0.1157, respectively. Then, in the varieddimension-reducing experiments, the proposed method shows superiority on fourdatasets out of eight and is competitive with others on two datasets out of eight. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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End-to-end performance modeling of distributed GPU applications 34

End-to-end performance modeling of distributed GPU applicati...

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34th ACM International Conference on Supercomputing, ICS 2020

作者： Choi, Jaemin Richards, David F. Kale, Laxmikant V. Bhatele, Abhinav Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States

ISBN: (纸本)9781450379830

With the growing number of GPU-based supercomputing platforms and GPU-enabled applications, the ability to accurately model the performance of such applications is becoming increasingly important. Most current performance models for GPU-enabled applications are limited to single node performance. In this work, we propose a methodology for end-to-end performance modeling of distributed GPU applications. Our work strives to create performance models that are both accurate and easily applicable to any distributed GPU application. We combine trace-driven simulation of MPI communication using the TraceR-CODES framework with a profiling-based roofline model for GPU kernels. We make substantial modifications to these models to capture the complex effects of both on-node and off-node networks in today's multi-GPU supercomputers. We validate our model against empirical data from GPU platforms and also vary tunable parameters of our model to observe how they might affect application performance. © 2020 ACM.

关键词： Graphics processing unit

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Sensor-based authentication in smartphone: A systematic review

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Shuwandy, Moceheb Lazam Jouda, A.S. Ahmed, M.A. Salih, Mahmood M. Al-qaysi, Z.T. Alamoodi, A.H. Garfan, Salem Albahri, O.S. Zaidan, B.B. Albahri, A.S. Computer Science Department College of Computer Science and Mathematics Tikrit University (TU) Tikrit Iraq Salahaddin Health Office Ministry of Health Tikrit Iraq MEU Research Unit Middle East University Amman Jordan Faculty of Computing and Meta-Technology (FKMT) Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Applied Science Research Center Applied Science Private University Amman Jordan SP Jain School of Global Management Lidcombe Sydney 2141 NSW Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq

With the widespread usage of smartphones, users not just using their smartphone for calls and messaging only, but they are using it for a variety of purposes such as banking transactions, e-mailing, chatting, online shopping, video conferencing, health monitoring, and many more. Most of these activities store personal and sensitive data of the user on the device, reaching this confidential information by unauthorized person may cause huge losses and bad consequences. Therefore, securing the smartphone's accessibility from unauthorized people became an extremely essential. With the advanced development in the area of sensor-based smartphone authentication methods, different methods were developed to be easily used by users through specific criteria. However, many aspects haven't been discovered yet in the area of sensor-based smartphone authentication methods. On the basis of that, this study aims to review the area of sensor-based smartphone authentication methods systematically to provide a comprehensive understanding of this new area and to discuss the current challenges and issues. To conduct this systematic review, four scientific digital databases were utilized;scienceDirect, IEEE Xplore, Web of science, and Scopus. The total number of studies found was n = 256 articles, only n = 46 articles were included based on our inclusion criteria. To provide a comprehensive understanding of the included studies, taxonomy was drawn. Also, this study provides a discussion on the current motivations, challenges and issues, and recommendations in sensor-based smartphone authentication methods. This review enlightens, encourage, and direct researchers to develop authentication solutions and reduce the current gaps in this area. © 2024 The Authors

关键词： Authentication MHealth Microphone Sensor Password Privacy Touch Sensor

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Reflections from the Workshop on AI-Assisted Decision Making for Conservation

arXiv

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arXiv 2023年

作者： Xu, Lily Rolf, Esther Beery, Sara Bennett, Joseph R. Berger-Wolf, Tanya Birch, Tanya Bondi-Kelly, Elizabeth Brashares, Justin Chapman, Melissa Corso, Anthony Davies, Andrew Garg, Nikhil Gaylard, Angela Heilmayr, Robert Kerner, Hannah Klemmer, Konstantin Kumar, Vipin MacKey, Lester Monteleoni, Claire Moorcroft, Paul Palmer, Jonathan Perrault, Andrew Thau, David Tambe, Milind School of Engineering and Applied Sciences Harvard University United States Harvard University United States United States Department of Computer Science University of Colorado Boulder United States Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Biology Carleton University Canada Department of Computer Science and Engineering The Ohio State University United States Department of Electrical and Computer Engineering The Ohio State University United States Department of Evolution Ecology and Organismal Biology The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Google United States Electrical Engineering and Computer Science University of Michigan United States Department of Environmental Science Policy and Management University of California Berkeley United States Department of Aeronautics and Astronautics Stanford University United States Department of Organismic and Evolutionary Biology Harvard University United States Operations Research and Information Engineering Cornell Tech United States African Parks South Africa Bren School of Environmental Science and Management University of California Santa Barbara United States School of Computing and Augmented Intelligence Arizona State University United States Microsoft Research United States Department of Computer Science and Engineering University of Minnesota United States Inria Paris France United States Wwf Global Science United States

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Conservation

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RECOMED: A Comprehensive Pharmaceutical Recommendation System

arXiv

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arXiv 2022年

作者： Zomorodi, Mariam Ghodsollahee, Ismail Martin, Jennifer H. Talley, Nicholas J. Salari, Vahid Plawiak, Pawel Rahimi, Kazem Acharya, U. Rajendra Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland Department of Computer Engineering Ferdowsi University of Mashhad Iran The University of Newcastle CallaghanNSW Australia Institute for Quantum Science and Technology Department of Physics and Astronomy University of Calgary Alberta Canada Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Deep Medicine Nuffield Department of Women’s and Reproductive Health University of Oxford Oxford United Kingdom School of Mathematics Physics and Computing University of Southern Queensland ToowoombaQLD Australia

OBJECTIVES: To build datasets containing useful information from drug databases and recommend a list of drugs to physicians and patients with high accuracy by considering features of a spectrum of people, diseases, and chemicals. METHODS: A comprehensive pharmaceutical recommendation system was designed based on the features of a spectrum of people, diseases, and medicines extracted from two major drug databases, a dataset of patients and drug information. Then the recommendation was given, and the response was analysed using patient and caregiver ratings and the knowledge obtained from drug specifications and interactions. Sentiment analysis was employed by natural language processing approaches in pre-processing, along with neural network-based methods and recommender system algorithms for modelling the system. Patient conditions and medicine features were used for making two models based on matrix factorisation. Then we used drug interaction criteria to filter drugs with severe or mild interactions with other drugs. We developed a deep learning model for recommending drugs by using data from 2304 patients as a training set, and then we used data from 660 patients as our validation set. We used knowledge from critical drug information and combined the model’s outcome into a knowledge-based system with the rules obtained from constraints on taking medicine. RESULTS: The results show that our recommendation system can recommend an acceptable combination of medicines according to the existing real-life prescriptions available. Compared with conventional matrix factorisation, our proposed model improves the accuracy, sensitivity, and hit rate by 26%, 34%, and 40%, respectively. In addition, it improves the accuracy, sensitivity, and hit rate by an average of 31%, 29%, and 28% compared to other machine learning methods. We have open-sourced our implementation in Python. CONCLUSION: Compared to conventional machine learning approaches, we obtained average accuracy, sens

关键词： Recommender systems

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A Hybrid Deep Learning Paradigm for Robust Feature Extraction and Classification for Cataracts

Applied AI Letters

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applied AI Letters 2025年第2期6卷

作者： Ramakrishnan, Akshay Bhuvaneswari Madavan, Mukunth Manikandan, R. Gandomi, Amir H. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD United States Mukunth Madhavan AI Security Engineer Banglore India School of Computing SASTRA Deemed University Thanjavur India Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia University Research and Innovation Center (EKIK) Óbuda University Budapest Hungary Department of Computer Science Khazar University Baku Azerbaijan

The study suggests using a hybrid convolutional neural networks-support vector machines architecture to extract reliable characteristics from medical images and classify them as an ensemble using four different models. Manual processing of fundus images for the automated identification of ocular disorders is laborious, error-prone, and time-consuming. This necessitates computer-assisted technologies that can automatically identify different ocular illnesses from fundus images. The interpretation of the photos also plays a massive role in the diagnosis. Automating the diagnosing procedure reduces human mistakes and helps with early cataract detection. The oneDNN library available in the oneAPI Environment provided by Intel has been used to optimize all transfer learning models for better performance. The suggested approach is verified through a range of metrics in experiments using the openly accessible Ocular Disease Intelligent Recognition dataset. The MobileNet Model outperformed other transfer learning techniques with an accuracy of 0.9836. © 2025 The Author(s). applied AI Letters published by John Wiley & Sons Ltd.

关键词： cataract computer-assisted fundus hybrid ocular oneAPI oneDNN transfer learning

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Dialog chatbot as an interactive online tool in enhancing ESP vocabulary learning

Saudi Journal of Language Studies

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Saudi Journal of Language Studies 2023年第2期3卷 76-86页

作者： Fawaz Qasem Mukhtar Ghaleb Hassan Saleh Mahdi Ahmed Al Khateeb Hind Al Fadda Department of English College of Sciences and Arts and the Applied College at Al-Namas University of Bisha Bisha Saudi Arabia Department of Information Systems College of Computing and Information Technology University of Bisha Al Namas Saudi Arabia Faculty of Computer Science and Information Technology Sana'a University Sana'a Yemen Department of English Hodeidah University Hodeidah Yemen English Language Centre Taif University Taif Saudi Arabia Department of English Language College of Arts King Faisal University Al-Ahsa Saudi Arabia Department of Curriculum and Instructions King Saud University Riyadh Saudi Arabia

Purpose Based on an experimental study on English for Specific Purposes (ESP) students, at the Business department at the University of Bisha, the purpose of the study is to examine the effect of chatbot use on learning ESP in online classrooms during COVID-19 and find out how Dialogflow chabot can be a useful and interactive online platform to help ESP learners in learning vocabulary well. Design/methodology/approach The research paper is based on an experimental study of two groups, an experiential group and a controlled group. Two tests were carried out. Pre-tests and post-test of vocabulary knowledge were conducted for both groups to explore the usefulness of using the Dialogflow chatbot in learning ESP vocabulary. A designed chatbot content was prepared and included all the vocabulary details related to words' synonyms and a brief explanation of words’ meanings. An informal interview is another tool used in the study. The purpose of using the interview with the participants was to elicit more data from the participants about using the chatbot and about how and in what aspects chatbot using the conversational program was useful and productive. Findings The findings of the study explored that the use of chatbots plays a major role in enhancing and learning ESP vocabulary. That was clear as the results showed that the students who used the chatbot Dialogflow in the experimental group outperformed their counterparts in the control group. Research limitations/implications The study displays an important pedagogical implication as the use of chatbots could be applied in several settings to improve language learning in general or learning ESP courses in particular. Chatbot creates an interesting environment to foster build good interactions where negotiation of meaning takes place clearly seems to be of great benefit to help learners advance in their L2 lexical development. Originality/value Examining and exploring whether the use of chatbots play

关键词： Chatbot English for specific purposes (ESP) ESP vocabulary English as second language (ESL) Online platform

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Beneath the Surface: The Role of Underwater Image Enhancement in Object Detection

SSRN

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SSRN 2025年

作者： Awad, Ali Saleem, Ashraf Paheding, Sidike Lucas, Evan Al-Ratrout, Serein Havens, Timothy C. Department of Applied Computing College of Computing Michigan Technological University 1400 Townsend Dr Houghton49931 United States Department of Computer Science and Engineering Fairfield University FairfieldCT06824 United States Institute of Computing and Cybersystems Michigan Technological University Michigan Houghton49931 United States Department of Computer Science Michigan Technological University Michigan Houghton49931 United States

Underwater imagery often suffers from severe degradation, resulting in low visual quality and reduced object detection performance. This work aims to evaluate state-of-the-art image enhancement models, investigate their effects on underwater object detection, and explore their potential to improve detection performance. To this end, we apply nine recent underwater image enhancement models, covering physical, non-physical, and learning-based categories, to two recent underwater image datasets. Following this, we conduct joint qualitative and quantitative analyses on the original and enhanced images, revealing the discrepancy between the two analyses, and analyzing changes in the quality distribution of the images after enhancement. We then train three recent object detection models on the original datasets, selecting the best-performing detector for further analysis. This detector is subsequently re-trained on the enhanced datasets to evaluate changes in detection performance, highlighting the adverse effect of enhancement on detection performance at the dataset level. Next, we perform a correlation study to examine the relationship between various enhancement metrics and the mean Average Precision (mAP). Finally, we conduct an image-level analysis that reveals images of improved detection performance after enhancement. The findings of this study demonstrate the potential of image enhancement to improve detection performance and provide valuable insights for researchers to further explore the effects of enhancement on detection at the individual image level, rather than at the dataset level. This could enable the selective application of enhancement for improved detection. The data generated, code developed, and supplementary materials are publicly available at: https://***/RSSL-MTU/Enhancement-Detection-Analysis. © 2025, The Authors. All rights reserved.

关键词： Underwater photography

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Sensitive vPSA - Exploring Sensitivity in Visual Parameter Space Analysis

arXiv

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arXiv 2022年

作者： Fröhler, Bernhard Elberfeld, Tim Möller, Torsten Hege, Hans-Christian Maurer, Julia Heinzl, Christoph the Research Group X-Ray Computed Tomography University of Applied Sciences Upper Austria Austria the imec-Vision Lab University of Antwerp Belgium Data Science @ Uni Vienna the Faculty of Computer Science University of Vienna Austria Visual and Data-Centric Computing Department Zuse Institut Berlin Germany

The sensitivity of parameters in computational science problems is difficult to assess, especially for algorithms with multiple input parameters and diverse outputs. This work seeks to explore sensitivity analysis in the visualization domain, introducing novel techniques for respective visual analyses of parameter sensitivity in multi-dimensional algorithms. First, the sensitivity analysis background is revisited, highlighting the definition of sensitivity analysis and approaches analyzing global and local sensitivity as well as the differences of sensitivity analysis to the more common uncertainty analysis. We introduce and explore parameter sensitivity using visualization techniques from overviews to details on demand, covering the analysis of all aspects of sensitivity in a prototypical implementation. The respective visualization techniques outline the algorithmic in- and outputs including indications, on how sensitive an input is with regard to the outputs. The detailed sensitivity information is communicated through constellation plots for the exploration of input and output spaces. A matrix view is discussed for localized information on the sensitivity of specific outputs to specific inputs. A 3D view provides the link of the parameter sensitivity to the spatial domain, in which the results of the multi-dimensional algorithms are embedded. The proposed sensitivity analysis techniques are implemented and evaluated in a prototype called Sensitivity Explorer. We show that Sensitivity Explorer reliably identifies the most influential parameters and provides insights into which of the output characteristics these affect as well as to which extent. © 2022, CC BY-NC-SA.

关键词： Sensitivity analysis

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