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arXiv 2024年

作者： Rao, Akash K. Uttrani, Shashank Men-On, Vishnu K. Shah, Darshil Bhavsar, Arnav Chowdhury, Shubhajit Roy Dutt, Varun Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Himachal Pradesh India Ashoka Centre for Social and Behavior Change Delhi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Himachal Pradesh India

Predicting and understanding the changes in cognitive performance, especially after a longitudinal intervention, is a fundamental goal in neuroscience. Longitudinal brain stimulation-based interventions like transcranial direct current stimulation (tDCS) induce short-term changes in the resting membrane potential and influence cognitive processes. However, very little research has been conducted on predicting these changes in cognitive performance post-intervention. In this research, we intend to address this gap in the literature by employing different EEG-based functional connectivity analyses and machine learning algorithms to predict changes in cognitive performance in a complex multitasking task. Forty subjects were divided into experimental and active-control conditions. On Day 1, all subjects executed a multitasking task with simultaneous 32-channel EEG being acquired. From Day 2 to Day 7, subjects in the experimental condition undertook 15 minutes of 2mA anodal tDCS stimulation during task training. Subjects in the active-control condition undertook 15 minutes of sham stimulation during task training. On Day 10, all subjects again executed the multitasking task with EEG acquisition. Source-level functional connectivity metrics, namely phase lag index and directed transfer function, were extracted from the EEG data on Day 1 and Day 10. Various machine learning models were employed to predict changes in cognitive performance. Results revealed that the multi-layer perceptron and directed transfer function recorded a cross-validation training RMSE of 5.11% and a test RMSE of 4.97%. We discuss the implications of our results in developing real-time cognitive state assessors for accurately predicting cognitive performance in dynamic and complex tasks post-tDCS intervention. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Empowering Precision Machine: Deep Learning Technique for Lung Cancer Diagnosis

Empowering Precision Machine: Deep Learning Technique for Lu...

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computing and Data Analytics (ICCDA), International Conference on

作者： Danish Jamil Syed Mehr Ali Shah Muhammad Numan Ali Khan Fatma Mahfoodh Mohammed AlJarwani Galician Studies and Development Institute (IDEGA) University of Santiago de Compostela Spain Department of Software Engineering Sir Syed University of Engineering & Technology College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Sultanat of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics.

关键词： Deep learning Technological innovation Computed tomography Computational modeling Precision medicine Lungs Lung cancer Medical services Data models Medical diagnostic imaging

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METHODS FOR THE EVALUATION OF UX OVER TIME 16

METHODS FOR THE EVALUATION OF UX OVER TIME

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16th International Conference on Interfaces and Human Computer Interaction, IHCI 2022, and 15th International Conference on Game and Entertainment Technologies 2022, GET 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022

作者： Mkpojiogu, Emmanuel O.C. Okeke-Uzodike, Obianuju E. Omopariola, Victor School of Computing Universiti Utara Malaysia UUM Sintok06010 Malaysia Department of Software Engineering Veritas University Abuja Nigeria Department of Applied Management Durban University of Technology KwaZulu-Natal Pietermaritzburg South Africa Department of Computer and Information Technology Veritas University Abuja Nigeria

ISBN: (纸本)9789898704405

In this paper a literature review strategy was utilized to assess the methods of evaluating UX longitudinally. The review revealed that studies on user experience evaluation over time are rare. This rareness is attributed to the cost involved in carrying out such studies. This research nonetheless suggests that lack of sufficient information and adequate knowledge on methods used for the evaluation of UX over time may also be the reason for the scarcity of studies on long-term user experience evaluation. On this note, this paper reviewed previous works on long-term evaluation of UX to elicit the methods employed in conducting such evaluation to inform possible researchers on the existing methods. The outcome of the study indicated a number of methods for the evaluation of UX over time. © 2022 International Conference on Interfaces and Human Computer Interaction

关键词： Human computer interaction

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Predicting Lung Cancer Risk Using Artificial Intelligence

Predicting Lung Cancer Risk Using Artificial Intelligence

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computing and Data Analytics (ICCDA), International Conference on

作者： Syed Mehr Ali Shah Danish Jamil Muhammad Numan Ali Khan Fatma Mahfoodh Mohammed Al-Jarwani College of Computing & Information Sciences University of Technology and Applied Sciences shinas Sultanat of Oman Galician Studies and Development Institute (IDEGA) University of Santiago de Compostela Spain Department of Software Engineering Sir Syed University of Engineering & Technology

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This research proposes a method to accurately forecast the risk of lung cancer in its initial stages by the combination of medical diagnosis and artificial intelligence. Two distinct models are employed: a symptoms-based model utilizing linear regression, and VGG19, a component of CNNs is employed for the analysis of chest x-ray images. The objective of this dual-model approach is to improve comprehensiveness and precision in the early prediction of lung cancer risk. The JSRT and Chest Xray-14 datasets were utilized to train and validate the convolutional neural network (CNN). The Regression model was trained using the Survey Lung cancer Dataset. This initiative prioritizes user-friendliness, interpretability, and ethical considerations of healthcare AI. Initial positive results show that this combined approach has a lot of potential for early detection

关键词： Surveys Analytical models Ethics Accuracy Linear regression Lung cancer Convolutional neural networks Medical diagnosis Artificial intelligence X-ray imaging

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QI-BRiCE: Quality Index for Bleeding Regions in Capsule Endoscopy Videos

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Computers, Materials & Continua 2021年第5期67卷 1697-1712页

作者： Muhammad Arslan Usman Muhammad Rehan Usman Gandeva Bayu Satrya Muhammad Ashfaq Khan Christos Politis Nada Philip Soo Young Shin Faculty of Science Engineering and ComputingKingston UniversityLondonKT12EEUK School of Electrical Engineering Superior UniversityLahorePakistan School of Applied Sciences Telkom UniversityBandungIndonesia Department of Network Computing System Architecture LabDongguk UniversitySeoulKorea Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

With the advent in services such as telemedicine and telesurgery,provision of continuous quality monitoring for these services has become a challenge for the network *** standards for provision of such services are application specic as medical imagery is quite different than general purpose images and *** paper presents a novel full reference objective video quality metric that focuses on estimating the quality of wireless capsule endoscopy(WCE)videos containing bleeding *** regions in gastrointestinal tract have been focused in this research,as bleeding is one of the major reasons behind several diseases within the *** method jointly estimates the diagnostic as well as perceptual quality of WCE videos,and accurately predicts the quality,which is in high correlation with the subjective differential mean opinion scores(DMOS).The proposed combines motion quality estimates,bleeding regions’quality estimates based on support vector machine(SVM)and perceptual quality estimates using the pristine and impaired WCE *** method Quality Index for Bleeding Regions in Capsule Endoscopy(QI-BRiCE)videos is one of its kind and the results show high correlation in terms of Pearson’s linear correlation coefcient(PLCC)and Spearman’s rank order correlation coefcient(SROCC).An F-test is also provided in the results section to prove the statistical signicance of our proposed method.

关键词： Automated bleeding detection high efciency video coding video quality assessment wireless capsule endoscopy

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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Comparison of document similarity algorithms in extracting document keywords from an academic paper 7

Comparison of document similarity algorithms in extracting d...

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Miah, M. Saef Ullah Sulaiman, Junaida Azad, Saiful Zamli, Kamal Z. Jose, Rajan Universiti Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Universiti Malaysia Pahang Center for Data Science and Artificial Intelligence Data Science Center Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Green University of Bangladesh Department of Cse Dhaka Bangladesh Universiti Malaysia Pahang Faculty of Industrial Sciences and Technology Gambang Malaysia

ISBN: (纸本)9781665414074

The idea of this study is to validate a list of keywords derived from a scientific article by a domain expert from years of knowledge with prominent document similarity algorithms. For this study, a list of handcrafted keywords generated by Electric Double Layer Capacitor (EDLC) experts are chosen, and relevant documents to EDLC are considered for the comparison. Then, different similarity calculation algorithms were employed in different settings on the documents such as using the whole texts of the documents, selecting the positive sentences of the documents, and generating similarity score with automatically extracted keywords from the documents. The experiment's outcome provides us with findings that the machine-generated keywords are mostly similar to the curated list by the domain experts. This study also suggests the preferable algorithms for similarity calculation and automated key-phrase extraction for the EDLC domain. © 2021 IEEE.

关键词： Supercapacitor

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Evaluating the Knowledge in Test Engineering Education using a Solar-powered Remote-controlled Flying Probe-Inspired In-Circuit Tester

Evaluating the Knowledge in Test Engineering Education using...

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International Symposium for Design and technology of Electronics Packages (SIITME)

作者： Sorin Liviu Jurj Raul Rotar Ioana Brîncovan Flavius Oprițoiu Mircea Vlăduțiu Computers and Information Technology Department Advanced Computing Systems and Architectures (ACSA) Laboratory “Politehnica” University of Timisoara Timisoara Romania Department of Automation and Applied Informatics Aurel Vlaicu University Arad Romania

ISBN: (数字)9798331539511

ISBN: (纸本)9798331539528

This paper presents a solar-powered remote-controlled Flying Probe-Inspired In-Circuit Tester (FPICT) designed to improve individuals’ knowledge and practical skills in the Test Engineering Education (TEE) domain. The proposed system consists of a user-friendly modern website that offers three main functions: (a) access to TEE-related documentation and tests, (b) a real-time webcam feed for remote monitoring, access, control, and execution of real-time tests on multiple Printed Circuit Boards (PCBs) using the FPICT platform, and (c) performance assessment of users’ TEE knowledge improvement. The effectiveness of the FPICT in enhancing TEE knowledge is evaluated by comparing the score distribution of users before and after the initial tests. Additionally, the power consumption of the FPICT platform is measured, and the feasibility of powering the system with solar energy is investigated. Experimental results demonstrate that the remote-controlled FPICT successfully increases the TEE-related knowledge of web users by an average of $26 \%$ over an average duration of 50 minutes, highlighting its efficacy in teaching theoretical concepts of In-Circuit Testing (ICT) and developing practical skills in the Flying Probe Testing (FPT) domain.

关键词： Knowledge engineering Power measurement Webcams Printed circuits Solar energy Real-time systems Probes Engineering education Remote monitoring Testing

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Skin Cancer Detection Using Constrained Neural Networks and the Development of an Optimized Technical Infrastructure for Resource-Efficient Diagnosis

Skin Cancer Detection Using Constrained Neural Networks and ...

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E-Health and Bioengineering Conference (EHB)

作者： Daniel-Mihai Armanda Alin-Gabriel Cococi Ioana Dogaru Radu Dogaru Doctoral School of Electronics Telecommunications and Information Technology University “Politehnica” of Bucharest Romania Dept. of Applied Electronics and Information Eng. Natural Computing Laboratory University “Politehnica” of Bucharest Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

This work addresses the challenge of detecting skin cancer from dermatological images using artificial intelligence. The focus is on exploring the potential of lightweight convolutional neural networks to develop efficient diagnostic tools that can function on portable, resource-constrained devices, ensuring accessibility for medical personnel. A comparison of several TinyML models is presented, considering both diagnostic accuracy and computational demands. Some recently introduced models (V-CNN and VRES-CNN) were evaluated against the well-established EfficientN et or ResN et and the results indicated certain advantages for the recent models. Particularly, a quantization technique applied to V-CNN and VRES-CNN was found to further reduce their complexity while maintaining a high accuracy of over 98,5% for the best model.

关键词： Accuracy Quantization (signal) Computational modeling Tiny machine learning Biological system modeling Neural networks Convolutional neural networks Personnel Medical diagnostic imaging Skin cancer

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Smart IoT-Based Solution for Emergency Assistance during Sandstorms and Landslides

Smart IoT-Based Solution for Emergency Assistance during San...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Thirupathi Regula Warda Al Hoqani Sunitha Cheriyan Sharmila K. Reddy Naveen Kumar Shazia Tazeen Kumar S. G. Kiran Department of IT College of Computing and Information Sciences University of Technology and Applied Science Muscat Oman Department of CSE Vaagdevi Engineering College Telangana India Department of CSE NIT Warangal Telangana India

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Natural disasters, which are caused by changes in the climate and geography, pose a major threat to human life and economic stability. These events, which include cyclones, tsunamis, landslides, and earthquakes, can result in serious ecological damage as well as fatalities. The speed at which climate change is occurring increases the unpredictability of such disasters, especially in vulnerable regions like deserts and forests. This study offers a solution to the challenges of rescuing those who are stranded by such catastrophes, where traditional rescue methods and communication may not be effective. The system uses Internet of Things (IoT) and mobile phone technologies to send location information and real-time alerts through Global Positioning System (GPS) and Geographic Information System (GIS), enabling the timely location of a person or vehicle for effective emergency response. This approach aims to improve disaster preparedness and survival rates in remote, catastrophe-prone areas.

关键词： Landslides Disasters Radar tracking Emergency services Tsunami Real-time systems Internet of Things Global Positioning System Meteorology Geographic information systems

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