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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Ibrahim, Hemin Al-Talabani, Abdulbasit K. Loo, Chu Kiong Tishk International University Information Technology Department Erbil Iraq Koya University Faculty of Engineering Department of Software Engineering Koya Iraq University of Malaya Department of Artificial Intelligence Kuala Lumpur Malaysia

ISBN: (纸本)9798350374131

Children with autism spectrum disorder (ASD) require long-term care, support, and empathy from experienced therapists. However, there is a shortage num-ber of highly experienced therapists available to provide consistent and high-quality care for all ASD children and teach them effectively. The Robotic-based assessment and treatment offer promising advantages for ASD children. However, many robotic and virtual therapies rely on pre-programmed that may not consider individual needs and cultural differences. This study aims to develop and test the hypothesis that robots can effectively assist in conducting therapy sessions for children with ASD. A Support Vector Machine (SVM) classifier has been used to predict out-comes from sckeleton and gaze basaed features of data generated during both robot-child and therapist-child in-teractions. A comprehensive evaluation across five distinct scenarios, incorporating various combinations of Robot-Enhanced Therapy (RET), Standard Human Treatment (SHT), and their concatenation have been conducted. The article results for both skeleton-based features and gaze-based data reveal a significant performance advantage over existing state-of-the-art methodologies. © 2024 IEEE.

关键词： Support vector machines

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Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

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Computers, Materials & Continua 2023年第3期74卷 5301-5317页

作者： Muhammad Ibrahim Imran Sarwar Bajwa Nadeem Sarwar Haroon Abdul Waheed Muhammad Zulkifl Hasan Muhammad Zunnurain Hussain Department of Computer Science The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Bahria University Lahore CampusLahorePakistan Software Engineering Department Faculty of ITUniversity of Central PunjabLahorePakistan Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSelangorMalaysia

Recommendation services become an essential and hot research topic for researchers *** data such asReviews play an important role in the recommendation of the *** was achieved by deep learning approaches for capturing user and product information from a short ***,such previously used approaches do not fairly and efficiently incorporate users’preferences and product *** proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True *** overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or *** proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed *** results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.

关键词： Neural sentiment classification user product attention deep collaborative filtering multivariate rating artificial intelligence

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Volumetric CT Segmentation with Mask Propagation Using Segment Anything 23

Volumetric CT Segmentation with Mask Propagation Using Segme...

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12th International Symposium on information and Communication technology, SOICT 2023

作者： Nguyen, Huu-Hung Nguyen, Cong-Tai Tran, Minh-Triet Faculty of Information Technology University of Science VNU-HCM Viet Nam National University Ho Chi Minh City Viet Nam Faculty of Information Technology Software Engineering Lab VNU-HCM Viet Nam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798400708916

Medical imaging is both an interesting and challenging field for researchers, mainly due to their lack of labeled data, especially in segmentation tasks. Many interactive system has been introduced to help streamline the annotation workflow, and most recently Segment Anything Model (SAM) has been a breakthrough as a foundation model in interactive segmentation using prompts. In this paper, we adapt SAM, a 2D segmentation for 3D organ interactive segmentation task, then we propose several performance improvement strategies and achieve a comparable result of 0.8694 of mean DSC in a full-supervised setting with a small amount of data. Furthermore, in the process of finding a method to reduce the human effort when using our algorithm, we also develop a novel method inspired by a beam search algorithm, stemming from the NLP domain, which can run volumetric segmentation for one target (i.e. the liver) with minimal human manual input, and still achieve an interesting result of 0.9410 Liver's DSC, allow it to be integrated to annotation system to aid medical expert. © 2023 ACM.

关键词： Computerized tomography

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TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates 4

TRIPORA: Intelligent Machine Learning Solution for Sri Lanka...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Legrand, T.R. Bandara, K.M.R.A.I. Stefania Crishani, J.A.D. Uvindu, L.W.P. Amarasena, Nelum Kasthurirathna, Dharshana Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Sri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study. © 2022 IEEE.

关键词： Obsolescence

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Analyze video action with a deep learning model based on space-time and context information 23

Analyze video action with a deep learning model based on spa...

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12th International Symposium on information and Communication technology, SOICT 2023

作者： Le, Cong-Luan Tran, Minh-Triet Faculty of Information Technology University of Science VNU-HCM Vietnam National University Ho Chi Minh City Viet Nam Faculty of Information Technology Software Engineering Lab University of Science VNU-HCM Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798400708916

Action detection aims to determine the temporal boundaries during which actions occur in untrimmed videos. In the real world, each moment in a video can involve multiple actions, making it challenging to detect and label actions accurately. In recent years, the effective interaction of relationships between short-term and long-term information contributes an important step forward for the efficient extraction of significant factors in temporal information. However, one aspect has not been highlighted in previous studies but it can include useful context information. The aspect is the use of features related to the region of interest (RoI - Region of Interest). Adding information about regions containing major interactions, as in the case of using the RoI feature by humans, can help focus the model on the most important parts of the temporal data. This is especially useful in dealing with complex situations and helps predict action more exactly. However, when applying the RoI features, there will be a challenge that some frames in the video may be missed. This can lead to the loss of important information and affect predictability. In this work, we propose a Context Normalization Module to address this problem. In summary, in this work, we have three main contributions: Proposing an enhanced framework that combines spatial-temporal information and context information, applying the RoI feature (specifically, human feature) as the context information to the model to improve the performance of the model, and applying KNN to handle information loss in extracting RoI information. © 2023 ACM.

关键词： Deep learning

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Assessing Cybersecurity Awareness among the Hashemite University Students in Terms of Computer Usage 16

Assessing Cybersecurity Awareness among the Hashemite Univer...

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16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Taamneh, Salah Wedyan, Fadi Obiedat, Mamoon Salameh, Hani Bani The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Computer Science and Applications Zarqa Jordan Lewis University Department of Engineering Computing and Mathematical Sciences RomeovilleIL United States The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Information Technology Zarqa Jordan The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Software Engineering Zarqa Jordan

ISBN: (纸本)9798350371154

Internet usage among information technology users has expanded substantially, as have cybercrimes with potentially disastrous repercussions. Therefore, internet users have to employ the available security measures and policies during internet usage. Nowadays, University students make up a sizable portion of internet users. This study assesses the cybersecurity awareness among The Hashemite University students In terms of Computer Usage. A questionnaire survey is used to assess students' cybersecurity knowledge while using their PCs and connecting to the internet, concentrating on their behaviors and use of security measures. © 2024 IEEE.

关键词： Cybersecurity

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Research on Faster R-CNN material micro-defect detection method for small data volume

Research on Faster R-CNN material micro-defect detection met...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Zhili Wang Hairong Yan Weiyu Zhou Software Engineering Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

Defect detection in material micro-images has a significant impact on the study of the relationship between the micro-structure and macro-properties, however, material microdefects are usually relatively small and span a wide range of scales, which increases the difficulty of defect detection. Meanwhile, since defects exist in a small number, overfitting becomes another challenge. In this paper, based on the Faster R-CNN algorithm, automated data enhancement is used to solve the overfitting, and a feature pyramid model is proposed for the defects multi-scale problem, and finally, the feasibility of the above viewpoint is verified by experiments.

关键词： Solid modeling Analytical models Automation Data models Artificial intelligence Defect detection

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OMP-ENGINEER: BRIDGING SYNTAX ANALYSIS AND IN-CONTEXT LEARNING FOR EFFICIENT AUTOMATED OPENMP PARALLELIZATION

arXiv

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arXiv 2024年

作者： Wang, Weidong Zhu, Haoran Department of Software Engineering Faculty of Information Technology Beijing University of Technology China

In advancing parallel programming, particularly with OpenMP, the shift towards NLP-based methods marks a significant innovation beyond traditional S2S tools like Autopar and Cetus. These NLP approaches train on extensive datasets of examples to efficiently generate optimized parallel code, streamlining the development process. This method’s strength lies in its ability to swiftly produce parallelized code that runs efficiently. However, this reliance on NLP models, without direct code analysis, can introduce inaccuracies, as these models might not fully grasp the nuanced semantics of the code they parallelize. We build OMP-Engineer, which balances the efficiency and scalability of NLP models with the accuracy and reliability of traditional methods, aiming to enhance the performance of automating parallelization while navigating its inherent challenges. © 2024, CC BY.

关键词： Application programming interfaces (API)

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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Computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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