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17th Asian Conference on Computer Vision, ACCV 2024

作者： Anh, Duy Le Dinh Tran, Kim Hoang Nguyen, Quang-Thuc Le, Ngan Hoang University of Arkansas FayettevilleAR United States Faculty of Information Technology and Software Engineering Lab University of Science VNU-HCM Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819609161

Despite recent progress, Multi-Object Tracking (MOT) continues to face significant challenges, particularly its dependence on prior knowledge and predefined categories, complicating the tracking of unfamiliar objects. Generic Multiple Object Tracking (GMOT) emerges as a promising solution, requiring less prior information. Nevertheless, existing GMOT methods, primarily designed as OneShot-GMOT, rely heavily on initial bounding boxes and often struggle with variations in viewpoint, lighting, occlusion, and scale. To overcome the limitations inherent in both MOT and GMOT when it comes to tracking objects with specific generic attributes, we introduce Grounded-GMOT, an innovative tracking paradigm that enables users to track multiple generic objects in videos through natural language descriptors. Our contributions begin with the introduction of the G2MOT dataset, which includes a collection of videos featuring a wide variety of generic objects, each accompanied by detailed textual descriptions of their attributes. Following this, we propose a novel tracking method, KAM-SORT, which not only effectively integrates visual appearance with motion cues but also enhances the Kalman filter. KAM-SORT proves particularly advantageous when dealing with objects of high visual similarity from the same generic category in GMOT scenarios. Through comprehensive experiments, we demonstrate that Grounded-GMOT outperforms existing OneShot-GMOT approaches. Additionally, our extensive comparisons between various trackers highlight KAM-SORT’s efficacy in GMOT, further establishing its significance in the field. Project page: https://***/G2MOT. The source code and dataset will be made publicly available. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Kalman filters

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A Case-Finding Clinical Decision Support System to Identify Subjects with Chronic Obstructive Pulmonary Disease Based on Public Health Data

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Tsinghua Science and technology 2023年第3期28卷 525-540页

作者： Xinshan Lin Yi Lei Jun Chen Zhihui Xing Ting Yang Qing Wang Chen Wang Department of Pulmonary and Critical Care Medicine China-Japan Friendship HospitalBeijing 100029China School of Software Engineering the Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Intelligent Healthcare Unit Baidu IncBeijing 100093China Department of Automation Tsinghua UniversityBeijing 100084China Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100005China

Chronic obstructive pulmonary disease(COPD)is a serious chronic respiratory *** the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the *** the continuous development of medical digitization,the application of big data informatization in the medical and health fields has become ***,applying innovative technologies such as big data analysis,machine learning,and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research *** on the identification and diagnosis of COPD in the high-risk population,this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health *** results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.

关键词： artificial intelligence machine learning case finding chronic obstructive pulmonary disease(COPD) clinical decision support system(CDSS)

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Optimization for Deep Takagi-Sugeno-Kang Fuzzy Classifier By Self-Adaptive Hybrid Search Evolutionary Algorithm with Competitive Behavior

Optimization for Deep Takagi-Sugeno-Kang Fuzzy Classifier By...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiao Feng Zhiwen Zheng Yongbin Yu Xiangxiang Wang Jingya Wang Xinyi Han Ziyue Zhang Jingye Cai Shiping Wen School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Australian AI Institute Faculty of Engineering and Information Technology The University of Technology Sydney NSW Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

To enhance the performance of a Takagi-Sugeno-Kang fuzzy classifier (TSKFC) on classification tasks, multiple single TSKFC models regarded as blocks to construct a deep TSKFC in series. Moreover, a novel evolutionary algorithm (EA) named hybrid search evolutionary algorithm with competitive behavior (C-SaHSEA) was proposed to search the best architecture of the deep TSKFNN for the different classification tasks. C-SaHSEA is a novel algorithm combined with two search algorithms owning strong exploration and exploitation characteristics separately which can be support to each other to enhance the search ability and stability through the hybrid search. Moreover, self-adaptive update laws for the parameters of mutation and crossover operators were adopted to alleviate the difficulty of complex design and the influence of additional parameters on the search ability. Considering the computation cost of the optimization task of the optimal architecture search, a mechanism called competitive behavior was deployed into the proposed search algorithm. To demonstrate the superiority of the C-SaHSEA, eight EAs was conducted as comparison methods on five test functions. The results of mean fitness and standard deviation fitness demonstrated the search ability and stability of the C-SaHSEA. Then, eight dataset was used to validate the performance improvement of the deep TSKFC optimized by the C-SaHSEA. By comparing with the other nine methods, the high-performance of the optimized deep TSKFC has been improved.

关键词： Costs Simulation Evolutionary computation Computer architecture Stability analysis Classification algorithms Takagi-Sugeno model

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A Hybrid Heuristic Algorithm for Solving COVID-19’s Social Distancing at Universities Campus

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Computer Systems Science & engineering 2022年第6期41卷 933-944页

作者： Hassan Al-Tarawneh Khalid Al-Kaabneh Aysh Alhroob Hazem Migdady Issam Alhadid Department of Data sciences and Artificial Intelligence Al-Ahlyyia Amman UniversityAmmanJordan Department of Software Engineering Isra UniversityAmmanJordan Department of CSMIS Oman college of management and technologyMuscatOman Faculty of Information Technology and Systems University of JordanAqabaJordan

Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physical attendance ofthe populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providingassistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main ***, a distance of two meters must be maintained between each student inside theclassroom. Second, no classrooms should contain more than 20% of their regularcapacity. Third, there would be no back-to-back classes. Lastly, no lectures shouldbe held simultaneously in adjacent classrooms. The proposed approach wasimplemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling coursetimetables at Al-Ahlyyia Amman University. However, the experimental resultsshow that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, theapproach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that,the proposed technique could help educational institutes to resume their regularoperations while complying with the social distancing guidelines.

关键词： COVID-19 social distance variable neighborhood search adaptive neighborhood structure university course timetable

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A Novel Development of Virtual Wardrobe Design Based on Artificial Intelligence and Internet of Things Enabled Technologies

A Novel Development of Virtual Wardrobe Design Based on Arti...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sugadev, M. Niranjana, S. Deepa, N. Dhaarani, T.G. Giri, Jayant Samhan, A. Abdelhafiz Ali Sathyabama institute of science and technology Department of Ece Chennai India Rajalakshmi Institute of Technology Department of Ai&ds Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India Nandha Engineering College Department of Ece Erode India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Almost every day, every person encounters the dilemma of what to wear. The issue is not that the users lack access to a sufficient number of outfits to wear;rather, it is that they are unable to keep track of the clothing they keep donning and have not touched in a long time. Not only does a smart wardrobe assist the user in maintaining a record of their clothing, but it also provides recommendations for what to wear based on the user's calendar events. The system generates statistical reports that reflect the frequency of utilization of the clothing, enabling the user to determine which ones are suitable for donation or sale. Additionally, a mobile application enables users to manage their clothing from any location. In this study, a novel approach is introduced, the IoT Virtual Wardrobe (IoTVW), which is cross-validated against the conventional model Classical Controller-Assisted Wardrobe (CCW), with the assistance of the Internet of Things (IoT) and Artificial Intelligence (AI). The proposed scheme is evaluated for its efficiency. The primary objective is to integrate a collection of technologies into a system that can be utilized to assist the user in the planning of their clothing. To accomplish this objective, it is imperative that we are capable of distinguishing between garments that are frequently worn and those that are seldom worn. It is necessary to organize our clothing into categories such as casual, formal, and function wear. The application will furnish statistics that illustrate the garments that are frequently worn and those that have not been handled in an extended period. © 2024 IEEE.

关键词： Smart textiles

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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technology acceptance model (TAM) for mobile durian plantation apps(Pro Durian);The analysis for Malaysian farmers 5

Technology acceptance model (TAM) for mobile durian plantati...

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5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Kamarudin, Noraziahtulhidayu Yusoff, Aiman Basari, Abd Samad Hasan Safar, Noor Zuraidin Bin Mohd Hussin, Mohd Hatta Universiti Tun Hussein Onn Multimedia and Signal Processing Focus Group Faculty of Computer Science and Information Department of Multimedia Malaysia Universiti Tun Hussein Onn Faculty of Computer Science and Information Department of Multimedia Malaysia Universiti Tun Hussein Onn Faculty of Computer Science and Information Technology Department of Software Engineering Malaysia Universiti Tun Hussein Onn Faculty of Computer Science and Information Department of Information Security and Web Technology Malaysia Pertubuhan Peladang Senggarang Johor Darul Takzim Malaysia

ISBN: (纸本)9798350337525

During the application development process, it is important for a developer to analyze user requirements. This is crucial to the success of interactive systems and is an essential component of the design of information systems. User requirement analysis aids developers in understanding and analyzing users' needs and technology requirements. Throughout this case study, it is able to forecast the understanding, and technology acceptance, and at the same time improve the quality of work, reducing training and support costing and perennially improve customer or user satisfaction. The method of research selected is questionnaires that focused on collecting the data directly from Malaysian farmers through Google Forms as an online medium. The method was chosen because it is easy to implement and inexpensive due to the current situation of Covid-19. The questionnaire is designed with two sections where;a) includes the participant's demography to analyze information of the intended audience while b)focused on the knowledge of participants about recognition images. Results demonstrate the farmer's opinion and knowledge of the technology of image recognition in the agriculture sector. The quantitative research was undertaken using an online platform due to the Covid-19 pandemic. From the analysis, it is justified that the technology Acceptance Model (TAM) together with the mobile farming image recognition app is useful in collecting farmers' feedback and positive results have an outcome on the challenges faced by the durian farmers. © 2023 IEEE.

关键词： Image recognition

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A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

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Computers, Materials & Continua 2022年第7期72卷 73-92页

作者： Farman Ali Sadia Khan Arbab Waseem Abbas Babar Shah Tariq Hussain Dongho Song Shaker EI-Sappagh Jaiteg Singh Department of Software Sejong UniversitySeoul05006Korea Institute of Computer Sciences&Information Technology The University of AgriculturePeshawar25130Pakistan College of Technological Innovation Zayed UniversityDubai19282UAE Department of Software Korea Aerospace UniversitySeoul10540Korea Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Information Systems Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13518Egypt Chitkara University Institute of Engineering&Technology Chitkara UniversityRajpura140401India

Medical Image Analysis(MIA)is one of the active research areas in computer vision,where brain tumor detection is the most investigated domain among researchers due to its deadly *** tumor detection in magnetic resonance imaging(MRI)assists radiologists for better analysis about the exact size and location of the ***,the existing systems may not efficiently classify the human brain tumors with significantly higher *** addition,smart and easily implementable approaches are unavailable in 2D and 3D medical images,which is the main problem in detecting the *** this paper,we investigate various deep learning models for the detection and localization of the tumor in MRI.A novel twotier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second ***,in this paper,we introduce a well-annotated dataset comprised of tumor and normal *** experimental results demonstrate the effectiveness of the proposed framework by achieving 97%accuracy using GoogLeNet on the proposed dataset for classification and 83%for localization tasks after finetuning the pre-trained you only look once(YOLO)v3 model.

关键词： Tumor localization MRI Image classification GoogLeNet YOLOv3

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The Impact of Generative Artificial Intelligence on Learning: A Case Study at the University of Petra

The Impact of Generative Artificial Intelligence on Learning...

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Cyber Resilience (ICCR), International Conference on

作者： Nuha El-Khalili May Al-Nashashibi Salam Al-E'mari Software Engineering Department Faculty of Information Technology University of Petra Amman Jordan Data science & Artificial Intelligence Department Faculty of Information Technology University of Petra Amman Jordan Information Security Department Faculty of Information Technology University of Petra Amman Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Recently, a noticeable trend of using Generative Artificial Intelligence (GAI) within the educational field has raised interest to study the shape and impact of this utilization. This study investigates the opinions of information technology (IT) undergraduate students about the utilization of GAI during the learning process, focusing on their perception of the quality of produced information, challenges and concerns while using it, how they utilize it during learning, and long-term impact of using GAI. An electronic survey was distributed as a Google from to undergraduate students from five IT programs at the University of Petra. The results of 122 students revealed that 82% use GAI while learning. The main use of GAI (80% of respondents) was for solving programming problems, while 66% of respondents use GAI to write reports and express their ideas. The majority (86 % ) of participants evaluate the presented information from GAI before using them, since they believe that sometimes the information is incorrect. 89% of respondents believe that they can learn from GAI tools, however, 95% of them feel it is necessary to train them on the proper utilization of GAI in education, and, 76% think that regulations must be released about the acceptable use of GAI.

关键词： Training Surveys Privacy Generative AI Statistical analysis Shape Regulation

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus 25

Transformer-based Text Classification on Unified Bangla Mult...

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25th International Arab Conference on information technology, ACIT 2024

作者： Sourav, Md Sakib Ullah Mahmud, Mohammad Sultan Zheng, Hua Aljaidi, Mohammad Talukder, Md. Simul Hasan Bin Sulaiman, Rejwan Nur, Abdullah Hafez Al-Qerem, Ahmad Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shenzhen University College of Computer Science and Software Engineering Senzhen China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou510990 China Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Dhaka University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Northumbria University Newcastle Tayne School of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Due to its importance in studying people's thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world's most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available. © 2024 IEEE.

关键词： Classification (of information)

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