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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Segmentation and Detection System for Varicocele Using Ultrasound Images

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computers, Materials & Continua 2022年第7期72卷 797-814页

作者： Ayman M.Abdalla Mohammad Abu Awad Omar AlZoubi La’aly A.Al-Samrraie Department of Computer Science Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science Jordan University of Science and TechnologyIrbid22110Jordan Department of Water and Environmental Engineering Al-Huson University CollegeAl-Balqa Applied UniversityIrbidJordan

The enlarged veins in the pampiniform venous plexus,known as varicocele disease,are typically identified using ultrasound *** diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male *** proposed system is designed to determine whether a patient is *** is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other ***,the experimental results of this study focused on images taken in the supine position of the left testicles of *** are two possible vein structures in each image:a cross-section(circular)and a tube(non-circular)*** proposed system identifies dilated(varicocele)veins of these structures in ultrasound images in three stages:preprocessing,processing,and detection and *** three stages are applied in three different color modes:Grayscale,Red-Green-Blue(RGB),and Hue,Saturation,and Value(HSV).In the preprocessing stage,the region of interest enclosing the pampiniform plexus area is extracted using a median filter and threshold ***,the processing stage employs different filters to perform image ***,edge detection is applied in multiple steps and the detected veins are measured to determine if dilated veins *** implementation results showed the proposed system is faster andmore effective than the previous work.

关键词： Varicocele color mode HSV median filter segmentation canny

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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How Large Language Models are Transforming Teachers' Assessment of Student Competency: A Case Study on LLM-Based Report Writing

How Large Language Models are Transforming Teachers' Assessm...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Dongyub Kim, Daejung Loeser, Martin Seo, Kyoungwon Seoul National University of Science and Technology Department of Applied Artificial Intelligence Seoul Korea Republic of Seoul National University of Science and Technology Department of Environmental Engineering Seoul Korea Republic of Electrical Engineering and Mechatronics ZHAW Zurich University of Applied Sciences Department of Computer Science Winterthur Switzerland

ISBN: (纸本)9798331510756

Evaluating student competencies is critical to fostering academic success, but the traditional process of assessment and report generation is time-consuming and labor-intensive for teachers. The emergence of Large Language Models (LLMs) presents an opportunity to automate this process, potentially reducing the workload for educators. However, concerns such as hallucinations in LLM outputs and the lack of usability studies involving teachers raise questions about their reliability and practical application. In response, we developed an LLM-based report writing system specifically designed for real-time competency evaluation. To assess its effectiveness, we conducted a case study involving five educational experts, each with over 15 years of experience. These experts used the system to generate student competency reports and found them to be both sensible and specific. While they expressed concerns about issues like insufficient detail, they recognized the system's significant time-saving benefits. Our findings demonstrate that LLMs can positively impact teachers' competency assessments by streamlining the reporting process, offering valuable support for educators. © 2025 IEEE.

关键词： Students

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Smart Traffic Control: Adaptive Signal Management Based on Real-time Lane Detection using YOLOv8

Smart Traffic Control: Adaptive Signal Management Based on R...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Ravichandran, M. Laxmikant, Kumar Muthu, Annamalai Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology & Applied Science Department of Electrical and Electronics Engineering Nizwa Oman

ISBN: (纸本)9798350382723

As cities grow, handling traffic in big urban are-as becomes a huge proble-m. More cars on the road and not enough roads le-ad to heavy traffic jams. This increases trave-l time and harms our environment. Our study tackle-s these problems with a ne-w approach. We use real-time- lane detection with YOLOv8 and adaptable- traffic lights. Using sharp computer vision, our system pinpoints vehicle-s. It adjusts traffic light timings on-the-go to improve traffic flow. Our model works e-xtremely accurately. During the- learning phase, it achieve-d a mAP50 score of 99.3% and a mAP50-95 score of 87.4%. In the te-sting phase, it got a mAP score of 99.2% and a mAP50-95 score of 86.2%. The re-sults highlight how the system can improve city trave-l. It's valuable for city planners and traffic officials. It helps the-m understand smart transportation systems bette-r. © 2024 IEEE.

关键词： computer vision

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Machine Learning for Indoor Localization using CSI Technique 21

Machine Learning for Indoor Localization using CSI Technique

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Lee, Wilaiporn Suwanasri, Cattareeya Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand

ISBN: (纸本)9798350377088

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others. © 2024 IEEE.

关键词： Adversarial machine learning

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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AI generated route data pre-processing for faster decision making 8

AI generated route data pre-processing for faster decision m...

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8th International Young Engineers Forum on Electrical and computer Engineering, YEF-ECE 2024

作者： Maas, Rene Shevtshenko, Eduard Karaulova, Tatjana TTK University of Applied Sciences Institute of Logistics Faculty of Science and Technology University of Tartu Institute of Computer Science Tallinn Estonia Tallinn University of Technology TTK University of Applied Sciences Department of Mechanical and Industrial Engineering Institute of Logistics Tallinn Estonia

ISBN: (纸本)9798350387643

The industry is constantly generating new data, allowing take advantage of their use to increase manufacturing efficiency through the reutilisation of operational *** such possibility is to use Artificial Intelligence (AI). While AI is heralded as a solution for industry efficiency and accuracy quandaries through process automation, its implementation grapples with the issue of inaccurate production data used for training, giving an erroneous *** article introduces a framework to facilitate the integration of AI tools within companies. The focus is on the accuracy of predicting normative times and selecting alternative routes when accuracy is low. This approach improves the price quotation prediction process, where using AI is practical due to time constraints. Making quick and accurate decisions is crucial for maintaining efficiency in *** elucidate the functionality, the authors have performed a case study in production for the framework's validation. The wood production sector was selected due to its lower operational complexity than machinery construction, resulting in time savings in the planning *** study revealed that the prediction accuracy of AI was 70% on average for operations normative times. To improve this accuracy, the t-Test method to classify operations was used. This approach eliminates operations with low AI prediction accuracy when making price quotations. Through the case study, AI module accuracy reached 80%, while the additional data collection volume decreased by one-Third. © 2024 IEEE.

关键词： Machine Learning Normative Times Wood Industry

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KTI-RNN:Recognition of Heart Failure from Clinical Notes

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Tsinghua science and technology 2023年第1期28卷 117-130页

作者： Dengao Li Huiting Ma Wenjing Li Baofeng Zhao Jumin Zhao Yi Liu Jian Fu College of Data Science Taiyuan University of TechnologyJinzhong 030600China Department of Statistics and Applied Probability University of CaliforniaSanta BarbaraCA 93106USA College of Mining Engineering Taiyuan University of TechnologyTaiyuan 030024China College of Information and Computer Taiyuan University of TechnologyJinzhong 030600China

Although deep learning methods have recently attracted considerable attention in the medical field,analyzing large-scale electronic health record data is still a difficult *** particular,the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment *** study uses data from the Medical Information Mart for Intensive Care *** with structured data,unstructured data contain abundant patient ***,this type of data has unsatisfactory characteristics,e.g.,many colloquial vocabularies and sparse *** solve these problems,we propose the KTI-RNN model for unstructured data *** proposed model overcomes sparse content and obtains good classification *** term frequency-inverse word frequency(TF-IWF)model is used to extract the keyword *** latent dirichlet allocation(LDA)model is adopted to extract the topic word *** models enable the expansion of the medical record text ***,we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network(BiRNN)model and the output *** call it gated-attention-BiRNN(GA-BiRNN)and use it to identify heart failure from extensive medical *** show that the F 1 score of the proposed KTI-RNN model is 85.57%,and the accuracy rate of the proposed KTI-RNN model is 85.59%.

关键词： heart failure diagnosis text classification deep learning

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Artificial Intelligence in Health Care: Various Applications 47

Artificial Intelligence in Health Care: Various Applications

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47th ICT and Electronics Convention, MIPRO 2024

作者： Protrka, Nikola Abazi, Blerton University Of Applied Sciences In Criminal Investigation And Public Security Zagreb Croatia University For Business And Technology Faculty Of Information Systems And Computer Science Prishtina Serbia

ISBN: (纸本)9798350382495

Artificial intelligence (AI) has become a rapidly growing field with the potential to revolutionize many industries, including healthcare. Integrating AI into healthcare has the potential to transform patient care and disease management, leading to improved outcomes and reduced costs. Our research explores the various applications of AI in healthcare, including predictive analytics, medical image analysis, drug discovery, and clinical decision making, caregiver or virtual assistant via AI chatbot. It also explores the challenges and limitations associated with the use of AI in healthcare, such as ethical concerns, data privacy issues, and algorithmic bias. Finally, this research paper concludes by highlighting the potential of AI to improve patient outcomes, increase efficiency in healthcare delivery and ultimately transform the future of healthcare. © 2024 IEEE.

关键词： Decision making

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