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7th International Conference on Big Data Analytics, ICBDA 2022

作者： Mao, Yanghong Chen, Chuixin Jiang, Huiyong Guangzhou Institute of Science and Technology Department of Computer Science and Engineering Guangzhou China Guangzhou Institute of Science and Technology Department of Intelligent Manufacturing and Electrical Engineering Guangzhou China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou China

ISBN: (纸本)9781665479387

In order to achieve the goal that the manipulator can automatically obtain any position within its working range, and can adjust the end actuator for grasping, a machine vision sorting system is designed. The system passes through image processing, target recognition and positioning, and finally realizes the sorting effect through end control. The collected image information is processed by MATLAB. The image preprocessing adopts median filtering, image enhancement, binarization and edge detection. For the detection of workpiece surface defects, the improved segmentation network method of convolution neural network is adopted. In the algorithm of identifying workpiece type, the template matching method of log polar coordinate and Fourier transform is adopted. In the algorithm of workpiece position, the centroid of the workpiece is extracted as the positioning reference coordinate, combined with the three degree manipulator, the kinematics model of the manipulator is established. In the algorithm of workpiece grasping, the joint angle of each manipulator is obtained by inverse operation according to the position coordinates of the workpiece, and sent to STM32 through the serial port. Then the STM32 completes the control command to grasp and place the workpiece and complete the sorting task. The experimental results show that the system meets the design requirements. © 2022 IEEE.

关键词： Template matching

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KDTL: knowledge-distilled transfer learning framework for diagnosing mental disorders using EEG spectrograms

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Neural Computing and Applications 2024年第30期36卷 18919-18934页

作者： Singh, Shreyash Jadli, Harshit Padma Priya, R. Surya Prasath, V.B. Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH45257 United States Department of Biomedical Informatics College of Medicine University of Cincinnati CincinnatiOH45267 United States Department of Computer Science University of Cincinnati CincinnatiOH45221 United States

Electroencephalography (EEG) is a well-known modality in neuroscience and is widely used in identifying and classifying neurological disorders. This paper investigates how EEG data can be used along with knowledge distillation-based deep learning models to detect mental disorders like epilepsy and sleep disorders. The EEG signals are converted into time–frequency plots using short-time Fourier transforms. Further, we propose a novel methodology for using knowledge distillation-based transfer learning (KDTL). Knowledge distillation is becoming quite prevalent in the machine learning field and is associated with various applications in our work;we propose its use in the detection of mental disorders from EEG spectrograms. We convert the EEGs using short-term Fourier transform to obtain time–frequency representation and apply teacher-student by first training a large teacher model and use knowledge distillation to train a student model. In our experiments, we found that ConvNext teacher and MobileNet student combination obtained better results. Our proposed KDTL approach is tested on two datasets with multiple cases, namely the Bonn and ISRUC datasets and obtain 98% and 95% accuracies, respectively. Further experimental results show that the overall KDTL methodology can obtain high classification accuracy across both datasets in binary and multiclass classifications and proves to be better than multiple prior works. Further, our KDTL approach provides a way to train lightweight models which have a smaller number of trainable parameters and thereby constitute lower training time overall. Our proposed KDTL-based approach obtained accurate results in diagnosing mental disorders from EEG spectrograms. Compared to other related methods, KDTL outperformed across tasks with obtained good results in both binary and multiclass classifications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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Towards agile collaborative multi-view modeling with inconsistency tolerance

Towards agile collaborative multi-view modeling with inconsi...

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2024 Symposium on software Technologies: Applications and Foundations, STAF 2024 Workshops: 2nd International Workshop on Agile Model-Driven engineering, AgileMDE 2024, 1st Large Language Models for Model-Driven engineering Workshop, AgileMDE 2024 and 4th International Workshop on Model-Driven engineering for Smart Internet-of-Things Systems, MeSS 2024

作者： Shahvari, Yaser Sharbaf, Mohammadreza Rahimi, Shekoufeh Kolahdouz Tehrani, Sobhan Yassipour MDSE Research Group Department of Software Engineering University of Isfahan Isfahan Iran School of Arts University of Roehampton London United Kingdom Computer Science Department Faculty of Engineering University College London London United Kingdom

Model-driven engineering (MDE) copes with the complexity of software development by using the principles of separation of concerns and automatic transformation. In MDE, stakeholders from diverse domains collaborate concurrently on different models to quickly analyze, design, and generate complex software-intensive systems. To adopt an agile development approach in MDE, an inconsistency tolerance framework is needed. This framework postpones the resolution phase for incompatibilities caused by online cooperation, allowing temporary incompatibilities to be automatically fixed while requiring resolution only for the remaining conflicts at the appropriate time. In this paper, we propose a general framework for identifying and diagnosing inconsistencies, making decisions regarding inconsistency tolerance or intervention for resolution, and outlining tolerance strategies in agile systems modeled as multi-views. Our framework comprises three primary phases: detection, analysis, and tolerance. It is designed to identify inconsistencies in multi-view models, which are inherently more complex than single-view models. The tolerance phase employs strategies to tolerate inconsistencies, enhancing the flexibility of the agile development approach for collaborative multi-view modeling. © 2024 Copyright for this paper by its authors.

关键词： software design

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Intelligent Systems for Early Malaria Disease Detection in Patient Cells Using Transfer Learning Approaches 4

Intelligent Systems for Early Malaria Disease Detection in P...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Zaman Khan, Gul Ali Shah, Ibrar Farhatullah Hassan, Muhammad Abul Junaid, Hazrat Sardar, Fouzia Uet Department of Computer Software Engineering Mardan Pakistan School of Automation China University of Geosciences Wuhan430074 China University of Trento Department of Information Engineering and Computer Science Italy University of Malakand Department of Computer Science and Information Technology Pakistan University F Malakand Department of Zoology Dir Lower Pakistan

ISBN: (纸本)9798350335316

Malaria is an infectious disease spread by mosquitoes that effect humans and other animals. It is a massive threat to humanity, with instances growing each year. It is essential to prevent and diagnose malaria immediately and efficiently. For the time being, conventional methods are used for diagnosing malaria in which the patient's blood sample is examined by microscope or by using malaria RTD kits. This approach has several limitations because it requires medical expertise, is expensive, takes a long time, and the results are unsatisfactory. Artificial intelligence-based systems can prevent and help in diagnose of this infectious disease. Because of these limitations, the proposed work has proposed an AI-based diagnosis system that can detect malaria parasites immediately and efficiently. In the proposed experiment, we have applied four different pre-trained deep learning models on the image dataset with some preprocessing and optimization techniques for malaria parasite detection. After investigations, evaluation matrices such as precision, Recall, F1-score, sensitivity, and specificity are used to measure the performance of the proposed models. The Inception-Resnet outperformed by achieving 95% accuracy, VGG16 achieved 92% accuracy, inception achieved 93% accuracy, and VGG19 achieved 91% accuracy. The positive outcomes of this study show that this approach performs much better than the approaches currently used. Furthermore, the proposed method is relevant to health experts for screening purposes. © 2023 IEEE.

关键词： Diseases

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Arabic Offense Text Detection in Social Networks using Collaborative Machine Learning 6

Arabic Offense Text Detection in Social Networks using Colla...

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6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2024

作者： Alabdulrahman, Maria Alotaibi, Lara Latif, Ghazanfar Alghazo, Jaafar Prince Mohammad Bin Fahd University Software Engineering Department Khobar Saudi Arabia Prince Mohammad bin Fahd University Department of Computer Science Al-Khobar Saudi Arabia University of Minnesota Software Engineering and Information Technology Management Departments CrookstonMN56716 United States

ISBN: (纸本)9798331529987

In an era of abundant social media use and digital communication, the classification of Arabic social media content and user comments into relevant categories poses a significant challenge. The content in social network platforms often encompasses a wide range of topics, sentiments, and societal concerns, making it essential to develop effective methods for discerning and categorizing these expressions. In this research, a new dataset for Arabic offensive text is built along with the methodology proposed for classifying Arabic content into five categories collected from different social networking sites. Leveraging a diverse array of text representation techniques, including TF-IDF, Bag of Words (BoW), custom embeddings, and pre-trained embeddings from models such as Arabert and Mbert, a comprehensive evaluation is conducted based, shedding light on the optimal approach for tackling this complex problem. The findings reveal that the TF-IDF and BoW methods stand out with an impressive 89% accuracy, emphasizing their robustness in addressing the nuances of Arabic tweet classification across these sensitive topics. This study offers valuable insights into the challenges inherent in categorizing Arabic social media content, emphasizing the significance of the TF-IDF approach as an effective tool for this task. © 2024 IEEE.

关键词： Adversarial machine learning

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Review of Machine Learning Techniques for Class Imbalance Medical Data Set 2

Review of Machine Learning Techniques for Class Imbalance Me...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Amshi, Ahmad Hauwa Usman, Asmau Prasad, Rajesh Salihu, Ibrahim Anka Dadile, Abubakar Muhammad Federal University Deptment of Computer Science Gashua Nigeria Abdu Gusau Polytechnic Department of Compter Science Talata Mafara Nigeria African University of Science and Technology Department of Computer Science Abuja Nigeria Nile University of Nigeria Department of Software Engineering Abuja Nigeria Umar Sulaiman College of Education Department of Computer Science Gashua Nigeria

ISBN: (纸本)9798350358834

Data imbalance threatens a medical dataset where the dominant class is typically viewed as unfavorable. In contrast, the minority class is t supposed to be the positive one, affecting the performance of the machine learning prediction. This aims to examine how resampling strategies in Machine Learning (ML) have recently been used in medical data sets. Many researchers used the preprocessing stage's data-level approach to resample the imbalanced medical data. Thirty-two sources were reviewed in which data level techniques of balancing the imbalanced data were applied to medical datasets spanning 2015 to 2022, with oversampling methods outperforming the under-sampling methods. © 2023 IEEE.

关键词： Machine learning

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Deep Learning Based Approach for Detecting Student Engagement Through Facial Emotions 2

Deep Learning Based Approach for Detecting Student Engagemen...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Lima, Mariam Ahmed, Kazi Rifat Jahan, Nusrat Mahmud, Imran Parvez, Md. Shahriar Revathi, P. Daffodil International University Department of Software Engineering Dhaka Bangladesh Daffodil International University Department of Information Technology & Management Dhaka Bangladesh Holy Cross College Department of Computer Science Tamilnadu Tiruchirapalli India

ISBN: (纸本)9798350373110

In today's educational system, maintaining students' interest is of the utmost importance. Researchers have been motivated to investigate approaches that can automatically measure engagement as a result of this. Using facial expressions to determine whether or not a student is interested in a certain topic and to give individualized assistance is the purpose of this research project. Using the well-known VGG16 model, This study provide a framework for evaluating degrees of engagement based on facial signals. This framework is built on face detection which used the FER2013 dataset, produced encouraging findings with a considerable accuracy of 96.16%. The objective of this study is to provide educators and online platforms with a dependable instrument that can assess and address student involvement. This will be accomplished by using deep learning algorithms that are particularly intended to do facial expression interpretation. The findings of this study provide a contribution to the growing subject of using technology to enhance learning settings by highlighting the benefits of automated systems in terms of increasing student engagement and academic accomplishment. © 2024 IEEE.

关键词： Contrastive Learning

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Deepfakes Examiner: An End-to-End Deep Learning Model for Deepfakes Videos Detection 16

Deepfakes Examiner: An End-to-End Deep Learning Model for De...

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Ilyas, Hafsa Irtaza, Aun Javed, Ali Malik, Khalid Mahmood University of Engineering and Technology Software Engineering Department Taxila Pakistan University of Engineering and Technology Computer Science Department Taxila Pakistan Oakland University Rochester Computer Science and Engineering Department MI United States

ISBN: (纸本)9781665464772

Deepfakes generation approaches have made it possible even for less technical users to generate fake videos using only the source and target images. Thus, the threats associated with deepfake video generation such as impersonating public figures, defamation, and spreading disinformation on media platforms have increased exponentially. The significant improvement in the deepfakes generation techniques necessitates the development of effective deepfakes detection methods to counter disinformation threats. Existing techniques do not provide reliable deepfakes detection particularly when the videos are generated using different deepfakes generation techniques and contain variations in illumination conditions and diverse ethnicities. Therefore, this paper proposes a novel hybrid deep learning framework, InceptionResNet-BiLSTM, that is robust to different ethnicities and varied illumination conditions, and able to detect deepfake videos generated using different techniques. The proposed InceptionResNet-BiLSTM consists of two components: customized InceptionResNetV2 and Bidirectional Long-Short Term Memory (BiLSTM). In our proposed framework, faces extracted from the videos are fed to our customized InceptionResNetV2 for extracting frame-level learnable features. The sequences of features are then used to train a temporally aware BiLSTM to classify between the real and fake video. We evaluated our proposed approach on the diverse, standard, and largescale FaceForensics++ (FF++) dataset containing videos manipulated using different techniques (i.e., DeepFakes, FaceSwap, Face2Face, FaceShifter, and NeuralTextures) and the FakeA VCeleb dataset. Our method achieved an accuracy greater than 90% on DeepFakes, FaceSwap, and Face2Face subsets. Performance and generalizability evaluation highlights the effectiveness of our method for detecting deepfake videos generated through different techniques on diverse FF++ and FakeA VCeleb datasets. © 2022 IEEE.

关键词： Long short-term memory

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MOALF-UAV-MEC: Adaptive Multiobjective Optimization for UAV-Assisted Mobile Edge Computing in Dynamic IoT Environments

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IEEE Internet of Things Journal 2025年第12期12卷 20736-20756页

作者： Al-Bakhrani, Ali A. Li, Mingchu Obaidat, Mohammad S. Amran, Gehad Abdullah Dalian University of Technology School of Software Dalian116024 China Albaydha University Faculty of Administration and Computers Albydha Yemen Jiangxi Normal University School of Computer and Information Engineering Jiangxi Nanchang330022 China Dalian University of Technology School of Software Technology Dalian116024 China The University of Jordan The King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University School of Computing Department of Computational Intelligence Kattankulathur603203 India Amity University School of Engineering Noida201301 India Dalian University of Technology Department of Management Science and Engineering Dalian116024 China

The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This article presents multiobjective adaptive learning framework for uncrewed aerial vehicle (UAV)-assisted mobile edge computing (MOALF-UAV-MEC), a novel MOALF-UAV-MEC tailored for dynamic IoT environments. The framework integrates multiobjective reinforcement learning (MORL), model predictive control (MPC), adaptive particle swarm optimization (APSO), and Lyapunov Optimization to optimize UAV trajectories, dynamic resource allocation, and system stability. MOALF-UAV-MEC addresses critical challenges in UAV-assisted mobile edge computing (MEC), including multiobjective optimization, adaptive resource allocation, energy efficiency, scalability, and quality of service guarantees. Our approach employs a unique burst mode feature for UAVs, enabling temporary performance boosts in high-demand situations. Extensive simulations demonstrate the framework’s efficiency in enhancing task completion rates, energy efficiency, and long-term system sustainability. Results show a task completion rate of 94.50%, significantly outperforming existing approaches, with an average of 1890 completed tasks per UAV and a load balancing efficiency of 96%. The framework exhibits robust adaptive behavior, achieving a 38% reduction in UAV route optimization and a 55% increase in task completion during high-load periods. This research contributes to the advancement of edge computing in IoT environments, offering a scalable and adaptive solution for deploying computational resources in areas with limited infrastructure, during temporary events, or in emergency situations. © 2014 IEEE.

关键词： Model predictive control

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Reliable Service Recommendation: A Multi-Modal Adversarial Method for Personalized Recommendation Under Uncertain Missing Modalities

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IEEE Transactions on Services Computing 2025年第3期18卷 1724-1738页

作者： Chen, Junyang Yang, Ruohan Guo, Jingcai Wang, Huan Wu, Kaishun Zhang, Liangjie Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Huazhong Agricultural University College of Informatics Wuhan430070 China The Hong Kong Polytechnic University Department of Computing Kowloon Hong Kong The Hong Kong University of Science and Technology DSA Thrust and IoT Thrust Center Guangzhou511455 China

Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-user/item interactions. While existing works on multimedia recommendation have made strides in leveraging multi-modal contents to enrich item representations, many of them overlook the practical scenario of multiple modality missing. As a result, the performance of recommendation systems can be significantly compromised in such cases. In this paper, we introduce a novel multi-modal adversarial method called MMAM © 2008-2012 IEEE.

关键词： Discriminators

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