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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Hireche, Abdelhadi Damseh, Rafat Sirpal, Parikshat Belkacem, Abdelkader Nasreddine United Arab Emirates University College of Information Technology Department of Computer and Network Engineering United Arab Emirates United Arab Emirates University College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates University of Oklahoma School of Electrical and Computer Engineering Gallogly College of Engineering United States

ISBN: (纸本)9798350382396

Epilepsy is a prevalent neurological disorder and has been studied through the analysis of Electroencephalogram (EEG) signals. However, the identification and classification of epileptic seizure patterns remains challenging due to the non-stationary nature of EEG signals and the presence of artifacts. In this paper, we investigate the applicability of a transformer-based deep learning model to classify seizure patterns observed in epileptic patients. We employed the self-Attention mechanism inherent in transformers to capture complex temporal relationships in the EEG recordings. By prepossessing the EEG signals into suitable input sequences and adapting the transformer architecture, we achieved 78.11% in distinguishing between different epileptic seizure patterns. Our findings indicate that the transformer model, with its ability to manage long-range dependencies, offers a robust approach to EEG-based seizure pattern classification. This work is important for building advanced automated diagnostic tools for epilepsy and related neurological disorders. © 2023 IEEE.

关键词： Electroencephalography

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Hybrid Convolutional Neural Networks for Enhanced Detection of Mango Leaf Diseases 6

Hybrid Convolutional Neural Networks for Enhanced Detection ...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Porna, Shweta Bhattacharjee Kabir, Md. Firoz Rana, Md. Imran Chowdhury Sajol, Md. Saiful Islam Roy, Tonmoy Khan, Mohammad Aman Ullah Adnan, Md. Al Bhavani, Girigula Durga Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Master's in Information Technology University of the Cumberlands United States Bachelor of Business Administration International American university Los Angeles CA United States Louisiana State University Department of Computer Science and Engineering LA United States Data Analytics & Information Systems Utah State University Logan United States Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka1212 Bangladesh Noakhali Science And Technology University Bsc in software engineering Bangladesh Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9798331505790

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model's performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of 100 % on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture. © 2024 IEEE.

关键词： Fruits

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An Analysis of Performance Bottlenecks in MRI Pre-Processing

arXiv

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arXiv 2024年

作者： Dugré, Mathieu Chatelain, Yohan Glatard, Tristan Department of Computer Science and Software Engineering Concordia University MontrealQC Canada

Magnetic Resonance Image (MRI) pre-processing is a critical step for neuroimaging analysis. However, the computational cost of MRI pre-processing pipelines is a major bottleneck for large cohort studies and some clinical applications. While High-Performance Computing (HPC) and, more recently, Deep Learning have been adopted to accelerate the computations, these techniques require costly hardware and are not accessible to all researchers. Therefore, it is important to understand the performance bottlenecks of MRI pre-processing pipelines to improve their performance. Using Intel VTune profiler, we characterized the bottlenecks of several commonly used MRI-preprocessing pipelines from the ANTs, FSL, and FreeSurfer toolboxes. We found that few functions contributed to most of the CPU time, and that linear interpolation was the largest contributor. Data access was also a substantial bottleneck. We identified a bug in the ITK library that impacts the performance of ANTs pipeline in single-precision and a potential issue with the OpenMP scaling in FreeSurfer recon-all. Our results provide a reference for future efforts to optimize MRI pre-processing pipelines. © 2024, CC BY.

关键词： Pipelines

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A Nature-Inspired AI Framework for Accurate Glaucoma Diagnosis

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computer Modeling in engineering & sciences 2025年第4期143卷 539-567页

作者： Jahanzaib Latif Ahsan Wajahat Alishba Tahir Anas Bilal Mohammed Zakariah Abeer Alnuaim School of Software Northwestern Polytechnical UniversityXi’an710072China Shifa College of Medicine Shifa Tameer-e-millat UniversityIslamabad44000Pakistan College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Computer Science and Engineering College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11495Saudi Arabia

Glaucoma,a leading cause of blindness,demands early detection for effective *** AI-based diagnostic systems are gaining traction,their performance is often limited by challenges such as varying image backgrounds,pixel intensity inconsistencies,and object size *** address these limitations,we introduce an innovative,nature-inspired machine learning framework combining feature excitation-based dense segmentation networks(FEDS-Net)and an enhanced gray wolf optimization-supported support vectormachine(IGWO-SVM).This dual-stage approach begins with FEDS-Net,which utilizes a fuzzy integral(FI)technique to accurately segment the optic cup(OC)and optic disk(OD)from retinal images,even in the presence of uncertainty and *** the second stage,the IGWO-SVM model optimizes the SVM classification process,leveraging a gray wolf-inspired optimization strategy to fine-tune the kernel function for superior *** testing on three benchmark glaucoma image databases DRIONS-DB,Drishti-GS,and Rim-One-r3 demonstrates the efficacy of our method,achieving classification accuracies of 97.65%,94.88%,and 93.2%,*** results surpass existing state-of-the-art techniques,offering a promising solution for reliable and early glaucoma detection.

关键词： Glaucoma fundus images machine learning nature-inspired algorithm segmentation classification

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Evaluating the Robustness of LiDAR-based 3D Obstacles Detection and Its Impacts on Autonomous Driving Systems

arXiv

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arXiv 2024年

作者： Pham, Tri Minh Triet Yang, Bo Yang, Jinqiu Department of Computer Science and Software Engineering Concordia University MontrealQC Canada

Autonomous driving systems (ADSs) require realtime input from multiple sensors to make time-sensitive decisions using deep neural networks. This makes the correctness of these decisions crucial to ADSs' adoption as errors can cause significant loss. Sensors such as LiDAR are sensitive to environmental changes and built-in inaccuracies and may fluctuate between frames. While there has been extensive work to test ADSs, it remains unclear whether current ADSs are robust against very subtle changes in LiDAR point cloud data. In this work, we study the impact of the built-in inaccuracies in LiDAR sensors on LiDAR-3D obstacle detection models to provide insight into how they can impact obstacle detection (i.e., robustness) and by extension trajectory prediction (i.e., how the robustness of obstacle detection would impact ADSs). We propose a framework SORBET, that applies subtle perturbations to LiDAR data, evaluates the robustness of LiDAR-3D obstacle detection, and assesses the impacts on the trajectory prediction module and ADSs. We applied SORBET to evaluate the robustness of five classic LiDAR-3D obstacle detection models, including one from an industry-grade Level 4 ADS (Baidu's Apollo). Furthermore, we studied how changes in the obstacle detection results would negatively impact trajectory prediction in a cascading fashion. Our evaluation highlights the importance of testing the robustness of LiDAR-3D obstacle detection models against subtle perturbations. We find that even very subtle changes in point cloud data (i.e., removing two points) may introduce a non-trivial decrease in the detection performance. Furthermore, such a negative impact will further propagate to other modules, and endanger the safety of ADSs. Copyright © 2024, The Authors. All rights reserved.

关键词： software testing

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Design approach centered on the activity or the learning situations: For a learner-centered learning environment (LCLE) 14

Design approach centered on the activity or the learning sit...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Wague, Aminata Bousso, Mamadou Capus, Laurence Iba der Thiam University of Thies Sustainable Development and Society Doctoral School Thies Senegal Iba der Thiam University of Thies Department of Computer Science Thies Senegal Laval University Department of Computer Science and Software Engineering QC Canada

ISBN: (纸本)9798350399431

This article deals with the design of TELE (Technology-Enhanced Learning Environment), specifically LCLE (Learner-Centered Learning Environment). It suggests adopting the activity-centered approach in favour of the non-formal learning situation to design an LCLE. After demonstrating that this approach only considers formal learning situations, we describe the different stages of its implementation process. Subsequently, we develop a comparative table of the activity paradigm's formal and non-formal learning situations, allowing us to validate the proposition in question. © 2023 IEEE.

关键词： computer aided instruction

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Adaptive Control and Simulation of Production Processes Using Temporal Neuro-Fuzzy Petri Net 7

Adaptive Control and Simulation of Production Processes Usin...

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7th International Conference on Information Technologies in engineering Education, Inforino 2024

作者： Bobryakov, A.V. Borisov, V.V. Misnik, A.E. Prokopenko, S.A. National Research University 'MPEI' Department of Computer Engineering Smolensk Russia National Research University 'MPEI' Department of Management and Computer Science Moscow Russia Inter-State Educational Institution of Higher Education 'Belarusian-Russian University' Information Techmology Software Mogilev Belarus

ISBN: (纸本)9798350371369

Complex systems surround people in many industries. In the process of functioning, complex systems need to overcome various difficulties associated with the influence of external factors, peculiarities of the functioning of processes, etc. Complex system is influenced by a complex and rapidly changing set of external conditions, where effective planning and optimization of resources become key success factors, while the possible trajectories of the production of complex technical objects, the employment plan of equipment and personnel, which helps to optimize processes and reduce production costs. The article examines one of the classes of complex systems, and also proposes a method for adaptive control of systems of this class using temporal neuro-fuzzy Petri nets. These networks combine the concepts of Petri nets, a graphical system modeling and analysis tool, with neural networks and fuzzy logic. Considered algorithmic and software for the implementation and support of processes. The proposed method makes it possible to increase the efficiency of a complex system under changing operating conditions. caused by the influence of various factors. © 2024 IEEE.

关键词： Complex networks

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Think Before You Serum” - An Experience Report on Daily Scrums to Aid Reflection

Think Before You Serum” - An Experience Report on Daily Scr...

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Conference on software engineering Education & Training (CSEE&T)

作者： Matthew Minish Matthias Galster Fabian Gilson Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (数字)9798350378979

ISBN: (纸本)9798350378986

Project-based software engineering courses often incorporate industry practices, e.g., the “Daily Serum” (i.e., daily stand-ups). While the main purpose of the Daily Serum is to discuss progress and blockers, we can also utilize them as an educational tool to facilitate student engagement and reflection since reflection is a crucial aspect of experience-based learning. This paper seeks to enhance reflective practices in software engineering project courses through a pre-meeting check-in add-on to the Daily Serum. We also aim to assess if this increases self-reflection and consequently impacts student engagement and performance. To this end, we adapted the Daily Serum in a software engineering project course with a pre-meeting semi-guided self-reflection on a student's work. This was supported by a bespoke online tool. Effects of the reflection were analyzed from the student perspective using surveys, application usage data, formal assessment data, and observations from teaching staff. From our experiences, we observed the pre-meeting check-in was beneficial in enhancing student self-reflectivity. Quan-titative data, supported by qualitative evidence from student feedback, also suggested positive effects on student engagement and performance. Students who used pre-meeting check-ins reported increased self-reflection and showed greater levels of improvement in assessed performance. Based on our experiences we recommend incorporating regular individual self-reflection sessions in software engineering project courses.

关键词： Surveys Industries Education Project management Reflection Scrum (software development) software engineering

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The role of library versions in Developer-ChatGPT conversations

arXiv

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arXiv 2024年

作者： Raj, Rachna Costa, Diego Elias Department of Computer Science and Software Engineering Concordia University MontrealQC Canada

The latest breakthroughs in large language models (LLM) have empowered software development tools, such as ChatGPT, to aid developers in complex tasks. Developers use ChatGPT to write code, review code changes, and even debug their programs. In these interactions, ChatGPT often recommends code snippets that depend on external libraries. However, code from libraries changes over time, invalidating a once-correct code snippet and making it difficult to reuse recommended code. In this study, we analyze DevGPT, a dataset of more than 4,000 Developer-ChatGPT interactions, to understand the role of library versions in code-related conversations. We quantify how often library version constraints are mentioned in code-related conversations and when ChatGPT recommends the installation of specific libraries. Our findings show that, albeit to constantly recommend and analyze code with external dependencies, library version constraints only appear in 9% of the conversations. In the majority of conversations, the version constraints are prompted by users (as opposed to being specified by ChatGPT) as a method for receiving better quality responses. Moreover, we study how library version constraints are used in the conversation through qualitative methods, identifying several potential problems that warrant further research. Copyright © 2024, The Authors. All rights reserved.

关键词： Libraries

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ScrumBoard: A Project Management Tool Purpose-Built for software engineering Education

ScrumBoard: A Project Management Tool Purpose-Built for Soft...

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Conference on software engineering Education & Training (CSEE&T)

作者： Matthew Minish Fabian Gilson Matthias Galster Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (数字)9798350378979

ISBN: (纸本)9798350378986

This paper recounts our experiences with the ‘ScrumBoard’, a custom-built open-source digital project man-agement tool specifically designed for software engineering project courses. We discuss how it has supported experiential learning pedagogy and assessment over multiple iterations of a two-semester, 3 rd year project course, having now been used extensively by more than 200 students. While offering a similar experience to commercial software project management tools, we reflect on how the ScrumBoard has supported our ability to assess students more accurately and efficiently, and how the tool has enabled us to identify problematic work practices in real-time, allowing for earlier interventions from teaching staff and an improved learning experience for students.

关键词： Surveys Measurement Education Project management software Reflection Real-time systems

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