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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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A two-stage algorithm for engagement detection in online learning

A two-stage algorithm for engagement detection in online lea...

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2019 International Conference on Sustainable Technologies for Industry 4.0, STI 2019

作者： Dash, Saswat Akber Dewan, M. Ali Murshed, Mahbub Lin, Fuhua Abdullah-Al-Wadud, M. Das, Animesh National Institute of Technology Department of Computer Science and Engineering Rourkela India School of Computing and Information Systems Athabasca University Edmonton Canada Software Development Bow Valley College Calgary Canada King Saud University Department of Software Engineering Riyadh Saudi Arabia

ISBN: (纸本)9781728160979

Online learning plays a key role in current education system. Engagement detection in online learning is crucial as the student's success in online courses heavily depends on his/her state of mind. In our previous work, we used facial expressions labeled as engaged and not-engaged for student's engagement detection. In this paper, we use student's behavioral (on-task and off-task) and emotional (satisfied, bored, and confused) information for engagement detection. Five different convolutional neural network models have been tested for the behavioral and the emotional dimensions detection to detect student's engagement in online learning. The models are All Convolutional Network, Network in Network, Very Deep Convolutional Network, Conv-Pool Convolutional Network, and a proposed model combing some special features from the above models. We used the dataset Dataset for the Affective States in E-Environments - for the performance evaluation. Experimental results show that the behavioral and emotional dimensions based engagement detection provides a high accuracy. © 2019 IEEE.

关键词： Neural network models

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A scalable deep attention mechanism of instance segmentation for the investigation of chromosome

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SLAS Technology 2025年 33卷 100306页

作者： Umbreen, Neelam Ali, Sara Sajid, Hasan Ayaz, Yasar Alsenan, Shrooq Nam, Yunyoung Kim, So Yeon Sial, Muhammad Baber Islamabad Pakistan Suzhou Industrial Park Monash Research Institute of Science and Technology Suzhou215000 China Laboratory Islamabad Pakistan Islamabad Pakistan Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Department of Artificial Intelligence Ajou University Suwon Korea Republic of Department of Software and Computer Engineering Ajou University Suwon Korea Republic of

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi

关键词： Molecular imaging

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Managing staff motivation in the digital transformation era

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AIP Conference Proceedings 2024年第1期3102卷

作者： V. V. Kuleshov T. G. Aygumov A. L. Zolkin A. N. Grigorev Department of Industrial Ecology and Safety FSAEI HE Omsk State Technical University 11 Mira avenue Omsk 644050 Russia Department of computer engineering software and automated systems Dagestan State Technical University Makhachkala Russia Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara 443010 Russia St. Petersburg State University St. Petersburg 199034 Russia

The digital transformation is a rapidly evolving trend that has significant impacts on businesses and employees. Companies need to embrace new technologies to remain competitive in this changing landscape, but the transition to digital processes is not always easy and may require changes in employees’ skills and mindsets. This article explores the role of motivation in the digital transformation process, with a focus on managing employee motivation. The research was conducted using a mixed-methods approach, including both qualitative and quantitative methods. The qualitative part of the research involved in-depth interviews with experts in human resources and digital transformation, while the quantitative part was an online survey of employees from different industries undergoing digital transformation. The findings from the research were validated through a peer review process. The literature review provides a comprehensive overview of the existing literature on employee motivation in the digital era and highlights the challenges faced by companies in maintaining a motivated and engaged workforce. The review also highlights innovative approaches, such as gamification, digital incentives, and technology, that have the potential to improve employee motivation and engagement.

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Behavioral-based real-time cheating detection in academic exams using deep learning techniques

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AIP Conference Proceedings 2023年第1期2909卷

作者： Zouheir Trabelsi Medha Mohan Ambali Parambil Fady Alnajjar Luqman Ali Department of Information Systems and Security College of Information Technology United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

Recently, with the development of Artificial Intelligence (AI), the use of automated evaluation in education has increased. Maintaining academic integrity is one of the most challenging aspects of higher education. Cheating is rampant in academic examinations and other forms of educational assessment. The vast majority of students believe that it is unethical to tolerate cheating; therefore, it is vital to devote a significant amount of effort to identifying and avoiding instances of cheating. Examining the student’s behavior is one way to determine whether they are engaged in cheating or not. This paper proposes a deep learning-based cheating detection system that can identify instances of students engaging in dishonest behavior. A YOLOv7 model is trained on a custom dataset collected from various resources. The dataset comprises two classes, i.e., cheating and not cheating, and 2565 images. Evaluation criteria like precision, F1 score, recall, and mAP (mean average precision) are used to validate the performance of the proposed model. The proposed model shows promising performance in categorizing the student’s visible actions into cheating or not cheating and achieved an overall mAP@0.5 of 0.719. Overall, the proposed method can be utilized to reduce the error rate associated with human monitoring by alerting the proper authorities whenever unusual behavior is observed during academic tests.

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Smart City Cybersecurity Projects Financing Model in Case of Description of Investors' Resources with Fuzzy Sets

Smart City Cybersecurity Projects Financing Model in Case of...

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2019 IEEE International Conference on Advanced Trends in Information Theory, ATIT 2019

作者： Lakhno, Valeriy Matus, Yurii Malyukov, Volodymyr Desyatko, Alyona Hnatchenko, Tetyana National University of Life and Environmental Sciences of Ukraine Department of Computer Systems and Networks Kyiv Ukraine National University of Trade and Economics Department of Software Engineering and Cyber Security Kyiv Ukraine

ISBN: (纸本)9781728161440

The paper describes the model of choosing mutual investment strategies for Smart City cybersecurity projects with incomplete information on the financial resources of the second investor. The case when the financial resources of the parties belong to fuzzy sets is considered. The model is designed for the developing decision support system in the tasks of choosing rational options for investing in Smart City cybersecurity projects. The solution was obtained through the use of tools of the theory of multi-step games with several terminal surfaces. Distinctive feature of the model is the assumption that the first investor does not have complete information about both the financial strategies of the second investor and of his financial resources state. Verifying the model was performed using computational experiment in MatLab. © 2019 IEEE.

关键词： Game theory

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Accessing Provenance Records in Semantic Web Services

Accessing Provenance Records in Semantic Web Services

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International Conference on Semantic Computing

作者： Omar Ochoa Miralda Rodney Nicholas Del Rio Department of Electrical Computer Software & Systems Engineering Embry-Riddle Aeronautical University Daytona Beach FL USA Air Force Research Laboratory Rome NY USA

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

Provenance on the Semantic Web provides information about the creation, publication and access of resources. Provenance and provenance metadata records from a resource are used to determine trust, to integrate diverse information about the resource and to determine how to credit originators when reusing information. Web services make resources available over the internet by using standardized communication services to automatically exchange data between applications and various systems. The World Wide Web consortium (W3C) proposed the Provenance Access and Query (PROV-AQ) that recommends how to represent and exchange provenance information about resources though a standardized communication protocol. Integrating semantics into web-based service descriptions enables agents to more quickly assemble, discover, and reuse arbitrary web services and the information retained within. The addition of provenance information as part of a Semantic Web service would further increase the reuse and discovery of information and trust on the Web when determining which services to employ in each application. In this paper, the provenance of a resource is tracked using a semantic version control tool with a query interface. The underlying data is made available through a web service using provenance access and query to associate the service to its provenance records. Furthermore, the implications of integrating provenance into a Semantic Web service are discussed. Findings from the literature review and developing a Semantic Web service that exposes provenance records, indicate few differences with a Semantic Web service without attaching provenance records.

关键词： Web services Semantic Web Semantics Ontologies Protocols Standards Tools

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Evaluation of deep segmentation models for the extraction of retinal lesions from multi-modal retinal images

arXiv

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arXiv 2020年

作者： Hassan, Taimur Akram, Muhammad Usman Werghi, Naoufel Center for Cyber-Physical Systems Khalifa University Abu Dhabi United Arab Emirates Department of Computer and Software Engineering National University of Sciences and Technology Islamabad Pakistan

Identification of lesions plays a vital role in the accurate classification of retinal diseases and in helping clinicians analyzing the disease severity. In this paper, we present a detailed evaluation of RAGNet, PSPNet, SegNet, UNet, FCN-8 and FCN-32 for the extraction of retinal lesions such as intra-retinal fluid, sub-retinal fluid, hard exudates, drusen, and other chorioretinal anomalies from retinal fundus and OCT scans. We also discuss the transferability of these models for extracting retinal lesions by varying training-testing dataset pairs. A total of 363 fundus and 173,915 OCT scans were considered in this evaluation from seven publicly available datasets from which 297 fundus and 59,593 OCT scans were used for testing purposes. Overall, the best performance is achieved by RAGNet with a mean dice coefficient (DC) score of 0.822 for extracting retinal lesions. The second-best performance is achieved by PSPNet (mean DC: 0.785) using ResNet50 as a backbone. Moreover, the best performance for extracting drusen is achieved by UNet (DC: 0.864). The source code is available at: http://***/***/downloads/. Copyright © 2020, The Authors. All rights reserved.

关键词： Ophthalmology

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E-learning Model for Students' Satisfaction in Higher Education Universities: Review Paper

E-learning Model for Students' Satisfaction in Higher Educat...

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2019 International Conference on Fourth Industrial Revolution, ICFIR 2019

作者： Tawafak, Ragad M Romli, Awanis B.T. Arshah, Ruzaini Bin Abdullah Faculty of Computer Systems and Software Engineering University Malaysia Pahang Kuantan Pahang26300 Malaysia Faculty of Information Technology Department AlBuraimi University College Buraimi Oman

ISBN: (纸本)9781728114781

The number of internet users with E-Learning models offered in high educational institutions has increased rapidly with the beginning of this century. However, with the increment in number of higher education in the universities, the learner needs more flexibility to attend lesions and easy participate in communication. Therefore, E-Learning is one of the ways provide an accessibility and flexibility to more learners to overcome the far distance of locations or different cultures or timing management for achieving student satisfaction. This paper aims to analyze the current studies on benefits of E-Learning of student satisfaction on the technologies applied and type of blended learning used in learning process to get better qualified learners and to improve the student learning outcomes. The method based on showing different types of E-Learning strategies and the way that allowed learners and faculties to communicate using the common communication models. The finding of this paper shows analytical studies results and their positive evidence with student satisfaction on E-Learning models. © 2019 IEEE.

关键词： E-learning

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Protected Bidding Against Compromised Information Injection in IoT-Based Smart Grid 2nd

Protected Bidding Against Compromised Information Injection ...

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2nd EAI International Conference on Smart Grid and Internet of Things, SGIoT 2018

作者： Bhuiyan, Md Zakirul Alam Zaman, Mdaliuz Wang, Guojun Wang, Tian Rahman, Md. Arafat Tao, Hai Department of Computer and Information Science Fordham University New York CityNY United States School of Computer Science and Educational Software Guangzhou University Guangzhou China Department of Computer Science and Technology Huaqiao University Xiamen China Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Pekan Malaysia Department of Computer Science Baoji University of Arts and Sciences BaojiShaanxi China

ISBN: (纸本)9783030059279

The smart grid is regarded as one of the important application field of the Internet of Things (IoT) composed of embedded sensors, which sense and control the behavior of the energy world. IoT is attractive for features of grid catastrophe prevention and decrease of grid transmission line and reliable load fluctuation control. Automated Demand Response (ADR) in smart grids maintain demand-supply stability and in regulating customer side electric energy charges. An important goal of IoT-based demand-response using IoT is to enable a type of DR approach called automatic demand bidding (ADR-DB). However, compromised information board can be injected into during the DR process that influences the data privacy and security in the ADR-DB bidding process, while protecting privacy oriented consumer data is in the bidding process is must. In this work, we present a bidding approach that is secure and private for incentive-based ADR system. We use cryptography method instead of using any trusted third-party for the security and privacy. We show that proposed ADR bidding are computationally practical through simulations performed in three simulation environments. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Internet of things

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