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7th International Conference on Contemporary information technology and Mathematics, ICCITM 2021

作者： Mohammed, Maha Majed Alheeti, Khattab M. Ali College of Computer and Information Technology University of Anbar Computer Sciences Department Anbar Iraq University of Anbar College of Computer and Information Technology Computer Networking System Department Anbar Iraq

ISBN: (纸本)9781665420327

Emerging technology makes one's life more comfortable;however, in the Internet of Things, there are a lot of weaknesses like infrastructure, connectivity, network, etc, due to the presence of millions of networked devices that make it difficult to implement safety on each device. Security threats are one of the most important issues recently gaining popularity in IoT, attacks that can cause major disruptions and loss of information within the IoT network. Intrusion Detection system (IDS) has a substantial role in protecting and securing an IoT network through detecting and preventing malicious activities. To develop IDS for timely detection and categorization of cyber threats at the network level, classical machine learning techniques are commonly utilized. However, because malicious attacks are continuously evolving and occurring at extremely large sizes, various problems arise, necessitating a scalable solution. In this paper, a convolutional neural network (CNN) approach, which is a kind of deep learning model for IDS discovery, is developed that is flexible and efficient for detecting and classifying cyber-Attacks in IoT networks. The well-Applied CNN model on the UNSW-NB15 dataset obtained 100% precision results. © 2021 IEEE.

关键词： Internet of things

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Student Assessment Factors in Study Success Using the Decision Tree Algorithm 2

Student Assessment Factors in Study Success Using the Decisi...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Murad, Dina Fitria Dwi Wijanarko, Bambang Mayatopani, Hendra Hassan, Rosilah Inn, Arba'Iah Sunardi Bina Nusantara University Binus Online Learning Information Systems Department Jakarta Indonesia Bina Nusantara University School of Computer Science Jakarta Indonesia Pradita University Department of Information System Tangerang Indonesia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Universiti Teknologi Malaysia Pusat Pengajian Diploma Space Kuala Lumpur Malaysia

ISBN: (纸本)9798350360523

The evaluation of students is essential to comprehending and enhancing successful learning in an educational setting. In the era of information technology, the use of decision tree algorithms has become increasingly pertinent in this sphere. This study seeks to identify the student assessment factors that have the greatest impact on learning success when using the Decision Tree algorithm. The collection and analysis of student assessment data, including test scores, class participation, and other variables Modeling the relationship between these factors and student learning outcomes using decision trees. The results of the analysis permit the identification of the most influential factors in predicting the success of student learning. The findings of this study can aid educators, schools, and educational institutions in designing learning strategies that are more effective and tailored to the specific needs of each student, as well as in identifying students who are at high risk and require special attention. The implementation of the Decision Tree algorithm in student evaluation is a crucial step toward a more individualized and effective educational approach. © 2024 IEEE.

关键词： Decision trees

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Crafting Compelling Argumentative Writing for Undergraduates: Exploring the Nexus of Digital Annotations, Conversational Agents, and Collaborative Concept Maps

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Journal of Educational Computing Research 2024年第5期62卷 1327-1357页

作者： Sandra, Randi Proska Hwang, Wu-Yuin Zafirah, Afifah Hariyanti, Uun Engkizar, Engkizar Hadi, Ahmaddul Fauzan, Ahmad Universitas Negeri Padang West Sumatra Indonesia Graduate Institute of Network Learning Technology National Central University Taoyuan Taiwan College of Science and Engineering National Dong Hwa University Hualien Taiwan Department of Information System Faculty of Computer Science Universitas Brawijaya East Java Indonesia

Argumentative writing is a fundamental aspect of undergraduate students’ academic and scientific writing related to critical thinking and problem-solving skills. However, previous studies have shown that students face various difficulties with argumentative writing, such as unclear and illogical ideas, less-structured arguments, and unbalanced interpretation of issues, data, and evidence. This study aims to improve the argumentative writing skills of undergraduate students by integrating computer-supported argumentative writing tools, such as annotation, conversational agents (CAs), and collaborative concept maps, into an online learning management system. Since the study was conducted during the COVID-19 pandemic, these tools can support meaningful learning activities and investigation in argumentative writing. The researchers divided sixty participants into the experimental group (N = 30) and the control group (N = 30). The results showed that the experimental group’s writing achievements outperformed the control group, and the three tools effectively improved the five elements of argumentative writing, including claims, grounds, warrants, backings, and rebuttal. Furthermore, a deep analysis found that the number of annotations, valid CAs’ responses, and argument nodes on collaborative concept maps can significantly predict students’ argumentative writing development. Moreover, students perceived that the incorporated tools could effectively improve their argumentative writing skills. © The Author(s) 2024.

关键词： argumentative writing collaborative concept map conversational agents digital annotation undergraduate students

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Investigating the Advantages of AI-driven Predictive Analysis in Healthcare 3

Investigating the Advantages of AI-driven Predictive Analysi...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Mohammed, Murtuza Ali Mohammed, Vazeer Ali Ganesh, Ashween Kiruthiga, T. Lewis University Dept. of Computer & Information System IL United States Fresenius Medical Care North America Research and Development ConcordCA94520 United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350384598

AI-pushed predictive evaluation can revolutionize healthcare. With the usage of superior predictive models and machine algorithms, AI can discover styles and appropriately expect consequences for clinical treatments and remedies. It may assist clinicians in making more informed decisions and ensuring certain affected persons' safety. In this summary, we assess the benefits of imposing AI-driven predictive analysis in healthcare AI-driven predictive evaluation permits the usage of state-of-the-art fashions that can correctly predict medical outcomes. It allows doctors and healthcare carriers to limit risks and discover ability problems earlier than they grow to be intricate. By way of leveraging available information, such as scientific records, genomic information, and more, AI can accurately generate insights and guidelines for remedy and prognosis. Moreover, AI-pushed predictive evaluation is incredibly capable of figuring out destructive drug reactions and different interactions among drugs, in addition to providing perception into drug interactions among illnesses and other conditions. AI-driven predictive evaluation has numerous packages in healthcare and may be used to pick out capacity mistakes before they occur. AI can analyze a large variety of records and generate reports on the consequences. It will assist healthcare providers in making the proper decisions in relation to imparting top-quality care for sufferers. Moreover, AI can be used to expand customized treatment plans, which are custom-designed to every patient's unique state of affairs. It may be extraordinary. © 2024 IEEE.

关键词： Drug interactions

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Automated User Experience (UX) Testing for Mobile Application: Strengths and Limitations

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第4期16卷 30-45页

作者： Abuaddous, Hayfa. Y. Saleh, Ashraf Mousa Enaizan, Odai Ghabban, Fahad Al-Badareen, Anas Bassam Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Department of Management Information System University of Tabuk Tabuk Saudi Arabia College of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Software Engineering Aqaba University of Technology Aqaba Jordan

User Experience (UX) evaluation has a significant importance for any interactive application. Mobile device applications have additional limitations to convey good user experiences (UX) due to the usage and features of mobile devices in ubiquitous computing contexts. There are a lot of automated tools available for evaluating, such applications particularly developed for Android and iOS. Automated UX evaluation provides a number of strengths which are able to overcome the limitations of non-automated (moderated) counterparts. Automated UX evaluation is regarded as unexplored field, especially when it is adopted for the UX evaluation of complex mobile application. Despite these, automated UX evaluation tools are unexplored and should only be used in coupling with traditional UX evaluation techniques. Hence, this study aims to discuss the literature in automated UX evaluation and to highlight the UX measurements that merit further investigation. This paper presents the most fre-quently used automated UX evaluation tools for mobile applications. Therefore, the study analyzes existing automated UX tools and frameworks and identifies which UX attributes and measurements of automated UX evaluation are likely to be of use in future research. Moreover, we discuss the strengths and limitations of automated UX evaluation methods for mobile applications. Finally, the paper provides an overview of automated UX evaluation techniques and tools. It also presents the strengths and limitations for comparing various evaluation methods. © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Automation

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A Time Deterministic Dynamic Segmentation Scheduling Algorithm 9

A Time Deterministic Dynamic Segmentation Scheduling Algorit...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Gu, Haonan Wu, Jing Guo, Yi Wuhan University of Science and Technology College of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

This paper proposed a soft real-time scheduling algorithm 'time-deterministic dynamic partitioning scheduling' for multimedia tasks in embedded devices. We established a new model based on the characteristics of embedded devices. We developed a pre-scheduling mechanism to select a suitable scheduling strategy based on the actual situation of the task set in the offline cache and to rearrange the task order so as to guarantee the reliability and real-time performance of the system while having higher flexibility and stability. The mechanism also completes the tasks assigned by the system in a stable manner with high utilization. © 2022 IEEE.

关键词： Scheduling algorithms

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Comparative Analysis of mmWave Radar-based Object Detection in Autonomous Vehicles

Comparative Analysis of mmWave Radar-based Object Detection ...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Ziaaddin Sharifisoraki Marzieh Amini Sreeraman Rajan School of Information Technology Carleton University Ottawa Canada System and Computer Engineering Department Carleton University Ottawa Canada

Millimeter-wave radar technology is gaining popularity as a perception sensor in autonomous vehicles. This is due to its ability to detect nearby objects in adverse weather conditions, such as rain, snow, or fog, as well as its cost-effectiveness. In this paper, we explore the impact of different backbones and object detector heads on the performance of radar-based object detection algorithms. More specifically, we employ the RADDet dataset and its object detection algorithm which provides the entire Range-Azimuth-Doppler spectrum and incorporates an automatic annotation approach. We examine different backbones and object detector heads to identify optimal model combinations for autonomous driving applications. Our results show that using a YOLOv4 head integrated with a modified ResNet backbone leads to the highest mean average precision, reaching 66.3% with an intersection over union (IoU) of 0.1, and 43.6% with an IoU of 0.3. This observation will help to advance radar-based object detection, thereby enhancing safety and reliability in diverse environmental conditions.

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A-VMD: Adaptive Variational Mode Decomposition Scheme for Noise Reduction in Sensor-Cloud 2nd

A-VMD: Adaptive Variational Mode Decomposition Scheme for No...

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2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Huo, Zhenru Jia, Guoqing Fang, Weidong Chen, Wei Zhang, Wuxiong School of Physics and Electronic Information Engineering Qinghai Minzu University Xining810007 China Science and Technology On Micro-System Laboratory Shanghai Institute of Micro-System and Information Technology Chinese Academy of Sciences Shanghai201899 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Research and Development Center for Micro-Nano Electronics Shanghai201210 China School of Mechanical Electronic and Information Engineering China University of Mining and Technology-Beijing Beijing100083 China School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China

ISBN: (纸本)9789819902712

Digital signal processing is critical during the use of sensor-clouds, and the data information acquired by sensors is inevitably noisy. The Variational Mode Decomposition (VMD) algorithm can be used to reduce noise on the signal. The selection of the modal decomposition number of the VMD algorithm and the selection of the modal components of the reconstructed signal affect the effect of signal noise reduction. In this paper, an adaptive variational mode decomposition scheme is proposed. It adaptively selects the modal decomposition number through the established modal decomposition number and the input signal sample entropy model, selects the optimal modal component according to the sample entropy threshold, and reconstructs the signal after the selected modal component is processed by data smoothing. The results show that compared with traditional variational mode decomposition, empirical mode decomposition algorithms, and other algorithms, the effect of adaptive variational mode decomposition in filtering out signal noise is analyzed. The proposed adaptive VMD (A-VMD) algorithm can effectively filter out the noise of the signal obtained by the sensor. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Variational mode decomposition

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Opnet: Deep Occlusion Perception Network with Boundary Awareness for Amodal Instance Segmentation

Opnet: Deep Occlusion Perception Network with Boundary Aware...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shihui Zhang Ziteng Xue Yuhong Jiang Houlin Wang School of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

The Amodal Instance Segmentation (AIS) task aims to infer the visible and occluded regions of an object instance. Existing AIS methods typically focus on directly predicting visible and occluded regions or leveraging prior knowledge to guide predictions. However, these methods often ignore the perception of occluded views, leading to inaccurate results. To address this issue and achieve high-quality AIS, we propose a boundary-aware Occlusion Perception Network (OPNet). OPNet consists of three main components: the Dynamic Feature Augmentation Pyramid (DFAP), the Dual-path Boundary Aware Module (DBAM), and the Shape-guide Refinement Module (SRM). Specifically, DBAM employs an occlusion-perception strategy to learn discriminative features with boundary information, enabling it to distinguish occlusion from multiple views. Additionally, DFAP and SRM optimize the results by enhancing feature aggregation and imposing geometric constraints. Experiments on the D2SA, KINS, and CWALT datasets show that OPNet significantly outperforms state-of-the-art AIS methods that without prior knowledge. Code is available at https://***/ZitengXue/OPNet.

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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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