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Frontiers of Electronics, Information and Computation Technologies (ICFEICT), International Conference on

作者： Yingxu Wang International Institute of Cognitive Informatics and Cognitive Computing (I2CICC) Stanford Univ. (2008|2016) MIT (2012) UC Berkeley (2008) Oxford Univ. (1995|2018-2023) Tsinghua Univ. (2019-2022) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary 2500 University Drive NW Calgary Alberta Canada

Latest advances in Intelligence Science (IS) [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26] underpinned by breakthroughs in Intelligent Mathematics (IM) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50] have enabled the convergence of contemporary information, computation, and electronic sciences and technologies. This trend has led to novel theories and disruptive technologies across the fields of Cognitive informatics (CI) [6], [13] and Cognitive computing (CC) [1] as fundamental theories of intelligence science and Autonomous AI (AAI) [12].This keynote lecture presents the latest advances in electronics, information, and computing theories underpinned by cognitive informatics [5], [10], [13], cognitive computing [1], [25] and their IM foundations. It will focus on three themes: a) The fundamental mechanisms of cognitive information representations and manipulations in the brain for cognitive computing; b) The contemporary IM theories for building the theoretical framework of IS towards autonomous and real-time intelligence generation by Cognitive Computers (κC) [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50]; and c) The architecture of κC towards full AAI for unleashing machine intelligence enabled by Brain-Inspired Systems (BIS) [14], [51], [73] driven by IM. Applications of KC and IM will be demonstrated by pilot projects and experiments in novel intelligent computing systems [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89].

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DeepEarthMY: A Remote Sensing Dataset for Tropical Land-Cover Segmentation

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IEEE Access 2025年 13卷 69103-69115页

作者： Mohammed Najib, Shaaban Pee, Chih-Yang Ting, Choo-Yee Wong, Lai-Kuan Multimedia University Faculty of Computing and Informatics Selangor Cyberjaya63100 Malaysia

Land-cover mapping is essential for applications such as urban planning, natural resource management, and environmental monitoring. However, tropical equatorial regions pose unique challenges to land-cover classification due to dense vegetation, persistent cloud cover, and spectral similarity across land-cover types. Moreover, existing land-cover classification models often perform poorly in these regions as they are primarily trained on datasets from non-equatorial climates. To address this gap, we introduce DeepEarthMY, a high-resolution land-cover mapping dataset specifically designed for equatorial regions. The dataset contains 4,007 meticulously annotated images from 52 diverse regions across Malaysia, representing key land-cover types such as forests, buildings, roads, water bodies, agricultural lands, and barren land. We evaluated the dataset by performing land-cover segmentation on selected state-of-the-art semantic segmentation models, including DC-Swin, HRNet, and UNetFormer. The experimental results reveal that the DC-Swin model achieves the best performance, with a mIoU score of 80.23%. To further evaluate the significance of region-specific datasets for land-cover classification, we performed cross-dataset testing on two datasets: DeepEarthMY from the equatorial region and LoveDA from the temperate climate, using five-fold cross-validation. HRNet models trained on DeepEarthMY achieved a mIoU of 77.2% on DeepEarthMY but only 33.2% on the LoveDA test set. Conversely, models trained on LoveDA achieved 51.4% and 43% on the LoveDA and DeepEarthMY test sets, respectively. This significant performance gap highlights the need for region-specific datasets to advance land-cover research, particularly in equatorial climates where land-cover datasets are scarce. In conclusion, the DeepEarthMY dataset can be an invaluable resource for remote sensing in the equatorial region. The dataset is available at https://***/records/14242124. © 2013 IEEE.

关键词： Semantic Segmentation

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Investigation into the Identification of AI-Generated Short Dialectal Arabic Texts

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IEEE Access 2025年 13卷 85131-85138页

作者： Alharthi, Haifa Saudi Electronic University College of Computing and Informatics Riyadh Saudi Arabia

Identifying AI-generated text (AIGT) has become crucial in addressing the potential misuse of generative AI tools and their impact on trust, fairness, and authenticity of content. This study investigates the detection of AIGT in short dialectal Arabic texts, an area that, to our knowledge, has not been previously explored. Two datasets were constructed featuring texts that were rephrased or generated to match the topics and length of MADAR CORPUS, encompassing dialectical human-written texts (HWTs) originating from five major Arabic cities. Among several AIGT detection methods, the pre-trained language models AraELECTRA and AraBERT outperformed both machine learning and feature based approaches. Moreover, we analyzed the key features that significantly influenced the accuracy of the model. © 2013 IEEE.

关键词： Natural language processing systems

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On detecting stock price manipulation attacks: a comprehensive systematic literature review

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Multimedia Tools and Applications 2025年 1-78页

作者： Alfajeer, Amal Altaweel, Ala Bouridane, Ahmed Mouheb, Djedjiga Aslam, Sidra College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Stock market is pivotal to economical systems. It is a platform where listed stocks of companies can be bought/sold by market participants to gain profit. In 2020, the total capitalization of all markets reached $95 trillion. Within such profiting platforms, malicious attacks like stock price/market manipulation are conducted. Stock price manipulation attack refers to the strategic act of trading either a stock, a future (derivative financial contracts), or a security (financial instrument) to increase or decrease their prices. Hence, making significant profits. Manipulation types vary by sources and/or exploited methods. To detect and deter such attacks/practices, extensive research have been conducted. To further assist the future studies, in this paper, we conduct a systematic literature review to extract research articles relevant to stock price/market manipulation attacks and their state-of-art detection systems/solutions. To the best of our knowledge, this is the first systematic literature review that is based on a predefined search methodology and systematically collects the relevant articles that were published between 2011 and 2021. After applying the systematic search methodology, we extracted and surveyed 66 articles and classified them into four main detection approaches. Namely, statistical-based, Machine Learning based, system-based/artefacts, and visualization-based/observation-based. We provide a thorough comparison among the detection systems in terms of utilized datasets, leveraged features, employed optimization techniques, and applied performance measures. Our survey also identifies and discusses a set of existing research gaps such as generality of detection approaches, near/real-time detection capability, validity of real/synthesised datasets, etc. We concluded our survey by proposing several research topics and recommendations to be investigated and addressed by future works. © The Author(s), under exclusive licence to Springer Science+Busine

关键词： Profitability

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Aspect-based sentiment analysis in MOOCs: a systematic literature review introducing the MASC-MEF framework

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Journal of King Saud University - Computer and Information Sciences 2025年第1期37卷 1-33页

作者： Awadh, Wed Akeel Sulaiman, Rosnafisah Bte Mahmoud, Moamin A. Institute of Informatics and Computing in Energy University Tenaga Nasional Kajang Malaysia Department of Computer Information Systems University of Basrah Basrah Iraq Institute of Informatics and Computing in Energy Department of Informatics Universiti Tenaga Nasional Jalan IKRAM UNITEN Selangor Kajang Malaysia Institute of Informatics and Computing in Energy Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia

Aspect-based sentiment analysis (ABSA) in massive open online courses (MOOCs) offers a powerful tool to understand student experiences and improve the quality of online education. As MOOCs grow in popularity, there is an increasing need to analyze student feedback at a granular level, focusing on specific course aspects rather than just overall sentiment. Despite ABSA’s potential in MOOCs, research in this specific domain remains limited. Thus, this review of the literature seeks to fill this gap by systematically examining ABSA within the realm of MOOCs. We utilized a PRISMA framework to guide the search process. Consequently, this review covered 57 studies conducted between 2019 and 2024, retrieved from eight electronic research databases. The studies included in this analysis were examined on the basis of their datasets, data preprocessing, ABSA tasks in MOOCs, evaluation aspects in online courses, fusion approach in ABSA, techniques of word representations, and evaluation criteria utilized to evaluate the proposed models. Additionally, this review introduces the multiaspect sentiment classification using multiembedding and fusion (MASC-MEF) framework, a novel approach that uniquely incorporates multiword embeddings and multifeature extraction along with advanced classification techniques. Results shed light on several critical issues and various limitations, pointing towards future directions for ABSA research within the realm of MOOCs. This review underscores significant contributions to improving ABSA in educational technology, with a focus on course effectiveness and advancements in online learning, guiding researchers towards innovative techniques to tackle this challenge in the future. © The Author(s) 2025.

关键词： Aspect-based sentiment analysis Massive open online courses Multiaspect sentiment classification Online education technology Systematic literature review

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Enhancing IoT Security: A Comparative Study of CNN and RNN-Based Anomaly Detection Using the CICIoT2023 Dataset

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第5期52卷 1424-1441页

作者： Al-Ghamdi, Amal M. Alansari, Marwah M. at College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Information Technology Department College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The rapid growth of Internet of Things (IoT) devices has resulted in numerous security challenges. Anomaly detection has become a crucial aspect of IoT security, as it helps identify unusual behaviors and potential threats within IoT networks. Deep learning techniques have shown considerable potential in recognizing anomalies across various domains. However, the dynamic and continuously developing nature of cyberattacks complicates the process of monitoring and identifying among different types of attacks effectively. This study aims to enhance IoT security by conducting a comparative analysis of two deep learning algorithms: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We deploy these models on the new CICIoT2023 dataset to detect large-scale attacks targeting IoT devices. Our findings reveal that the CNN model outperforms others with an accuracy of 98%, while the BiLSTM model achieves 85%, and the hybrid CNN-BiLSTM model reaches 94%. These results show that CNNs are better at extracting spatial features and that hybrid models might be able to use both spatial and temporal dependencies. By providing insights into model selection and optimization, this research contributes to the development of robust intrusion detection systems for IoT security. © (2025), (International Association of Engineers). All rights reserved.

关键词： Recurrent neural networks

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Identifying the Barriers to Human-Centered Design in the Workplace: Perspectives from UX Professionals

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Proceedings of the ACM on Human-Computer Interaction 2025年第2期9卷 1-25页

作者： Gorichanaz, Tim College of Computing & Informatics Drexel University PhiladelphiaPA United States

Human-centered design, a theoretical ideal, is sometimes compromised in industry practice. Technology firms juggle competing priorities, such as adopting new technologies and generating shareholder returns, which may conflict with human-centered design values. This study sought to identify the types of workplace situations that present barriers for human-centered design, going beyond the views and behaviors of individual professionals. Q methodology was used to analyze the experiences of 14 UX professionals based in the United States. Five factors were identified, representing workplace situations in which human-centered design is inhibited, despite the involvement of UX professionals: Single-Minded Arrogance, Competing Visions, Moving Fast and Breaking Things, Pragmatically Getting By, and Sidestepping Responsibility. Underpinning these five factors are the dimensions of speed and clarity of vision. This paper demonstrates connections between the literature on UX ethics and human-centered design practice, and its findings point toward opportunities for education and intervention to better enable human-centered and ethical design in practice. © 2025 Owner/Author.

关键词： Professional aspects

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Analyzing Cybersecurity Risk with a Phishing Simulation Website 24

Analyzing Cybersecurity Risk with a Phishing Simulation Webs...

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9th International Conference on Cloud computing and Internet of Things, CCIOT 2024

作者： Ali, Alya Nasuha Binti Mohamed Rahman, Siti Husna Abdul Faculty of Computing & Informatics Multimedia University Selangor Malaysia

ISBN: (纸本)9798400717161

Phishing is a cyberattack that exploits human psychology and technical vulnerabilities. Previous research has revealed inconsistency of relation between age and phishing susceptibility. Hence, this project aims to identify which age group is more susceptible to phishing. Therefore, to achieve these objectives, this project developed and deployed a phishing simulation website to analyse which age group is more vulnerable. Respondents are considered successfully phished if they submitted information to the phishing website. The project adopted previous researcher's phishing process with addition of two phases as a guideline. The phases were Planning, Preparation, Attack, Valuable Acquisition, Knowledge Transfer and Result Analysis. A blog was developed to redirect phishing victims to an e-Learning page to raise their cybersecurity awareness. Persuasion techniques used in this simulation were Reward and Urgency. The phishing website gained 146 clicks and 89 form submissions. Adults aged 18 to 59 years old (group A) has a higher phishing success rate of compared to adults aged 60 years and above (group B). These findings suggest younger adults are more susceptible to phishing. Future research should involve larger sample size and seek broader input from participants. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Phishing

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Stunting Classification Among Toddlers Using Decision Tree C4.5 in Berastagi City

Stunting Classification Among Toddlers Using Decision Tree C...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Saveria, Hygea Gunawan, Putu Harry Informatics School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Stunting is a condition where a child's height significantly falls below the average for their age, primarily due to prolonged malnutrition and inadequate nutrient intake. This condition poses long-term challenges, affecting both physical growth and cognitive development. This study focuses on developing a predictive model using the C4.5 Decision Tree algorithm to classify the stunting status of children under five. The dataset comprises nutritional status data of toddlers collected from the Berastagi Health Center. Incorporating attributes such as gender, birth weight and height, age in months, weight, height, and the height-to-age ratio. The model aims to enhance the accuracy of stunting classification to support targeted prevention and treatment strategies. When compared to Naive Bayes, the Decision Tree C4.5 demonstrated superior performance, achieving an F1 score and average accuracy of 86%. This reflects a robust balance between precision and recall, underscoring its reliability in identifying children at risk of stunting. The research highlights the critical role of data-driven methodologies in public health. With its high accuracy, the model serves as a promising tool for healthcare providers, enabling more effective interventions to reduce stunting prevalence and improve nutritional outcomes among children under five years of age. © 2025 IEEE.

关键词： Health risks

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A Robust Resource-Aware Framework for YOLO-Based Object Detection in Dynamic and Multi-Platform Resource-Constrained Environments

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IEEE Access 2025年 13卷 79977-79995页

作者： Shiddiqi, Ary Mazharuddin Navastara, Dini Adni Studiawan, Hudan Romadhona, Yasinta Azhar, Muhammad Fath Mushaffa Rahma, Fayruz Bramantoro, Arif Alzahrani, Ahmad A. Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Informatics Surabaya60111 Indonesia Informatics Study Program Telkom University Surabaya Campus Surabaya60231 Indonesia Universitas Islam Indonesia Department of Informatics Yogyakarta55584 Indonesia Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri BegawanBE1410 Brunei King Abdulaziz University Faculty of Computing and Information Technology Jeddah21589 Saudi Arabia

Efficient management of resources such as CPU, RAM, and disk storage is a critical challenge for object detection systems that rely on multi-camera video streaming, especially for resource-constrained devices. These constraints frequently result in the risk of overheating and potential system failure during high workloads. This paper presents a novel Resource-Aware Framework (RA-Framework), designed to optimize resource allocation using dynamic adaptation mechanisms. The RA-Framework dynamically manages CPU, RAM, and disk storage resources for YOLO-based object detection, supporting multiple camera streams and scheduling mechanisms. The framework was implemented in resource-constrained environments using various YOLO models with single and dual-camera setups. Experimental results showed that the RA-Framework successfully reduced average CPU usage by up to 68.62%, decreased RAM consumption by approximately 13.47%, and extended the time for disk usage to increase by 1% by more than 500% in specific scenarios. In contrast, systems without the RA-Framework experienced higher resource utilization and greater susceptibility to resource scarcity or even overheating. © 2025 IEEE.

关键词： Resource allocation

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