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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Nur Suhaili Mansor Hapini Awang Nor Hazlyna Harun Mohamad Ghozali Hassan Sarkin Tudu Shehu Malami Noraishah Shamsuddin Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Malaysia Disaster Management Institute (DMI) School of Technology Management and Logistics Universiti Utara Malaysia Sintok Malaysia Department of Computer Science and Information Technology Faculty of Science Sokoto State University Sokoto Nigeria ICI in Smart Manufacturing Sirim Bhd Shah Alam Malaysia

The development of industrial areas is frequently thought to have a good impact on society because it helps to meet the population's requirements. However, it also negatively impacts the environment, such as river pollution. If a country experiences rapid economic growth without considering the effects of industrial development on the environment, then human well-being will be affected. In this context, advanced development is attained by a developed nation while ensuring the well-being of society. Uncontrolled and unsustainable development increases environmental pollution, adversely affecting human health. As water is essential for human life, water resources, such as rivers, must be properly managed and protected to ensure human well-being. Thus, the study conducted on the changes in the water quality of the Kim Kim River provides insights into how unintended consequences of urban and industrial development can be identified and addressed using three sets of satellite imagery data from 2015, 2019 and 2020. The study was led to identify research problems and achieve the study objectives to obtain research results. The results of image classification analyses indicate that the development area near Sungai Kim Kim increased to 50%, with 38% from industrial areas. The RMSE for the image classification is 1.51, significantly better than the minimum allowed value of 2.00. It provides an understanding of the change in river water quality because of urban and industrial development. By highlighting the importance of sustainable development and the need to balance economic growth with environmental protection, this study contributes to the development of effective policies and strategies for the sustainable development of industrial areas.

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Evaluating Security of MQTT Protocol in Internet of Things

Evaluating Security of MQTT Protocol in Internet of Things

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Ayman Al-Ani Wong Kang Shen Ahmed K. Al-Ani Shams A. Laghari Omar E. Elejla Cybersecurity Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah Malaysia School of Computing and Data Science Xiamen University Malaysia Sepang Selangor Malaysia School of Engineering Technology and Applied Science Centennial College Toronto Ontario Canada National Advanced IPv6 Centre (NAv6) Universiti Sains Malaysia (USM) Gelugor Penang Malaysia Department of Computer Science Al-Aqsa University Gaza Palestine

The Internet of Things (IoT) has revolutionized the way people interact, communicate, and perform daily activities in various domains ranging from households to industries and cities. MQTT is one of the commonly adopted protocols for implementing IoT. However, IoT systems that are connected through MQTT are susceptible to security breaches as MQTT was not originally designed with security as a priority. The credentials and messages transmitted in plaintext by default, thereby compromising data confidentiality and integrity. This study presents a comprehensive analysis of the MQTT protocol, including experimentation on an MQTT system using various cryptographic implementations, such as AES-CBC, RSA, and ECC AES Hybrid Scheme, to assess the processing time and message size. The findings indicate that payload encryption increases processing time and message bytes. Among the cryptographic implementations, RSA incurs the highest processing time, followed by ECC AES Hybrid Scheme and AES- CBC. Furthermore, the study demonstrates the effectiveness of attack prevention between standard MQTT and secured MQTT implementations by simulating various IoT attacks, such as black-box penetration attack, identity spoofing, DoS attack, and MITM attack. The results and subsequent discussion provide insights that answer the research question, revealing the cryptographic algorithms that result in the most overhead on the standard MQTT implementation and their capacity to resist common attacks.

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Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives

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Informatics in Medicine Unlocked 2023年 38卷

作者： Kanyongo, Wellington Ezugwu, Absalom E. Department of Computer Science Faculty of Science Engineering Bindura University of Science Education Bindura 0771 603 900 Zimbabwe School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg Campus Pietermaritzburg 3201 South Africa Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom 2520 South Africa

Medication nonadherence is a significant public health concern that leads to ineffective treatment, which in turn engenders complications such as increased morbidity risks, unnecessary hospitalisations, and premature mortality. Technologies of the Fourth Industrial Revolution, such as machine learning, provide breakthroughs in identifying the most important features for building predictive models of medication adherence. Due to the diversity and complexity of medication adherence, it is crucial to leverage machine learning to determine significant medication adherence factors. This study systematically reviewed articles exhibiting feature selection and feature importance in research utilising machine learning to analyse medication adherence among Non-communicable diseases (NCD) patients. The articles were retrieved using Google Scholar, Research4Life, IEEE Xplore, and PubMed. The requirements for inclusion were met by 27 papers published between 2010 and 2022. The publications reviewed incorporate machine learning while also demonstrating feature selection and the importance of predictors of medication adherence in NCD patients with hypertension (n = 6), cardiovascular diseases (n = 6), diabetes (n = 4), opioid use disorder (n = 3), and other NCDs (n = 8). The findings demonstrate that medication adherence is a multifactorial issue influenced by various features such as sociodemographic and economic characteristics, medication information, behavioural, disease-related, and healthcare system-related factors. Some of these features, such as the patient's age, gender and race, cannot be modified. Once the patients with nonmodifiable features have been identified, they must be proactively monitored for medication adherence. On the other hand, adjustable risk features, such as self-efficacy and medication knowledge, can be modified and improved through medication education or medication adherence awareness. Various techniques for selecting and ranking features have emerg

关键词： Chronic disease Feature importance Feature selection (FS) Machine learning (ML) Medication adherence (MA) Non-communicable disease (NCD) Predictors of MA

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Feature Aggregation Network for Memory-Based Industrial Anomaly Detection

Feature Aggregation Network for Memory-Based Industrial Anom...

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IEEE International Conference on data science in Cyberspace (DSC)

作者： Min Li Delong Han Rui Shao Jinghui He Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation Network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet.

关键词： Training Runtime Shape Memory management Training data Production Feature extraction Robustness Anomaly detection Testing

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Women's Health Diary: Tracker System for Menstruation, Intermenstrual, Ovulation, and Postnatal Bleeding Using Rules-Based Technique

Women's Health Diary: Tracker System for Menstruation, Inter...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Nursyuhada binti Abdullah Azwa bin Abdul Aziz Wan Azani Mustafa Muntather Almusawi Abbas Hameed Abdul Hussein Faculty of Informatics & Computing Universiti Sultan Zainal Abidin (UniSZA) Besut Campus Terengganu Malaysia Data Science & Analytics (DASA) Special Interest Group (SIG) Universiti Sultan Zainal Abidin (UniSZA) Terengganu Malaysia Centre of Excellence Advanced Computing (CoE AdvComp) Universiti Malaysia Perlis Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

Menstruation, a natural physiological process in women, entails regular vaginal bleeding as part of the monthly menstrual cycle. While existing systems facilitate menstrual cycle tracking, they often lack the capability to differentiate between various types of vaginal bleeding, such as menstruation (haid), intermenstrual bleeding (istihadah), or postnatal bleeding (nifas). To address this limitation, the Women's Health Diary: Tracker System for Menstruation, Intermenstrual, Ovulation, and Postnatal Bleeding) was developed using a rule-based technique. This system employs an iterative method comprising eight phases: initial planning, planning, requirements, analysis and design, implementation, testing, evaluation, and deployment. By ensuring the optimal development and performance of this system, women can effectively manage their menstrual blood, a vital aspect of their overall health. Moreover, this system holds particular significance for every women, as it facilitates tracking and identification of regular and irregular cycles, enabling them to uphold the legitimacy of their worship.

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Structural Gender Imbalances in Ballet Collaboration Networks

arXiv

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arXiv 2023年

作者： Herrera-Guzmán, Yessica Lee, Eun Kim, Heetae Research Center for Social Complexity Universidad del Desarrollo Chile Department of Scientific Computing Pukyong National University Korea Republic of Department of Energy Engineering Korea Institute of Energy Technology Korea Republic of Data Science Institute Faculty of Engineering Universidad del Desarrollo Chile

Ballet, a mainstream performing art predominantly associated with women, exhibits significant gender imbalances in leading positions. However, the collaboration’s structural composition on gender representation in the field remains unexplored. Our study investigates the gendered labor force composition and collaboration patterns in ballet creations. Our findings reveal gender disparities in ballet creations aligned with gendered collaboration patterns and women occupying more peripheral network positions respect to men. Productivity disparities show women accessing 20-25% of ballet creations compared to men. Mathematically derived perception errors show the underestimation of women artists’ representation within ballet collaboration networks, potentially impacting women’s careers in the field. Our study highlights the structural disadvantages that women face in ballet and emphasizes the need for a more inclusive and equal professional environment to improve the career development of women in the ballet industry. These insights contribute to a broader understanding of structural gender imbalances in artistic domains and can inform cultural organizations about potential affirmative actions towards a better representation of women leaders in ballet. © 2023, CC BY.

关键词： Employment

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ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels

arXiv

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arXiv 2025年

作者： He, Changpeng Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated. © 2025, CC BY.

关键词： Graph neural networks

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： Image quality Quantization (signal) Image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing Highly Cited and Impactful Publications across Six Decades

arXiv

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arXiv 2023年

作者： Ezugwu, Absalom E. Greeff, Japie Ho, Yuh-Shan Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa School of Computer Science and Information Systems Faculty of Natural and Agricultural Sciences North-West University Vanderbijlpark South Africa Trend Research Centre Asia University No. 500 Lioufeng Road Taichung41354 Taiwan

Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields, thereby driving advancements in other domains of interest. As the field continues to evolve, it is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contributions made thus far. In this paper, we present a comprehensive bibliometric analysis of highly cited ML publications. We collected a dataset consisting of the top-cited papers from reputable ML conferences and journals, covering a period of several years from 1959 to 2022. We employed various bibliometric techniques to analyze the data, including citation analysis, co-authorship analysis, keyword analysis, and publication trends. Our findings reveal the most influential papers, highly cited authors, and collaborative networks within the machine learning community. We identify popular research themes and uncover emerging topics that have recently gained significant attention. Furthermore, we examine the geographical distribution of highly cited publications, highlighting the dominance of certain countries in ML research. By shedding light on the landscape of highly cited ML publications, our study provides valuable insights for researchers, policymakers, and practitioners seeking to understand the key developments and trends in this rapidly evolving field. © 2023, CC BY.

关键词： Geographical distribution

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Personalized Care for Multiple Sclerosis with IoT Wearables and Deep Learning

Personalized Care for Multiple Sclerosis with IoT Wearables ...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： D Vetriselvi Rajeshkumar Sampathrajan A. Deepa V. Sujatha P. Pandi Deepa S. Murugan Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India McKinsey & Company Fort Worth Texas USA Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Salem Tamil Nadu India Department of Artificial Intelligence and Data Science Agni College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

This research work presents a new method for improving individualized treatment for patients with Multiple Sclerosis (MS) by combining Internet of Things (IoT) wearables with deep learning (DL) methods, namely Long Short-Term Memory (LSTM) networks. MS is a long-term autoimmune disorder that affects the central nervous system. It is marked by unexpected episodes of worsening symptoms and periods of improvement, highlighting the need for tailored therapy. The widespread use of IoT devices presents unique possibilities for the ongoing monitoring of many physiological and activity measurements important to the development and symptoms of MS. Using LSTM networks, which are very proficient in analyzing temporal sequences, create a prediction model that may anticipate exacerbations of MS symptoms and customize therapies appropriately. Through the analysis of longitudinal data obtained from IoT devices, our model acquires knowledge about unique patterns of illness development and treatment response. This knowledge allows us to create customized care plans for individuals. These paths include proactive activities such as adjusting medication, making lifestyle changes, and seeking early medical advice. The goal is to reduce the worsening of symptoms, improve the quality of life, and optimize treatment results for individuals with MS. The proposed method shows potential for transforming the treatment of MS by offering proactive and patient-centered therapy adaptable to individual requirements and the dynamics of the illness.

关键词： Deep learning Analytical models Multiple sclerosis Accuracy Predictive models Loss measurement Internet of Things Wearable devices Biomedical monitoring Long short term memory

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