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IEEE International Conference on Multi Topic

作者： Ali Saeed M. Usman Akram Muazzam A. Khan M. Belal Afsar Department of Computer Science Quaid-i-Azam University Islamabad Pakistan Department of Computer and Software Engineering National University of Sciences & Technology Islamabad Pakistan Faculty of Electrical Engineering and Information Technology Otto-von-Guericke University Maddeburg Germany

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

The global market of machine condition monitoring is projected to grow at a rate of 8.3% in next five years. The recent technological advancement in IoTs and AI has driven predictive maintenance to be one of the most effective approach in this domain. Vibration analysis is the most efficient technique used to carry out predictive maintenance, using advanced data- driven intelligent approaches. The vibration data carries most significant information regarding the health state of machine components especially bearings. The proposed hybrid framework utilizes CNN and transformer utilizing the complex weightsharing capabilities of CNNs, combined with ability of transformer to capture the broader context of spatial relationships in large-scale patterns, making it suitable for datasets of varying sizes. A fault detection accuracy of 98.86% is achieved through experimentation on a run-to-failure real-industrial environment dataset composed of vibration data of large-scale coaxial fans.

关键词： Vibrations Deep learning Condition monitoring Fans Accuracy Fault detection Transformers Data models Maintenance Predictive maintenance

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${\sf Img2Acoustic}$Img2Acoustic: A Cross-Modal Gesture Recognition Method Based on Few-Shot Learning

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IEEE Transactions on Mobile Computing 2024年第3期24卷 1496-1512页

作者： Yongpan Zou Jianhao Weng Wenting Kuang Yang Jiao Victor C. M. Leung Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Information Hub Hong Kong University of Science and Technology Guangzhou China

Acoustic-based human gesture recognition (HGR) offers diverse applications due to the ubiquity of sensors and touch-free interaction. However, existing machine learning approaches require substantial training data, making the process time-consuming, costly, and labor-intensive. Recent studies have explored cross-modal methods to reduce the need for large training datasets in behavior recognition, but they typically rely on open-source datasets that closely align with the target domain, limiting flexibility and complicating data collection. In this paper, we propose ${\sf Img2Acoustic}$ , a novel cross-modal acoustic-based HGR approach that leverages models trained on open-source image datasets (i.e., EMNIST, Omniglot) to effectively recognize custom gestures detected via acoustic signals. Our model incorporates a task-aware attention layer (TAAL) and a task-aware local matching layer (TALML), enabling seamless transfer of knowledge from image datasets to acoustic gesture recognition. We implement ${\sf Img2Acoustic}$ on commercial devices and conduct comprehensive evaluations, demonstrating that our method not only delivers superior accuracy and robustness compared to existing approaches but also eliminates the need for extensive training data collection.

关键词： Training Gesture recognition Acoustics Data models Sensors Feature extraction Target recognition Mobile computing Image recognition Few shot learning

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Diabetes Prediction Using Machine Learning Techniques: A Case Study from Jordan

Diabetes Prediction Using Machine Learning Techniques: A Cas...

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Information Technology (ACIT)

作者： Raed Alazaidah Radwan Batyha Ghassan Samara Esraa Al Henawi Maram Y. Al-Safarini Ahmad A. Samhan Ali Elrashidi Mo’Ath Alluwaici Deana Al-Omari Department of Data Science and AI Faculty IT Zarqa University Zarqa Jordan Department of Computer Science Faculty of IT Applied Science Private University Amman Jordan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Department of Software Engineering Faculty of IT Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Diabetes is one of the most common diseases in Jordan. It is the main reason of death among Jordanian adult citizens. Worldwide, 48% of all deaths are due to Diabetes occurred before the age of 70 years. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that BayesNet and NaiveBayes are the best classification models to handle the task of the early prediction of Diabetes in Jordan

关键词： Measurement Machine learning Predictive models Diabetes Bayes methods Information technology Diseases

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Urine dataset having eight particles classes

arXiv

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arXiv 2023年

作者： Tuncer, Taner Erkuş, Merve Çınar, Ahmet Ayyıldız, Hakan Tuncer, Seda Arslan Fırat University Faculty of Engineering Computer Engineering Elazığ23119 Turkey Elazığ Fethi Sekin Central Hospital Elazığ23000 Turkey Fırat University Faculty of Engineering Software Engineering Elazığ23119 Turkey

Urine sediment examination (USE) is one of the main tests used in the evaluation of diseases such as kidney, urinary, metabolic, and diabetes and to determine the density and number of various cells in the urine. USE's manual microscopy is a labor-intensive and time-consuming, imprecise, subjective process. Recently, automatic analysis of urine sediment has become inevitable in the medical field. In this study, we propose a dataset that can be used by artificial intelligence techniques to automatically identify particles in urine sediment images. The data set consists of 8509 particle images obtained by examining the particles in the urine sediment obtained from 409 patients from the Biochemistry Clinics of Elazig Fethi Sekin Central Hospital. Particle images are collected in 8 classes in total and these are Erythrocyte, Leukocyte, Epithelial, Bacteria, Yeast, Cylinders, Crystals, and others (sperm, etc.). © 2023, CC BY-NC-ND.

关键词： Sediments

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Beyond Data, Towards Sustainability: A Sydney Case Study on Urban Digital Twins

arXiv

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arXiv 2024年

作者： Sohail, Ammar Shen, Bojie Cheema, Muhammad Aamir Ali, Mohammed Eunus Ulhaq, Anwaar Babar, Muhammad Ali Qureshi, Asama Faculty of Information Technology Monash University Australia Department of Computer Science and Engineering BUET Bangladesh School of Engineering and Technology Central Queensland University Australia Centre for Research on Engineering Software Technologies University of Adelaide Australia

As urban areas grapple with unprecedented challenges stemming from population growth and climate change, the emergence of urban digital twins offers a promising solution. This paper presents a case study focusing on Sydney's urban digital twin, a virtual replica integrating diverse real-time and historical data, including weather, crime, emissions, and traffic. Through advanced visualization and data analysis techniques, the study explores some applications of this digital twin in urban sustainability, such as spatial ranking of suburbs and automatic identification of correlations between variables. Additionally, the research delves into predictive modeling, employing machine learning to forecast traffic crash risks using environmental data, showcasing the potential for proactive interventions. The contributions of this work lie in the comprehensive exploration of a city-scale digital twin for sustainable urban planning, offering a multifaceted approach to data-driven decision-making. © 2024, CC BY.

关键词： Risk analysis

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Data Transmission with Priority Service and Limited Capacity Storage in Unstationary Flow and High Load

Data Transmission with Priority Service and Limited Capacity...

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Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED)

作者： S. Z. Lygdenov I. Z. Lygdenova Faculty of Software Engineering and Computer Engineering ITMO University Saint-Petersburg Russia Medical Faculty First St. Petersburg University named after Academician Pavlov Saint-Petersburg Russia

Subject of research. The influence of priority service in a multichannel data transmission system with drives of limited capacity and high load with a non-stationary nature of the intensity of packets entering the system and service time in channels. Method. Calculation and analysis of the functional characteristics of a multichannel system is realized using simulation methods and mathematical statistics. Main results. A simulation model of a multichannel system with priority service is proposed to calculate the functional characteristics of a multichannel system with a high load. A number of experiments were carried out on the influence of priority maintenance on the efficiency and reliability of the system. Dependencies between stationary and non-stationary functional quantities have been identified. Practical significance. The presented research results can be used in the design of real multithreaded data transmission systems with a highly heterogeneous load.

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Materials Behaviour Analysis of 3D Printed Brass-PLA Filament 7

Materials Behaviour Analysis of 3D Printed Brass-PLA Filamen...

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7th International Conference on Mechanical engineering Research 2023, ICMER 2023

作者： Rajan, Dinesh Samykano, M. Suraparaju, S.K. Moorthy, K. Kadirgama, K. Ramasamy, D. Pandey, A.K. Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia Centre for Research in Advanced Fluid and Process University Malaysia Pahang Al-Sultan Abdullah Lebuhraya Tun Razak Pahang Kuantan Gambang26300 Malaysia Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Al-Sultan Abdullah Kuantan Malaysia School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia

The current study aims to bridge a crucial gap in existing research, potentially paving the way for a groundbreaking transformation in the development and application of PLA/Brass composites within diverse industries such as aerospace, automotive, consumer goods, and medical devices. The primary objective of this research is to assess the mechanical properties of a composite material made up of Polylactic Acid (PLA) and Brass, produced using Fused Deposition Modelling (FDM) 3D printing technology. Brass, renowned for its exceptional mechanical properties, has been integrated into PLA to form this composite material. The study employs various analytical techniques, including Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray Spectroscopy (EDX), to scrutinize the chemical and physical characteristics of the PLA/Brass composite. This research revolves around exploring the impact of different printing parameters on the mechanical behavior of the printed specimens. The investigation delves into aspects such as tensile strength, compression resistance, bending properties, and impact resistance. To achieve this, test specimens with varying compositions have been produced using a Raise3D N2 Plus FDM 3D printer, with careful manipulation of printing parameters such as layer height and printing speed. The compositional variations range from 15% wt. to 80% wt., with layer height values spanning 0.25 mm, 0.30 mm, and 0.35 mm, and printing speeds ranging from 20 mm/s to 40 mm/s. The outcomes of this research have revealed the distinct influences of specific printing parameters on various mechanical properties. For example, in the context of tensile testing, it was observed that the combination of a layer height of 0.25 mm and a printing speed of 30 mm/s resulted in the highest elastic modulus. Similarly, the study provides crucial insights into optimizing PLA/Brass composite material properties through controlled addit

关键词： Scanning electron microscopy

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ML Techniques for Employee Performance Prediction

ML Techniques for Employee Performance Prediction

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kumar, Brajesh Agrawal, Prateek Uike, Dipesh Lourens, Melanie Bhati, Indira Peter, M. Chandrakumar Dr. Apj Abdul Kalam Women's Institute of Technology Department of Computer Science & Engineering Bihar Darbhanga India India Dr. Ambedkar Institute of management studies and research Maharashtra Nagpur India Durban University of Technology Faculty of Management Sciences South Africa Department of Management India PeriyarManiammai Institute of Science and Technology Department of Software Engineering Tamilnadu Thanjavur India

ISBN: (纸本)9798331543617

When it comes to maximizing the effectiveness of a business and promoting professional growth, employee performance prediction is an extremely important factor. This research article investigates the use of machine learning (ML) approaches to forecast employee performance, with a particular emphasis on the incorporation of different algorithms from different sources to improve accuracy and dependability. Methods that have been used for a long time, such as linear regression and decision trees, are evaluated alongside more recent approaches, such as ensemble methods and deep learning. To refine the prediction models, our research makes use of approaches such as feature selection and dimensionality reduction. These techniques are utilized by utilizing previous performance data, demographic information, and behavioral indicators. It has been demonstrated through the analysis that sophisticated machine learning techniques, in particular ensemble and deep learning models, are superior to traditional methods when it comes to anticipating employee performance. Our findings offer HR departments concrete insights that can be used to adopt data-driven methods for performance management, which will eventually contribute to improved organizational outcomes and increased employee satisfaction. This paper offers opportunities for future development in predictive analytics within the field of human resources and emphasizes the potential of machine learning to alter performance evaluation systems. © 2024 IEEE.

关键词： Predictive analytics

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Introduction to the Special Issue on Thriving Amidst Disruptive Technologies

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Distributed Ledger Technologies: Research and Practice 2024年第3期3卷 1-2页

作者： Jairo Gutierrez Amarolinda Klein Patrick C. K. Hung Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Unisinos - Campus de Porto Alegre Porto Alegre Brazil Faculty of Business and IT Ontario Tech University Oshawa Canada

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Clinically Significant Prostate Cancer Detection and Diagnosis in Bi-Parametric MRI with Deep Learning Models

Clinically Significant Prostate Cancer Detection and Diagnos...

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International Conference on computer Science and engineering (UBMK)

作者： Clinton Binda Asoh-Itambi Mecit Yüzkat Songül Varlı Department of Computer Engineering Faculty of Electrical and Electronics Yıldız Technical University Istanbul Türkiye Department of Software Engineering Faculty of Engineering and Architecture Muş Alparslan University Health Institutes of Türkiye Istanbul Türkiye Department of Computer Engineering Faculty of Electrical and Electronics Yıldız Technical University Health Institutes of Türkiye Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

This study presents a significant improvement in the detection and diagnosis of clinically significant prostate cancer (csPCa) in bi-parametric magnetic resonance imaging (bpMRI) by adapting the nnU-Net framework. We address the inherent limitations of traditional imaging analysis techniques by modifying the loss function used in nnU-Net, replacing the default combination of Cross-Entropy and soft Dice loss with a novel integration of Cross-Entropy loss and Focal loss. This modification targets class imbalance and enhances the detection sensitivity for less represented, clinically significant lesions, which are crucial for effective csPCa management while minimizing false diagnosis. Employing a semi-supervised learning approach, the modified nnU-Net was trained and validated on the PI-CAI (Prostate Imaging: Cancer AI) Public Training dataset (1500 cases), the current benchmark dataset for csPCa detection and diagnosis. It was also tested on the PI-CAI Hidden Testing cohort dataset consisting of 100 unseen cases. These datasets offer a comprehensive and diverse collection of prostate MRI exams, providing a robust foundation for model training and testing. We conducted a rigorous 5-fold cross-validation to ensure the robustness and reproducibility of our findings. The model's performance was evaluated with Average Precision (AP) at the lesion level and Area Under the Receiver Operating Characteristics curve (AUROC) at the patient level. Our model with AUROC and AP of 0.824 and 0.603 respectively on the Hidden Tuning cohort, outperformed the state-of-the-art U-Net, nnDetection models, and other nnU-Net variants. This work contributes to ongoing efforts to refine diagnostic tools in medical imaging, offering the potential for more accurate and timely prostate cancer screenings.

关键词： Training Sensitivity Magnetic resonance imaging Receivers Semisupervised learning Robustness Reproducibility of results Lesions Prostate cancer Tuning

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