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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Kadu, Anil Dharashive, Rajnandini Nimje, Rachit Rajvaidya, Arya Palkar, Sanket Gaikwad, Vijay Vishwakarma Institute of Technology Department of Instrumentation and Control Pune India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vishwakarma Institute of Technology Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798331519056

In recent years, the importance of ensuring road safety has intensified, prompting the development of advanced driver monitoring systems. This paper presents a real-time bus driver monitoring system utilizing a Raspberry Pi, OpenCV, a RaspiCam and machine learning techniques. The system aims to enhance passenger safety by detecting driver fatigue and distraction, which are major contributors to road accidents. The proposed solution leverages the Raspberry Pi's processing capabilities and the RaspiCam's imaging features to continuously capture and analyze the driver's facial expressions and eye movements. OpenCV's robust image processing algorithms are employed to identify signs of drowsiness and inattentiveness. The innovative aspects of the proposed solution include the use of Eye Aspect Ratio (EAR) for drowsiness detection and Mouth Aspect Ratio (MAR) for detecting yawning. Both the EAR and MAR are calculated through real-time facial landmark analysis. The system issues alerts when unsafe driving behavior is detected, thereby allowing for timely intervention. The proposed monitoring system is cost-effective and scalable. It offers a promising approach to improving public transportation safety by mitigating the risks associated with driver fatigue and distraction. Experimental results showed an accuracy of 92% for fatigue detection and 88% for distraction detection. This demonstrates the system's effectiveness in real-time detection and its potential for deployment in commercial bus fleets. © 2024 IEEE.

关键词： Bus drivers

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

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computer Modeling in engineering & Sciences 2023年第12期137卷 2163-2192页

作者： Ali S.Alghamdi Mohana Alanazi Abdulaziz Alanazi Yazeed Qasaymeh Muhammad Zubair Ahmed Bilal Awan M.G.B.Ashiq Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka72341Saudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityAr’Ar73222Saudi Arabia Automation and Control Department College of Engineering TechnologyUniversity of Doha for Science and TechnologyDoha24449Qatar Department of Electrical and Computer Engineering College of Engineering and Information TechnologyAjman UniversityAjman20550United Arab Emirates Department of Physics College of ScienceImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried *** has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the *** standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight *** proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub ***,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.

关键词： Stochastic energy hub scheduling energy profit uncertainty Hong’s two-point estimate method improved artificial rabbits optimization

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Quality-Relevant Modeling and Monitoring of Industrial Cyber-Physical Systems: The Semi-Supervised Dynamic Latent Variable Models

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 39-47页

作者： Zhou, Le Wang, Yaoxin Wu, Yuanqing He, Shenghuang Song, Zhihuan Zhejiang University of Science and Technology Department of Automation and Electrical Engineering Hangzhou310023 China Guangdong University of Technology Department of Automation Guangzhou510006 China Dongguan University of Technology Department of Computer Science and Technology Dongguan523808 China Ningbo Industrial Internet Institute Ningbo315012 China Zhejiang University Department of Control Science and Engineering Hangzhou310027 China

In modern industrial cyber-physical systems, a mass of process variables has been obtained by the high-sampling online sensors. Meanwhile, the key quality indexes are usually obtained infrequently from the laboratory. Hence, these quality variables are with low sampling rate. To avail of the complete process and quality variables with various sampling rates in the dynamic processes, a set of semi-supervised dynamic latent variable models are proposed for dynamic modeling and quality-relevant monitoring. The proposed models have built a unified structure to consider both the auto-correlations and cross-correlations between the process and quality variables with unbalanced sampling sizes. Hence, the feature extraction of the time series data is dynamically adjusted under the guidance of the quality variables. Then, the quality-relevant monitoring schemes are proposed, which is validated by a numerical case and an actual wastewater treatment process. © 2024 IEEE. All rights reserved.

关键词： Indexes Probabilistic logic Kalman filters Noise Cyber-physical systems Analytical models Parameter estimation Time series analysis Process monitoring Numerical models

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Emotion detection on phone calls during emergency using ensemble model with hyper parameter tuning

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International Journal of Information Technology (Singapore) 2023年第2期15卷 745-757页

作者： Selvan, A. Kalai Nimmi, K. Janet, B. Sivakumaran, N. C-DAC Thiruvananthapuram India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India

The paper’s main focus is on speech-based emotion detection in Malayalam phone call records, including emergency calls [(emergency response support system (ERSS)] and elicited *** emotions taken into consideration are fear, anger, sad, disgust, panic, anxious are among the emotions considered, while all other emotions are labeled as others. When reporting an emergency, callers commonly exhibit emotional stress, which results in speech output discrepancies. In such uncontrolled situations, determining caller qualities is exceedingly challenging due to poor speech quality and background noise. Fear, anger, and sadness are the three basic emotions expressed during an emergency. In order to classify the emotions, this paper used a bagging and boosting ensemble model. Using the Malayalam speech emotion dataset (MSED), the proposed support vector machine ensembles with Bagging with radial basis function kernel function were found to have an accuracy of 93.01%. Elicited and emergency speech from emergency calls are used to train support vector machine ensembles. For comparison of the performance of the model, we have used RAVDESS and Emo-DB. The accuracy obtained were 81.40% while using the RAVDESS dataset and 93.01% while using Emo-DB dataset which is high when compared to existing machine learning and ensemble model. The performance of boosting models was comparatively lesser than that of the Bagging model. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Emergency response support system (ERSS) Ensemble model Mel frequency cepstral co-efficient (MFCC) Speech emotion detection (SED) Support vector machine (SVM) ZeroCrossing rate (ZCR)

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Improving graph collaborative filtering with network motifs

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Neural Computing and Applications 2025年 1-20页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

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A network-based bioinformatic analysis for identifying potential repurposable drugs for obesity avoiding hepatic steatosis side-effect

A network-based bioinformatic analysis for identifying poten...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Brunetti, Martina Fiscon, Giulia Costanzo, Alessia Di Arca, Marcello Paci, Paola Sapienza University of Rome Department of Experimental Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science "a. Ruberti" National Research Council of Rome Department of Computer Control and Management Engineering "a. Ruberti" Rome Italy Sapienza University of Rome Policlinico Umberto I Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering "a. Ruberti" Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Obesity is a complex multifactorial disorder characterized by the excess accumulation of body fat that impairs human health due to the risk of developing other diseases, including cardiovascular and hepatic diseases, hypertension, diabetes, hyperlipidemia. Its spread has been progressively accelerating, resulting in an unprecedented epidemic with no significant signs of slowing down any time soon. Drug therapy via the proposal repurposing solutions can represent an actionable treatment strategy, even if the emergence of drug-induced adverse effects can affect the treatment of this pathology. In this study, we propose a network-based analysis to identify a list of drug candidates predicted to be repurposable for obesity that are unlikely to produce specific adverse side-effect, such as hepatic steatosis. © 2024 IEEE.

关键词： Nutrition

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CYBER-PHYSICAL SYSTEM IMPLEMENTATION FOR THE AR-ASSISTED AIT OF AEROSPACE COMPONENTS 34th

CYBER-PHYSICAL SYSTEM IMPLEMENTATION FOR THE AR-ASSISTED AIT...

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34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024

作者： Pesce, J. Carini, A. Marinacci, M. Boscia, M. Marzioli, P. Pasquali, M. Eugeni, M. Mecella, M. Piergentili, P. Gaudenzi, P. Department of Mechanical and Aerospace Engineering Sapienza University of Rome Via Eudossiana 18 Rome00184 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy

Industry 4.0 technologies are profoundly transforming the assembly, integration, and testing (AIT) processes of aerospace components, with augmented reality (AR) and cyber-physical systems (CPS) at the forefront of this evolution, marking a significant shift in traditional methodologies. This research explores the integration of CPS into the AIT processes of aerospace components, leveraging AR as a powerful tool to enhance efficiency, precision, and safety. To do so, the case of an aerospace system that showcases the practical implementation of the proposed CPS and AR framework in aerospace AIT is presented. Results show how the developed CPS can provide valuable assistance to the AIT operator, constituting a promising option to achieve significant improvements in assembly accuracy and overall process efficiency. The research also explores the implications of the proposed framework on the testing phase of aerospace components resorting to the CPS capability to facilitate real-time monitoring of performance metrics, ensuring the accomplishment of stringent quality standards. © 2024, International Council of the Aeronautical Sciences. All rights reserved.

关键词： Smart manufacturing

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Differential gene correlation analysis to investigate evolutionary divergence between Cardamine hirsuta and Arabidopsis thaliana

Differential gene correlation analysis to investigate evolut...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： D'Annunzio, Federica Fiscon, Giulia Bertolotti, Gaia Dello Ioio, Raffaele Paci, Paola Sapienza University of Rome Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science 'A. Ruberti' National Research Council of Rome Department of Computer Control and Management Engineering 'A. Ruberti' Rome Italy Sapienza University of Rome Department of Biology and Biotechnology 'Charles Darwin' Rome Italy Department of Computer Control and Management Engineering 'A. Ruberti' Sapienza University of Rome Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Dissecting the intricate regulatory dynamics between genes stands as a critical step towards the development of precise predictive models within biological systems. A highly effective strategy in this pursuit involves an examination of variations in correlation among gene pairs across different conditions trough differential gene correlation analysis. We anticipated that the application of this method to phylogenetically close plant species could reveal powerful insights on how genes' relationships have a fundamental role in evolutionary dynamics. Building upon this principle, our investigation focused primarily on conducting a comprehensive differential correlation analysis of the roots' orthologous genes of two closely related plant species: Cardamine hirsuta and Arabidopsis thaliana. By leveraging their shared phylogenetic proximity, we were able to draw comparisons that shed light on how evolutionary pressures might have shaped their gene regulatory networks. By integrating differential expression analysis, differential correlation analysis and functional enrichment analysis, we offer a robust framework for future studies aiming to unravel the complexities of adaptation and evolution across a wide range of biological systems. © 2024 IEEE.

关键词： Plant diseases

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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