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International Multidisciplinary Conference on computer and Energy science (SpliTech)

作者： Marija Habijan Irena Galić Aleksandra Pizurica Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia TELIN-GAIM Faculty of Engineering and Architecture Ghent University Ghent Belgium

Cardiac auscultation is the process of listening to the sounds of the heart with a stethoscope, which can provide important diagnostic information about a patient’s heart function. It is a key component of a physical examination and can help doctors identify potential heart problems such as murmurs, arrhythmias, and valve disorders. Deep learning-based cardiac auscultation is of great interest to medical professionals as it can reduce the burden of manual auscultation by automatically detecting abnormal heartbeats. However, automatic cardiac auscultation is a challenging task due to the need for dependable and precise results, as well as the interference of background noise in heart sounds. In this work, we propose a heart sound classification method that could potentially accelerate automated cardiac auscultation. The method is based on Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN). We use heart sounds from the dataset Classifying Heart Sounds Challenge and extract input features using Mel Frequency Cepstral Coefficients (MFCCs). Further, we examine the use of a simple data augmentation technique and show that adding noise, stretching, and shifting of heart sound signals significantly improves the classification accuracy of the normal, murmur, and artifact heart sounds.

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Improving the Performance of Job Backfilling Based on Job's Weight and Back Propagation Neural Network

Improving the Performance of Job Backfilling Based on Job's ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hazem Alnajjar Nadia Al-Rousan Syed Sahal Nazli Alhady Syed Hassan Junita Mohamad-Saleh Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of computer engineering College of Engineering AlBalqa Applied University Salt Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University Amman Jordan Colleage of Engineering and Architecture School of Electrical & Electronic Engineering Engineering Branch Campus Universiti Sains Malaysia Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Nowadays, the grid computing environment faces many difficulties executing new jobs, especially jobs requiring large resource requirements and long execution times. This motivates researchers and scholars to find cheap and fast methods to improve the efficiency of grid environments. One of the cheap and fast methods is to implement job scheduling algorithms based on cheap and fast techniques. This paper proposes a new job ranking backfilling algorithm based on the job's weight and back propagation neural network. To define the weight of the job, first, the proposed model will use a clustering algorithm to cluster the job's dataset into groups, and then the groups will be ranked using an experimental ranking equation. A discrete event simulator is used to validate the proposed algorithm's capability and robustness. The average results revealed that the new algorithm outperforms previous algorithms. The improvement of the studied metrics is between 1.19 and 6.30, respectively. The results proved that the proposed model is efficient and can be used with low overhead in a real environment.

关键词： Backpropagation Measurement Scheduling algorithms Computational modeling Neural networks Clustering algorithms Grid computing Mathematical models Robustness Faces

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A survey and recent advances in black widow optimization: variants and applications

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Neural Computing and Applications 2024年 1-21页

作者： Shehab, Mohammad Shambour, Mohd Khaled Yousef Abu Hashem, Muhannad A. Al Hamad, Husam Ahmad Shannaq, Fatima Mizher, Manar Jaradat, Ghaith Sh. Daoud, Mohammad Abualigah, Laith College of Computer Sciences and Informatics Amman Arab University Amman11953 Jordan The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia College of Engineering Al Ain University Abu Dhabi United Arab Emirates Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied science research center Applied science private university Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their efforts and abilities on adapting the optimization algorithms for solving various problems. This review introduces one of the recent nature-inspired optimization algorithms, called black widow optimization (BWO). This review highlights the main procedures of BWO, its strengths and weaknesses, the growth of BWO, the variants of BWO (i.e., binary, modification, and hybridization), and the efficiency of BWO in different fields and applications (i.e., feature selection, medical, image processing, engineering, IoT, power and energy, and networks). As a result, the review seeks to assist researchers who are interested in using the BWO to address their challenges. For instance, it could help manage large pilgrim crowds, enabling smoother ritual performances and overcoming potential difficulties they encounter. Also, it suggests new possible ways of enhancing the efficiency of the basic BWO. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Anomaly Localization at High-Density Crowd Using Motion Shape Image (MSI) 1st

Anomaly Localization at High-Density Crowd Using Motion Shap...

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1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Farooq, M.U. Mohamad Saad, M.N. Daud Khan, S. Saleh, Y. Universiti Teknologi PETRONAS Tronoh Malaysia Department of Computer Science National University of Technology Islamabad Pakistan Electrical Engineering Department Faculty of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia

ISBN: (纸本)9789811621826

Anomaly localization plays a critical role if a disaster occurs in a high-density crowd to efficiently rescue the crowd from the right location. This paper enriches anomaly localization by introducing new localization features, in addition to the ‘only’ localization feature of source/start point detection existing literature offers. New features introduced include crowd density estimation in localized regions and direction/angle of a localized region. A motion shape image (MSI) based approach is introduced for localization and features detection and experimentation is performed on benchmark and our proposed high-density crowd datasets. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Deep Learning Approach for Divergence Behavior Detection at High Density Crowd 1st

Deep Learning Approach for Divergence Behavior Detection at ...

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1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Farooq, M.U. Mohamad Saad, M.N. Saleh, Y. Daud Khan, S. Universiti Teknologi PETRONAS Tronoh Malaysia Electrical Engineering Department Faculty of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science National University of Technology Islamabad Pakistan

ISBN: (纸本)9789811621826

At high-density crowd gatherings, people naturally escape from the region where any unexpected event happens. Escape in high-density crowds appears as a divergence pattern in the scene and timely detecting divergence patterns can save many human lives. In this paper, we propose to physically capture crowd normal and divergence motion patterns (or motion shapes) in form of images and train a shallow convolution neural network (CNN) on motion shape images for divergence behavior detection. Crowd motion pattern shape is obtained by extracting ridges of Lagrangian Coherent Structure (LCS) from the Finite-Time Lyapunov Exponent (FTLE) field and convert ridges into the grey-scale image. We also propose a divergence localization algorithm to pinpoint anomaly location(s). Experimentation is carried out on synthetic crowd datasets simulating normal and divergence behaviors at the high-density crowd. Comparison with state-of-the-art methods shows our method can obtain better accuracy for both divergence behavior detection and localization problems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion estimation

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Inspecting Vanet from Various Critical Angles – a Systematic Review

SSRN

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SSRN 2023年

作者： Karabulut, Muhammet Ali Shah, A.F.M. Shahen Ilhan, Haci Pathan, Al-Sakib Khan Atiquzzaman, And Mohammed Department of Electrical Electronics Engineering Faculty of Engineering and Architecture Kafkas University Kars Turkey Department of Electronics and Communications Engineering Faculty of Electrical & Electronics Engineering Yildiz Technical University Istanbul Turkey Computer Science and Engineering Department School of Science & Engineering United International University Dhaka Bangladesh Department of Computer Science School of Computer Science University of Oklahoma Norman United States

Due to its strategic significance in the area of smart transportation to facilitate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, Vehicular Ad hoc Networks (VANETs) have grown very popular in recent years. The rapid increase in the number of vehicles on the road has also led to the development of heterogeneous, large-scale, and highly dynamic VANETs, which introduce difficulty to fulfill the stringent requirements, which include low latency, high mobility, top security, and massive connectivity of the 5G network. Research works in VANETs mainly focus on message transmission within strict delay requirements based on different applications, data privacy and security. In this respect, a number of studies have been carried out by the researchers that propose models and solutions linked to the enhancement of VANET from several angles, including applications, quality of service (QoS), security, physical layer fading, artificial intelligence (AI) techniques, Medium Access Control (MAC), and routing protocols. These factors serve as the driving force for this study's thorough examination of VANETs, which includes information on specific applications, QoS, channel fading, MAC protocols, channel access mechanisms, routing protocols, security, and difficulties. None of the surveys in existence today address all critical aspects of VANET in a single survey. In this paper, a complete taxonomy of VANETs has been provided based on various issues. First, an overview of VANET is presented with different applications. Then, QoS in VANETs and different proposed MAC protocols are discussed. Channel fading and access mechanisms are presented. After that, routing protocols, security, and clustering in VANETs are provided. AI approaches proposed in VANET are also summarized. Finally, a discussion of future research direction for all aspects is presented. This article might be used as a guide or a point of reference while designing and creating applications, n

关键词： Vehicular ad hoc networks

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Integrating Structural Information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (Case Study: Protein-Peptide Region Residue-Level Interaction) Using Structural Information

Integrating Structural Information: Comparing Classification...

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International CSI computer Conference, CSICC

作者： Shima Shafiee Abdolhossein Fathi Faezeh Safari Department of Computer Engineering and Information Technology Razi University Kermanshah Iran Faculty of Eng. Architecture and Info. Tech. School of Electrical Eng. and Computer Science University of Queensland Brisbane QLD Australia

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method.

关键词： Proteins Support vector machines Deep learning Peptides Predictive models Sensitivity and specificity Prediction algorithms Robustness Drug discovery Testing

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Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

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Bone Research 2024年第1期12卷 1-19页

作者： Xiwei Fan Antonia Rujia Sun Reuben S.E.Young Isaac O.Afara Brett R.Hamilton Louis Jun Ye Ong Ross Crawford Indira Prasadam Centre for Biomedical Technologies Queensland University of TechnologyBrisbaneQLDAustralia School of Mechanical Medical&Process EngineeringQueensland University of TechnologyBrisbaneQLDAustralia Central Analytical Research Facility Queensland University of TechnologyBrisbaneQLDAustralia Molecular Horizons University of WollongongWollongongNSWAustralia Department of Technical Physics University of Eastern FinlandKuopioFinland School of Electrical Engineering and Computer Science Faculty of EngineeringArchitecture and Information TechnologyUniversity of QueenslandBrisbaneQLDAustralia Centre for Microscopy and Microanalysis University of QueenslandBrisbaneQLDAustralia The Prince Charles Hospital BrisbaneQLDAustralia

Osteoarthritis(OA)is a debilitating degenerative disease affecting multiple joint tissues,including cartilage,bone,synovium,and adipose *** presents diverse clinical phenotypes and distinct molecular endotypes,including inflammatory,metabolic,mechanical,genetic,and synovial ***,innovative technologies are needed to support the development of effective diagnostic and precision therapeutic *** analysis of bulk OA tissue extracts has limitations due to technical constraints,causing challenges in the differentiation between various physiological and pathological phenotypes in joint *** issue has led to standardization difficulties and hindered the success of clinical *** insights into the spatial variations of the cellular and molecular structures in OA tissues,encompassing DNA,RNA,metabolites,and proteins,as well as their chemical properties,elemental composition,and mechanical attributes,can contribute to a more comprehensive understanding of the disease *** resolved biology enables biologists to investigate cells within the context of their tissue microenvironment,providing a more holistic view of cellular *** advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various-omics lenses,such as genomics,transcriptomics,proteomics,and metabolomics,with spatial *** fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial ***,advanced imaging techniques,including high-resolution microscopy,hyperspectral imaging,and mass spectrometry imaging,enable the visualization and analysis of the spatial distribution of biomolecules,cells,and *** these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease *** review summarizes the rec

关键词： insight Spatial enable

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Investigating Different Approaches on Online Laboratory Practices

Investigating Different Approaches on Online Laboratory Prac...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Inmaculada Tomeo-Reyes Zhuoyu Chen Chamith Wijenayake Arash Khatamianfar School of Electrical Engineering and Telecommunications Faculty of Engineering University of New South Wales Sydney Australia School of Electrical Engineering and Computer Science Faculty of Engineering Architecture and Information Technology University of Queensland Brisbane Australia

Laboratories are an integral part of engineering education. In this post-pandemic era, there is an increased emphasis on distance learning and hybrid approaches, which demands novel teaching and learning approaches to maintain the hands-on aspects of laboratory work. In this paper, different approaches for online laboratories are explored by encapsulating recent literature as well as the authors’ own experience in electrical engineering courses. Specific surveys are used to capture both student and staff perspectives and feedback on online learning approaches for laboratory classes. Recommendations are made based on student and staff experience with these approaches.

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Design of a Mobile Data Collection Robot for Learning-based Localization and Autonomous Driving

Design of a Mobile Data Collection Robot for Learning-based ...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Baykar, Ali Omer Lambrecht, Jens Kural, Ayhan Uygur, Selcuk Eray Yildiz, Ahmet Istanbul Technical University Department of Mechanical Engineering Istanbul Turkey Turkish-German University Department of Mechanical Engineering Istanbul Turkey Berlin Institute of Technology Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Germany Turkish-German University Istanbul Turkey Macromedia University of Applied Sciences Hamburg Germany

ISBN: (纸本)9798350306590

This study introduces a mobile robot capable of collecting position and corresponding visual data seamlessly from both indoor and outdoor settings within the same sequence. The mobile robot has been specifically designed to navigate obstacles such as stairs and steps during transitions between indoor and outdoor environments. To accomplish this, the robot incorporates differential driving dynamics and is equipped with essential sensors including two stereo cameras, LIDAR, IMU, and GNSS. The entire system operates on the Robot Operating System (ROS). Consequently, it becomes possible to create a comprehensive dataset that encompasses not only the routes traversed by mobile vehicles but also includes all vehicle and pedestrian roads, as well as indoor spaces, found within a campus environment. © 2023 IEEE.

关键词： Mobile robots

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