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2021 Emerging technology in computing, Communication and Electronics, ETCCE 2021

作者： Yet, Ong Zu Rassem, Taha H. Rahman, Md Arafatur Rahman, M.M. Universiti Malaysia Pahang College of Computing and Applied Sciences Faculty of Computing Pekan Malaysia Bournemouth University Faculty of Science and Technology PooleBH12 5BB United Kingdom University of Wolverhampton School of Mathematics and Computer Science WolverhamptonWV1 1LY United Kingdom Universiti Malaysia Pahang College of Engineering Department of Mechanical Engineering Kuantan26300 Malaysia

ISBN: (纸本)9781665483643

Fine-grained classification is a challenging problem as one has to deal with a similar class of objects but with various types of variations. For more elaboration, they are almost similar and have subtle differences, and are confusing. In this study, aircraft will be the fine-grained object to be focused on. Aircraft which has almost similar shapes and patterns can be hardly recognized even for humans, especially those who haven not gone through any training. In recent years, a lot of proposed methods addressed to solve the difficulties in fine-grained problems by learning contrastive clues from an image. This study aims to increase the accuracy of the Attentive Pairwise Interaction Network (API-Net) by introducing data augmentation into the network structure. Some of the previous studies proved that data augmentation does help improve a network. So, this study is going to modify the API-Net with different data augmentation settings. In this study, various settings have been introduced to the API-Net. Several experiments had been done with a simple modification where a portion of the train dataset's images will randomly convert into greyscale images. These settings are, only brightness & contrast 0.2, only grayscale 0.3, only grayscale 0.5, brightness & contrast 0.2 with grayscale 0.3, and brightness & contrast 0.2 with grayscale 0.5. As a result, the proposed modification achieved with 92.74% with brightness & contrast 0.2, 92.80% on brightness & contrast 0.2 with grayscale 0.5, and 92.86% on brightness & contrast 0.2 with grayscale 0.3. While grayscale 0.3 alone achieve 93.25% and grayscale 0.5 alone achieve 93.46% compared with the original results which reached 92.77%. © 2021 IEEE.

关键词： Luminance

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： Computerized tomography

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Deep and handcrafted feature supported diabetic retinopathy ...

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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes;(i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Understanding Travellers’ Mode Choice Behaviour: The Association of Neural Network Activation Function

SSRN

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SSRN 2024年

作者： Mohd Ali, Nur Fahriza Abdul Majeed, Anwar P.P. Mohd Sadullah, Ahmad Farhan Mohd Razman, Mohd Azraai Nasir, Ahmad Fakhri Ab. Musa, Rabiu Muazu Abd Manan, Teh Sabariah Binti Faculty of Civil Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Chancellery Building Lebuh Persiaran Tun Khalil Yaakob Pahang Kuantan26300 Malaysia School of Engineering and Technology Sunway University Jalan Universiti Bandar Sunway Selangor Darul Ehsan 47500 Malaysia School of Civil Engineering Universiti Sains Malaysia Engineering Campus Penang Nibong Tebal14300 Malaysia Innovative Manufacturing Mechatronics and Sports Laboratory Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Center for Fundamental and Continuing Education Universiti Malaysia Terengganu Terengganu Kuala Nerus Malaysia

Machine learning technique has been widely used as an alternative for mode choice modelling. The urgency in working out to balance the modal split between public transport and private vehicles is due to congestion that is getting worst in Kuantan City. The uniqueness of Kuantan city as a getaway between east and west coast lead to more cars coming in and cause bumper-to-bumper happened especially during peak hours. A study on door-to-door journey that including walking distance from home to public transport stop, waiting time, and in-vehicle time is believed to be a resort in finding mitigation measures to curb with the congestion issues. Data collection is conducted in Kuantan City with 386 respondents involved. A technique called Classification Accuracy as performance indicator is applied to compare the best model of Neural Network to perform prediction for travel mode choice modelling. The performance of Neural Network model is compared by conducting some hyperparameter tuning including Rectified Linear Unit Activation (ReLU), Hyperbolic Tangent (Tanh), Logistic (Sigmoid Function), and Identified Function activation function. The best performance of Neural Network after tuning its activation function is depicted using ReLU activation function and was chosen to represent the travel mode choice modelling in Kuantan City. © 2024, The Authors. All rights reserved.

关键词： Chemical activation

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Energy Generation from Plastic Composites: A Systematic Review of Sustainable Practices and Technologies

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Circular Economy and Sustainability 2025年第2期5卷 1307-1343页

作者： Ametefe, Divine Senanu Ametefe, George Dzorgbenya John, Dah Aliu, Abdulmalik Adozuka Owen, Macaulay M. Shuib, Solehuddin Hamid, Aisha Wireless Communication Technology Group (WiCOT) College of Engineering School of Electrical Engineering Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia Department of Biotechnology College of Science Engineering and Technology Osun State University Osogbo Nigeria College of Computing Informatics and Mathematics Universiti Teknologi MARA (UiTM) Selangor Puncak Perdana 40150 Malaysia College of Built Environment Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia Institute of Energy Infrastructure IEI Universiti Tenaga Nasional UNITEN Jalan IKRAM-UNITEN Selangor Kajang 43000 Malaysia School of Mechanical Engineering College of Engineering Universiti Teknologi MARA UiTM Shah Alam 40450 Malaysia School of Chemistry and Environment Faculty of Applied Sciences Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia

Energy generation from plastic composites offers a viable solution to the dual challenges of plastic waste management and renewable energy production. This study explores the potential of plastic composites for energy generation through key waste-to-energy technologies, including incineration, pyrolysis, gasification, and anaerobic digestion. The study employs a Systematic Literature Review (SLR) methodology, following the PRISMA guidelines, and draws from two primary databases: ScienceDirect and Web of Science. Each method is assessed in terms of energy efficiency, environmental impact, economic feasibility, and scalability, based on studies published between 2020 and the second quarter of 2024. Pyrolysis and gasification emerge as particularly promising technologies due to their flexibility and higher energy recovery potential. Incineration, although widely used and well-established, presents challenges related to emissions, despite improvements in emissions control technologies. Anaerobic digestion, though limited in its application to conventional plastics, provides sustainability benefits when used with biodegradable materials. The analysis reveals regional disparities in research focus, the influence of policy frameworks, and the need for technological innovation to optimize these processes. Ultimately, the findings highlight the importance of integrating energy recovery from plastic waste within circular economy models to achieve sustainable waste management and renewable energy goals. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Circular Economy Plastic Recycling Renewable Energy Production Sustainable Waste Management Waste-to-Energy

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Modeling of Rotary Tool Adapted Electrochemical Machining of AISI 202

Modeling of Rotary Tool Adapted Electrochemical Machining of...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： T. Sekar Elango Natarajan Chun Kit Ang Lim Wei Hong Parvathy Rajendran Department of Mechanical Engineering Government College of Technology Coimbatore Tamilnadu India Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur Malaysia Department of Mechanical Engineering PSG Institute of Technology and Applied Research Coimbatore India School of Aerospace Engineering Universiti Sains Malaysia Engineering Campus Nibong Tebal Pulau Pinang Malaysia Faculty of Engineering & Computing First City University College Bandar Utama Petaling Jaya Selangor Malaysia

Concurrent achievement of better material removal rate (MRR), surface roughness (Ra) and Overcut (OC) are still considered as one of the challenging tasks in Electrochemical Machining (ECM), particularly on the metals having high toughness. The traditional ECM machine was modified with indigenously designed rotary tool adapter to increase the performance of machining. Meanwhile, experimental design was done to conduct the experiments with different machining conditions. The responses of machining for different set of voltage, tool feed rate, electrolyte discharge rate and, rotary speed using 15% NaCl aqueous solution were observed and measured. Response Surface Methodology (RSM) model was then developed and validated through confirmatory experiments. The best response of rotary adopted machining under same operating conditions are: material removal rate of 361.410 mm3/min, surface roughness of 1.62 μm and overcut of 4.9 % at 18 V, 0.54 mm/min, 12 lit/min and 100 rpm. The results are 11.96% lower in surface roughness, 21.36% lower in overcut when compared to non-rotary tool.

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A Fast Exponential Analysis and Variable Projection Based Method for Linear Array Synthesis

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Sengupta, Ramonika Knaepkens, Ferre Cuyt, Annie Prinsloo, David S. Schäfer, Thomas Smolders, A. Bart Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Faculty of Applied Engineering University of Antwerp Groenenborgerlaan 171 Antwerp2020 Belgium Computing Science and Mathematics Division University of Stirling Scotland StirlingFK9 4LA United Kingdom ASTRON Dwindloo Oude Hoogeveensedjik 4 7991 PD Netherlands SATCUBE Nellickevägen 22 Gothenburg41263 Sweden

This paper presents a fast exponential analysis and variable projection method to reduce the number of elements in linear phased arrays. The numerical examples given in this work demonstrate a reduction of approximately 20% or 10% in the number of elements, with only a slight decrease in directivity of 0.63 dBi observed for a scan range of [−45◦, 45◦]. Compared to the combined matrix pencil and Tabu search algorithm, the proposed technique converges in fewer iterations and exhibits ≈ 5 dB reduction in maximum side lobe level. © 2002-2011 IEEE All rights reserved.

关键词： Antenna phased arrays

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Detecting Information on the Spread of Dengue on Twitter Using Articial Neural Networks

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Computers, Materials & Continua 2021年第4期67卷 1317-1332页

作者： Samina Amin M.Irfan Uddin M.Ali Zeb Ala Abdulsalam Alarood Marwan Mahmoud Monagi H.Alkinani Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan College of Computer Science and Engineering University of JeddahJeddah21959Saudi Arabia Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Articial Intelligence College of Computer Sciences and EngineeringUniversity of JeddahJeddahSaudi Arabi

Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning *** analytical and statistical models are available to infer a variety of user sentiments in posts on social *** amount of data generated by social media platforms,such as Twitter,that can be used to track diseases is increasing *** paper proposes a method for the classication of tweets related to the outbreak of dengue using machine learning *** articial neural network(ANN)-based method is developed using Global Vector(GloVe)embedding to use the data in tweets for the automatic and efcient identication and classication of *** proposed method classies tweets related to the outbreak of dengue into positives and *** were conducted to assess the proposed ANN model based on performance evaluation matrices(confusion matrices).The results show that the GloVe vectors can efciently capture a sufcient amount of information for the classier to accurately identify and classify tweets as relevant or irrelevant to dengue *** proposed method can help healthcare professionals and researchers track and analyze epidemic outbreaks through social media in real time.

关键词： Articial neural network classication social media GloVe social networking sites

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Clustering Medical Transcriptions Using K -Means

Clustering Medical Transcriptions Using K -Means

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Intelligent computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Said Salloum Dina Tahat Khalaf Tahat Raghad Alfaisal Ayham Salloum School of Science Engineering and Environment University of Salford Manchester UK Applied sociology department Al Ain University UAE Media & Creative Industries United Arab Emirates University and Yarmouk University Jordan Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia College of Medicine University of Sharjah Sharjah UAE

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

The clustering of medical transcriptions is an essential task for the categorization and summarization of large volumes of medical records. This paper explores the efficacy of k-means clustering, a well-known unsupervised machine learning algorithm, to discern patterns and segregate medical transcriptions into distinct clusters. We processed a dataset comprising various medical reports, systematically cleaning and preparing the text for analysis. By employing a Term Frequency-Inverse Document Frequency (TF-IDF) approach, we converted the textual data into a vectorized format amenable to machine learning methods. Subsequent dimensionality reduction through Principal Component Analysis (PCA) facilitated the visualization and interpretation of the high-dimensional data in two-dimensional space. The k-means algorithm was then applied, revealing five distinct clusters. Each cluster was characterized by examining the prevalence of key terms, uncovering thematic consistencies that may correspond to particular medical procedures or specialties. The resulting clusters demonstrate the algorithm's potential to automatically categorize medical documentation in a way that mirrors clinical relevance, thereby providing a foundation for improved information management systems in healthcare settings.

关键词： Dimensionality reduction Machine learning algorithms Clustering algorithms Data visualization Medical services Documentation Frequency conversion Information management Mirrors Principal component analysis

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Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

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Computer Systems Science & Engineering 2023年第9期46卷 2947-2961页

作者： IbrahimM.Alwayle Badriyya B.Al-onazi Mohamed K.Nour Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Amal S.Mehanna Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Online reviews regarding purchasing services or products offered are the main source of users’*** gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or *** practice is called review *** the last few years,various techniques have been recommended to solve the problem of spam *** spam detection study focuses on English reviews,with a lesser interest in other *** review detection in Arabic online sources is an innovative topic despite the vast amount of data ***,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion *** presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and ***,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible ***,unigram and bigram feature extractors are *** SGRU model is employed in this study to identify and classify Arabic spam *** the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU *** experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC *** extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

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