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Comparative analysis of resource-efficient YOLO models for rapid and accurate recognition of intestinal parasitic eggs in stool microscopy

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Intelligence-Based Medicine 2025年 11卷 100212-100212页

作者： Venkatesan, Kotteswaran Muthulakshmi, Muthunayagam Prasanalakshmi, Balaji Karthickeien, Elangovan Pabbisetty, Harshini Syarifah Bahiyah, Rahayu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai 601103 India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai 601103 India Department of Computer Science King Khalid University Abha 61421 Saudi Arabia Faculty of Defense Science and Technology National Defence University Malaysia Kuala Lumpur 57000 Malaysia Cyber Security and Digital Industrial Revolution Centre National Defence University Malaysia Kuala Lumpur 57000 Malaysia

Faster and reliable recognition of the specific species of intestinal parasite eggs in stool microscopic images is required for targeted and quick intervention of soil transmitted helminths (STH) disease. The main objective of the proposed work is to identify the effective light weight basic yolo models among the recent compact yolo variants such as yolov5n, yolov5s, yolov7, yolov7-tiny, yolov8n, yolov8s, yolov10n and yolov10s, that could assist in rapid and accurate recognition of 11 parasite species egg. The real time performance of the compact yolo models have been analyzed in embedded platforms: Raspberry Pi 4, Intel upSquared with the Neural Compute Stick 2 and Jetson Nano. Finally, Gradient-weighted class activation mapping (Grad-CAM) has been used as an explainable AI (XAI) visualization method to elucidate the egg detection performance of the proposed models. Yolov7-tiny achieved the overall highest mean Average Precision (mAP) score of 98.7 %. On contrary, yolov10n yielded highest recall and F1 score of 100 % and 98.6 %. On other hand, yolov8n took least inference time with processing speed of 55 frames per second with Jetson Nano. Notably, the proposed framework demonstrates superior performance in detection of egg classes - Enterobius vermicularis, Hookworm egg, Opisthorchis viverrine, Trichuris trichiura, and Taenia spp. which is a significant outcome of the current research. Further, Grad-CAM depicts the discriminative power of unique features in parasite eggs. Thus, this study demonstrates the effectiveness, compactness and inference latency analysis of basic compact yolo variants in learning the specific patterns, texture and shape of parasitic egg species, thereby potentially enhancing the diagnostic accuracy of STH. © 2025 The Authors

关键词： Intel up squared board Intelligent parasitic egg detection Jetson nano Raspberry pi 4 Soil transmitted helminths YOLOv7-tiny

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Deep Hand Gesture Recognition: A Wavelet Scattering Alternative to Convolutional Networks

Deep Hand Gesture Recognition: A Wavelet Scattering Alternat...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Adel Al-Jumaily Rami N. Khushaba Faculty of Engineering University of Technology Brunei Mukim Gadong Brunei ENSTA Bretagne French State Graduate Brest France School of Computing Mathematics and Engineering Charles Sturt University Wagga Wagga NSW Australia School of Science Edith Cowan University Joondalup WA Australia Australian Centre for Field Robotics (ACFR) University of Sydney Chippendale NSW Australia

Hand gesture recognition (HGR) is crucial for improving human-computer interaction, aiding people with disabilities, and enhancing industrial efficiency. Radars are a popular choice for HGR, as they can detect hand gestures in various lighting conditions, through obstructions, with low latency, and without line-of-sight. Deep Convolutional Neural Networks (DCNN) are commonly used to analyze radar signals and recognize complex hand gestures. However, training DCNN models requires significant computational resources, making real-time applications challenging. This study proposes using Wavelet Scattering Transform (WST) as a feature extractor to replace DCNN, while relying on lightweight traditional classifiers for identifying the class of hand movement. WST is a non-linear signal representation that preserves high levels of discriminability while maintaining stability under time-warping deformations. To compare DCNN against WST, the study used a publicly available database of ultra-wideband (UWB) impulse radar gestures, collected from eight participants performing twelve hand gestures. The results showed that WST can achieve an average accuracy of 95% across all subjects, making it a reliable, computationally efficient, and accurate alternative to DCNN. This is the first research demonstrating the effectiveness of WST against DCNN for radar HGR applications (to the best of the authors’ knowledge).

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A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

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Computers, Materials & Continua 2022年第5期71卷 3533-3556页

作者： Siti Julia Rosli Hasliza A Rahim Khairul Najmy Abdul Rani Ruzelita Ngadiran Wan Azani Mustafa Muzammil Jusoh Mohd Najib Mohd Yasin Thennarasan Sabapathy Mohamedfareq Abdulmalek Wan Suryani Firuz Wan Ariffin Ahmed Alkhayyat Advanced Communication Engineering Centre of Excellence(ACE)Universiti Malaysia Perlis(UniMAP)01000 KangarPerlisMalaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis(UniMAP)02600 ArauPerlisMalaysia Advanced Computing(AdvCOMP) Centre of ExcellenceUniversiti Malaysia Perlis(UniMAP)02600 ArauPerlisMalaysia Faculty of Electrical Engineering Technology Universiti Malaysia Perlis(UniMAP)02600 ArauPerlisMalaysia Faculty of Engineering and Information Science University of Wollongong in DubaiDubai 20183United Arab Emirates Department of Computer Technical Engineering College of Technical Engineeringthe Islamic University54001 NajafIraq

The essential purpose of radar is to detect a target of interest and provide information concerning the target’s location,motion,size,and other *** knowledge about the pulse trains’properties shows that a class of signals is mainly well suited to digital processing of increasing practical importance.A low autocorrelation binary sequence(LABS)is a complex combinatorial *** main problems of LABS are low Merit Factor(MF)and shorter length ***,the maximum possible MF equals 12.3248 as infinity length is unable to be ***,this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm(HMSCACSA)using Inverse Filtering(IF)and clipping method to achieve better *** proposed algorithms,LABS-IF and HMSCACSA-IF,achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237,respectively,where the optimal solutions belong to the skew-symmetric *** MF outperformed up to 24.335%and 2.708%against the state-of-the-art LABS heuristic algorithm,xLastovka,and Golay,*** results indicated that the proposed algorithm’s simulation had quality solutions in terms of fast convergence curve with better optimal means,and standard deviation.

关键词： Merit factor autocorrelation skew-symmetric sequences combinatorial optimization sine cosine algorithm cuckoo search algorithm radar system wearable antenna antenna and propagation

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Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks

arXiv

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arXiv 2023年

作者： Horry, Michael James Chakraborty, Subrata Pradhan, Biswajeet Paul, Manoranjan Zhu, Jing Barua, Prabal Datta Rajendra Acharya, U. Chen, Fang Zhou, Jianlong School of Civil and Environmental Engineering Faculty of Engineering and IT University of Technology Sydney UltimoNSW2007 Australia IBM Australia Limited SydneyNSW2000 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Earth Observation Centre Institute of Climate Change Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia Department of Radiology Westmead Hospital WestmeadNSW2145 Australia Cogninet Brain Team Cogninet Australia SydneyNSW2010 Australia Faculty of Business Education Law & Arts University of Southern Queensland ToowoombaQLD4350 Australia School of Mathematics Physics and Computing University of Southern Queensland SpringfieldQLD4300 Australia Data Science Institute University of Technology Sydney UltimoNSW2007 Australia

Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray provides an inexpensive imaging mode for lung cancer diagnosis, especially for remote populations with limited healthcare access. Unfortunately, suspicious nodules are difficult to distinguish from pulmonary vascular and bone structures using Chest X-ray. Computer vision algorithms have previously been proposed to assist human radiologists in this task, however, the leading studies use down-sampled images and highly parameterized, computationally expensive methods with unproven generalization capability to unseen datasets. Instead, this study localizes lung nodules from chest X-ray images using highly efficient encoder-decoder neural networks that have been crafted to process full resolution input images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested internally using the Japanese Society of Radiological technology lung nodule dataset. The trained networks are used to localize lung nodules from an independent external chest x-ray dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization from chest x-ray images. We find that nodule localization is influenced by subtlety, with more subtle nodules being detected in earlier training epochs. Therefore, we propose a novel self-ensemble model from three consecutive epochs centered on the conventional validation optimum. This ensemble achieved a sensitivity of 85% in 10-fold internal testing with false positives of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6 following morphological false positive reduction. This result is comparable to more computationally complex systems based on linear and

关键词： Image resolution

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Indirect solar drying of Saharan agricultural products using an intelligent system

Indirect solar drying of Saharan agricultural products using...

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Energy Transition and Security (ICETS), International Conference on

作者： Bensaid Achour Hadef Khawla Zahra Boulal Ahmed Dahbi Abdeldjalil Khelafi Mostefa Laboratory of Saharan Natural Resources Faculty of Science and Technology University of Ahmed Draia Adrar Algeria Centre de Développement des Energies Renouvelables (CDER) Unité de Recherche en Energies Renouvelables en Milieu Saharien(URERMS) Adrar Algeria Laboratory of Sustainable Development and Computing (L.D.D.I) University of Adrar Adrar Algeria

Solar drying of meat products has been practiced for thousands of years in many parts of the world. The advantage of this processing technique is that it extends the shelf life of meat products (conservation), making them safe to eat. Today, the methods of salting, smoking and drying are practically practiced just in time for occasion religious *** drying has a number of advantages, the most important of which is that it is less costly, depending as it does on sunshine, unlike freezing. To this end, our aim is to develop a prototype of an improved indirect solar dryer. This involves precise adjustments with a few modifications to existing models, using an automatic tracking sensor to increase the prototype’s efficiency in order to reduce drying time and maintain the nutritional quality of the dried material. The impact of solar drying on the quality of meat products is studied through microbiological and biochemical analyses.

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Solving the multiplication problem of a large language model system using a graph-based method

arXiv

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arXiv 2023年

作者： Tuncer, Turker Dogan, Sengul Baygin, Mehmet Barua, Prabal Datta Hafeez-Baig, Abdul Tan, Ru-San Chakraborty, Subrata Acharya, U. Rajendra Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey Department of Computer Engineering Faculty of Engineering and Architecture Erzurum Technical University Erzurum Turkey University of Southern Queensland Australia School of Management and Enterprise University of Southern Queensland ToowoombaQLD Australia Department of Cardiology National Heart Centre Singapore Singapore169609 Singapore Duke-NUS Medical School Singapore169857 Singapore School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

The generative pre-trained transformer (GPT)-based chatbot software ChatGPT possesses excellent natural language processing capabilities but is inadequate for solving arithmetic problems, especially multiplication. Its GPT structure uses a computational graph for multiplication, which has limited accuracy beyond simple multiplication operations. We developed a graph-based multiplication algorithm that emulated human-like numerical operations by incorporating a ×10k operator, where k represents the maximum power to base 10 of the larger of two input numbers. Our proposed algorithm attained 100% accuracy for 1,000,000 large number multiplication tasks, effectively solving the multiplication challenge of GPT-based and other large language models. Our work highlights the importance of blending simple human insights into the design of artificial intelligence algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Blending

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DAFNI: A computational platform to support infrastructure systems research

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2023年第3期176卷 108-116页

作者： Matthews, Brian Hall, Jim Batty, Michael Blainey, Simon Cassidy, Nigel Choudhary, Ruchi Coca, Daniel Hallett, Stephen Harou, Julien J James, Phil Lomax, Nik Oliver, Peter Sivakumar, Aruna Tryfonas, Theodoros Varga, Liz Scientific Computing Department Science and Technology Facilities Council Didcot United Kingdom School of Geography and the Environment University of Oxford Oxford United Kingdom Centre for Advanced Spatial Analysis University College London London United Kingdom Transportation Research Group Faculty of Engineering and Physical Sciences University of Southampton Southampton United Kingdom Department of Civil Engineering University of Birmingham Birmingham United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Centre for Environmental and Agricultural Informatics Cranfield University Cranfield United Kingdom Department of Mechanical Aerospace and Civil Engineering University of Manchester Manchester United Kingdom School of Engineering Newcastle University Newcastle United Kingdom School of Geography University of Leeds Leeds United Kingdom Department of Civil and Environmental Engineering Imperial College London London United Kingdom Department of Civil Engineering University of Bristol Bristol United Kingdom Department of Civil Environmental and Geomatic Engineering University College London London United Kingdom

Research into the engineering of infrastructure systems is increasingly data intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a 'multi-systems' approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users' models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project. © 2023 Emerald Publishing Limited: All rights reserved.

关键词： Urban growth

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Application of GIS in Public Health Practice: A Consortium’s Approach to Tackling Travel Delays in Obstetric Emergencies in Urban Areas 12

Application of GIS in Public Health Practice: A Consortium’...

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12th International Conference on Geographic Information science, GIscience 2023

作者： Wang, Jia Osayande, Itohan Macharia, Peter M. Makanga, Prestige Tatenda Wong, Kerry L.M. Olubodun, Tope Gwacham-Anisiobi, Uchenna Ogunyemi, Olakunmi Olaniran, Abimbola Abejirinde, Ibukun-Oluwa O. Beňová, Lenka Afolabi, Bosede B. Banke-Thomas, Aduragbemi School of Computing & Mathematical Sciences University of Greenwich London United Kingdom School of Human Sciences University of Greenwich London United Kingdom Department of Public Health Institute of Tropical Medicine Antwerp Belgium Population & Health Impact Surveillance Group Kenya Medical Research Institute-Wellcome Trust Research Programme Nairobi Kenya Centre for Health Informatics Computing and Statistics Lancaster Medical School Lancaster University United Kingdom Surveying and Geomatics Department Faculty of Science and Technology Midlands State University Gweru Zimbabwe Climate and Health Division Centre for Sexual Health and HIV/AIDS Research Zimbabwe Faculty of Epidemiology and Population Health London School of Hygiene and Tropical Medicine United Kingdom Department of Community Medicine and Primary Care Federal Medical Centre Abeokuta Ogun Abeokuta Nigeria Nuffield Department of Population Health University of Oxford United Kingdom Lagos State Ministry of Health Nigeria Royal Tropical Institute Amsterdam Netherlands Dalla Lana School of Public Health University of Toronto Canada Women’s College Hospital Institute for Health System Solutions and Virtual Care Toronto Canada Department of Obstetrics and Gynaecology College of Medicine University of Lagos Nigeria Maternal and Reproductive Health Research Collective Lagos Nigeria

ISBN: (纸本)9783959772884

Geographic Information System (GIS) has become an effective and reliable tool for researchers, policymakers, and decision-makers to map health outcomes and inform targeted planning, evaluation, and monitoring. With the advent of big data-enabled GIS, researchers can now identify disparities and spatial inequalities in health at more granular levels, enabling them to provide more accurate and robust services and products for healthcare. This paper aims to showcase the progress of the On Tackling In-transit Delays for Mothers in Emergency (OnTIME) project, which is a unique collaborative effort between academia, policymakers, and industrial partners. The paper demonstrates how the limitations of traditional spatial accessibility models and data gaps have been overcome by combining GIS and big data to map the geographic accessibility and coverage of health facilities capable of providing emergency obstetric care (EmOC) in conurbations in Africa. The OnTIME project employs various GIS technologies and concepts, such as big spatial data, spatial databases, and public participation geographic information systems (PPGIS). We provide an overview of these concepts in relation to the OnTIME project to demonstrate the application of GIS in public health practice. © Jia Wang, Itohan Osayande, Peter M. Macharia, Prestige Tatenda Makanga, Kerry L. M. Wong, Tope Olubodun, Uchenna Gwacham-Anisiobi, Olakunmi Ogunyemi, Abimbola Olaniran, Ibukun-Oluwa O. Abejirinde, Lenka Beňová, Bosede B. Afolabi, and Aduragbemi Banke-Thomas.

关键词： Geographic information systems

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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Engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, scienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study 7

Automated Detection of Brain Abnormality using Deep-Learning...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Nam, Yunyoung Rauf, Hafiz Tayyab Rajinikanth, Venkatesan Lawal, Isah A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Soonchunhyang University Department of Computer Science and Engineering Asan-si Korea Republic of Staffordshire University Centre for Smart Systems Ai and Cybersecurity Stoke-on-Trent United Kingdom St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

Brain is the vital organ in human physiology;which is conscientious for sensory signal handling and judgment making. The irregularity in brain severely influence entire decision making procedure and the unrecognized and untreated defect will lead to various harsh conditions. This research aims to implement pre-trained Deep-Learning-Scheme (DLS) to classify the brain MRI slices using a multi-class classifier. In this research, the brain MRI slices with classes;normal, stroke, Low-Grade-Glioma (LGG) and High-Grade-Glioma (HGG) are considered for the experimental study. In this work every test picture is resized into 224x224x3 pixels and these imagery are then considered to validate the performance of DLS, such as VGG16, VGG19 and ResNet50 using different classifiers. The attained classification accuracy of every DLS with classifiers, SoftMax, SVM-RBF and SVM-Cubic are presented and discussed. © 2021 IEEE.

关键词： Magnetic resonance imaging

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