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Event-based White Blood Cell Classification from Nailfold Capillaries

IEIE Transactions on Smart Processing and Computing 2022年第4期11卷 270-275页

作者： Hariyani, Yuli Sun Park, Cheolsoo Department of Computer Engineering Kwangwoon University Seoul Korea Republic of School of Applied Science Telkom University Bandung Indonesia

Detecting absorption gaps in a nailfold capillary can be used to quantify an estimation of white blood cells (WBCs). Previously, the absorption gaps in a nailfold capillary were usually measured using a standard camera on a fingernail. However, difficulties arise due to low visibility of the gap, the small size of the capillaries, the high speed of WBC movement, and the lack of contrast between the capillary and its environment/background. To address these issues, an event-based WBC image is utilized as input data to detect WBC existence in the nailfold. Specifically, we utilize a dynamic vision sensor (DVS) camera, which can detect a change in luminance on a pixel basis and can produce a stream of asynchronous event output at a microsecond temporal resolution. With the event-based WBC dataset, we conduct a classification task using three different machine learning algorithms: k-nearest neighbors, the decision tree, and random forest. The best result is from random forest with 75.51% accuracy. Based on our evaluation, event-based WBC classification is a promising new approach to detecting WBC presence in nailfold capillaries. © 2022 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Classification (of information)

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Application of Machine Learning in Mental Diseases Diagnosis 25

Application of Machine Learning in Mental Diseases Diagnosis

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alsawi, Rahaf Alazaidah, Raed Samara, Ghassan Alzoui, Haneen Alidmat, Omar Elsoud, Esraa Abu Talat Khouj, Mohammed Katrawi, Anwar Alluwaici, Mo'Ath Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Mental diseases, mental disorders, or psychological disorders is a large group of symptoms which causes changes to the state of mood and reflect this on the behavior of the person with the mental disorder. It affects the patient's way of thinking, which limits his ability to perform his simple daily needs, and thus, becomes a burden on the individual, and on the surrounding society as well. There are common types of mental disorders such as depression, anxiety disorders, schizophrenia, and bipolar disorder. In this research, two main objectives are considered. The first objective is determining which is the best classifier for predicting a person's mental disorder among six well-known classifiers. The second objective is determining what is the best feature selection technique to use with mental disorders datasets considering six popular techniques, and with respect to several evaluation metrics. The results revealed that Logistic Regression and Naïve Bayes are the two best classification models, while Info Gain Ratio is the best feature selection method. © 2024 IEEE.

关键词： Adversarial machine learning

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Outage behavior of the downlink reconfigurable intelligent surfaces-aided cooperative non-orthogonal multiple access network over Nakagami-m fading channels

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Wireless Networks 2024年第6期30卷 5093-5110页

作者： Chen, Hsing-Chung Widodo, Agung Mulyo Lin, Jerry Chun-Wei Weng, Chien-Erh Do, Dinh-Thuan Department of Computer Science and Information Engineering Lioufeng Rd. Wufeng Taichung41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Jingmao Rd. Beitun Dist. Taichung406040 Taiwan Department of Computer Science Esa Unggul University Jalan Arjuna Utara Kebon Jeruk Jakarta11510 Indonesia Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Inndalsveien Hordaland Bergen5063 Norway Department of Telecommunication Engineering National Kaohsiung University of Science and Technology Haijhuan Rd. Nanzih Dist. Kaohsiung81157 Taiwan

Although the non-orthogonal multiple access network (NOMA) network has already been used in 5G in wireless communication, it still needs to be supplied with large enough energy, especially for the amplify-and-forward (AF) process. Reconfigurable intelligence surface (RIS)-aided NOMA answers this weakness because it can relay without an AF process so that it could reduce the energy required for NOMA. This is conducted by vigorously relaying communication signals via transmitted signal reflection. In addition, the decode-and-forward process is dispatched to increase the signal coverage performance. It is thus expected that the RIS-aided NOMA will substantially ensure the obtainment of a lower outage probability towards the signal to noise ratio, relative channel estimation error, and distance between a base station and near users. Despite its potential, RIS might encounter a challenge to being effectively incorporated with communication networks regarding channel estimation error. This study evaluates the RIS-aided NOMA to address the issues mentioned above, and carefully derives the closest-form expressions of outage probability for a pair of users by applying perfect channel statistic information over Nakagami-m fading channel. The performance of cooperative relaying scenarios during outages is thoroughly analyzed. According to the simulation results, RIS-aided NOMA has a lower outage probability than conventional NOMA. Additionally, the optimal location for user relaying in RIS-aided NOMA and conventional NOMA networks should be closest to the base station. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Signal to noise ratio

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Recent Advances in Analysis and Detection of Tuberculosis System in Chest X-Ray Using Artificial Intelligence (AI) Techniques: A Review

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Current Materials science 2023年第1期16卷 43-51页

作者： Suchitra, Shanmugam Ibrahim, S. Jafar Ali Sathya, Mariappan Sahini, Varsha Chakravarthy, N. Surya Kalyan Kumar, Vaneet Saruchi Department of Computer Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT SCOPE Vellore Institute of Technology Tamilnadu Vellore India AMC Engineering College Karnataka Bengaluru India Department of Computer Science Engineering CT Institute of Engineering Management and Technology Punjab Jalandhar India Data Science QIS College of Engineering and Technology Andhra Pradhesh Ongole India Department of Applied Sciences CT Institute of Engineering Management and Technology Shahpur Campus Punjab Jalandhar India Department of Biotechnology CT Institute of Pharmaceutical Sciences Shahpur Campus Punjab Jalandhar India

Mycobacterium tuberculosis causes tuberculosis (TB), a bacterial illness. Although germs are most typically found in the lungs, they can affect other sections of the body as well. Tuberculosis is one of the primary causes of mortality in both developed and developing nations, necessitating worldwide attention. Even though TB may be prevented in the majority of instances if discovered and treated early, the number of deaths caused by the disease is quite high. There has been a significant increase in interest and research activity in TB detection in recent years. The new advancement in the field of AI Technology may be able to assist them in overcoming these development gaps. computer-Aided Detection and Diagnosis (CADD) aids in the diagnosis of diseases by analysing symptoms and X-ray images of patients. Many solutions are currently be-ing developed to improve the effectiveness of TB diagnosis classification using AI and DL ap-proaches. Although a variety of TB detection techniques have been developed, there is no common-ly acknowledged method. The purpose of this study is to give a survey on Tuberculosis Detection. It also emphasises the difficulty and complexity of the Tuberculosis Detection System's design. © 2023 Bentham science Publishers.

关键词： computer vision

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Comparative Analysis of CNN and SVM Machine Learning Techniques for Plant Disease Detection 4th

Comparative Analysis of CNN and SVM Machine Learning Techniq...

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Towards Explainable Renewable Energy Communities Operations Using Generative AI

Towards Explainable Renewable Energy Communities Operations ...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Nammouchi, Amal Chaabani, Cyrine Theocharis, Andreas Kassler, Andreas Center for Digital Technology and Innovation Munich Germany Karlstad University Engineering and Physics Department Karlstad Sweden Karlstad University Computer Science Department Karlstad Sweden Deggendorf Institute of Technology Institute of Applied Computer Science Deggendorf Germany

ISBN: (纸本)9789531842976

Renewable Energy Communities (RECs), characterized by localized energy generation and consumption, are a key enabler for enhancing renewable energy utilization, cost-efficient planning and clean energy transition. However, optimizing RECs operations is challenging due to the complex interplay of different stakeholders with conflicting requirements. The complexity of managing such systems often leads to a lack of transparent and reliable decision-making, creating barriers for actors within the community. This paper explores the integration of Generative AI into Renewable Energy Communities (RECs) to enhance the transparency, explainability and accessibility of energy management systems (EMSs) that depend on solving optimization problems. We propose a novel framework, Chat-SGP, which uses generative AI to synthesize optimization modeling code to provide actionable, explainable insights for managing the REC operations. Our approach allows us to interact with the EMS through natural language queries, enhancing the system's accessibility and user-friendliness. Our evaluation shows that using GPT-4 with in-context learning performs 96.72% accuracy on average in generating correct answers. © 2024 IEEE.

关键词： Clean energy

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Efficient Vehicle Detection and Classification using YOLO v8 for Real-Time Applications

Efficient Vehicle Detection and Classification using YOLO v8...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Ravichandran, M. Laxmikant, Kumar Muthu, Annamalai Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India University of Technology & Applied Science Department of Electrical and Electronics Engineering Nizwa Oman

ISBN: (纸本)9798350308167

In the realm of real-time applications such as autonomous driving and surveillance, efficient vehicle detection stands as a paramount concern. The revolutionary You Only Look Once (YOLO) framework, renowned for its rapid object recognition, has reshaped this landscape. This study delves into the fusion of real-time vehicle detection and YOLO v8, optimizing its architecture for swift and precise identification. Empirical validation underscores the model's prowess, achieving a remarkable accuracy of 97.9% on mAP50 and 91.3% on mAP50-95. This achievement not only attests to the model's accuracy but also reaffirms its real-time processing capabilities. The implications resonate across transportation safety and urban planning, promising transformative potential. This research underscores the power of YOLO v8 in intelligent object detection, poised to reshape and enhance various domains. © 2023 IEEE.

关键词： mAP50 mAP50-95 Object Detection Vehicle Detection YOLO Framework

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Enhanced Adaptive Brain-computer Interface Approach for Intelligent Assistance to Disabled Peoples

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computer Systems science & engineering 2023年第8期46卷 1355-1369页

作者： Ali Usman Javed Ferzund Ahmad Shaf Muhammad Aamir Samar Alqhtani Khlood M.Mehdar Hanan Talal Halawani Hassan A.Alshamrani Abdullah A.Asiri Muhammad Irfan Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical Sciences and Information SystemsNajran UniversityNajran61441Kingdom of Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

Assistive devices for disabled people with the help of Brain-computer Interaction(BCI)technology are becoming vital bio-medical *** with physical disabilities need some assistive devices to perform their daily *** these devices,higher latency factors need to be addressed ***,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command *** proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification ***-sky mind wave device has been used to transfer the data to our implemented server for command ***-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data *** collection and processing are the responsibility of the central integrated server for system load *** of implemented architecture and deep learning model shows excellent *** proposed system integrity level was the minimum data loss and the accurate commands processing *** training and testing results are 99%and 93%for custom model implementation based on *** proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.

关键词： Disable person electroencephalogram convolutional neural network brain signal classification

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Enhancing Event Extraction and Impact Analysis with Knowledge Graph Augmented Transformer 21

Enhancing Event Extraction and Impact Analysis with Knowledg...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Songklang, Kallaya Srisomboon, Kanabadee Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Understanding the impact of events reported in news articles plays pivotal roles in various tasks. In this study, we propose a framework for extracting event components from the news and analyzing their multifaceted impact. The framework employs transformers techniques for event extraction and leverages a biography knowledge base to assess the potential impact of events across social, political, economic, and culture (SPEC) dimensions. Our framework achieves an F1-score of 78.12% in event identification as well as significant accuracy score in evaluating the impact levels prediction. By integrating results from event extraction and impact analysis, the level of multifaceted influence of the event along with the event information are effectively visualized. This comprehensive approach enhances understanding of multi-perspective interpretation and supports insights for various applications. © 2024 IEEE.

关键词： Distribution transformers

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