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18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Khokhar, Maham Bakir-Gungor, Burcu Yousef, Malik Department of Data Science Social Sciences Institute Abdullah Gul University Kayseri38080 Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat13206 Israel

The advent of high-throughput transcriptomic technologies has generated vast transcriptomic datasets, challenging current analytical methodologies with their sheer volume and complexity. The Grouping-Scoring Modeling (G-S-M) approach is one of the recent approaches that treat groups of genes (or clusters of genes) by embedding prior biological knowledge with machine learning in order to detect the most significant groups for classification tasks. The G-S-M might need to treat thousand ten thousand of groups (scoring those groups) which might affect the speed and performance of the algorithm. In response, this study introduces the Pre Scoring G-S-M model, an enhancement of the established Grouping-Scoring-Modeling (G-S-M) framework. This approach incorporates a Pre-Scoring component that leverages the Limma package for its empirical Bayes methods to optimize initial transcriptomic data evaluation through a percentage-based selection of statistically significant gene groups. Aimed at reducing computational demand and streamlining feature selection, the model also addresses data redundancy by eliminating duplicate gene-disease associations. Application to nine human gene expression datasets from the GEO database showed promising results. It demonstrated improvements in computational efficiency and analytical precision while reducing the number of features selected per dataset compared to the traditional G-S-M approach, without compromising accuracy. These initial findings highlight the Pre-Scoring G-S-M model's potential to enhance transcriptomic data analysis, indicating a promising direction for future bioinformatics research. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Data accuracy

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Robotic fish:Opening a new era of underwater detection

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The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Deep Learning for Segmentation of the Left Ventricle in Echocardiography

Deep Learning for Segmentation of the Left Ventricle in Echo...

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Sofia Ferraz Miguel Coimbra João Pedrosa Faculty of Engineering of the University of Porto (FEUP) Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal

Two-dimensional echocardiography is the most widely used non-invasive imaging modality due to its fast acquisition time, low cost, and high temporal resolution. Accurate segmentation of the left ventricle in echocardiography is vital for ensuring the accuracy of subsequent diagnosis. Currently, numerous efforts have been made to automatize this task and various public datasets have been released in recent decades to further develop present research. However, medical datasets acquired at different institutions have inherent bias caused by various confounding factors, such as operation policies, machine protocols, treatment preference, etc. As a result, models trained on one dataset, regardless of volume, cannot be confidently utilized for the others. In this study, we investigated model robustness to dataset bias using two publicly available echocardiographic datasets. This work validates the efficacy of a supervised deep learning model for left ventricle segmentation and ejection fraction prediction, outside the dataset on which it was trained. The exposure of this model to unseen, but related samples without additional training maintained a good performance. However, a performance decrease from the original results can be observed, while the impact of quality is also noteworthy with lower quality data leading to decreased performance.

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Flexibility Modeling and Trading in Renewable Energy Communities

Flexibility Modeling and Trading in Renewable Energy Communi...

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International Conference on European Electricity Market, EEM

作者： João Agrela Igor Rezende Tiago Soares Clara Gouveia Ricardo Silva José Villar Institute for Systems and Computer Engineering Technology and Science (INESC TEC) University of Porto Faculty of Engineering (FEUP) Porto Portugal

This ork presents an approach to the flexibility of energy consumption in Renewable Energy Communities (RECs). A two-stage model for quantifying the flexibility provided by the domestic energy resources operation and its negotiation in a market platform is proposed. In stage 1, the optimal consumption of each prosumer is determined, as well as the respective technical flexibility of their resources, namely the maximum and minimum resource operation limits. In stage 2, this technical flexibility is offered in a local flexibility-only market structure, in which both the DSO and the prosumers can present their flexibility needs and requirements. The flexibility selling and buying bids of the prosumers participating in the market are priced based on their base tariff, which is the energy cost of the prosumers corresponding to their optimal schedule of the first stage when no flexibility is provided. Therefore, providing flexibility is an incentive to reduce their energy bill or increase their utility, encouraging their participation in the local flexibility market.

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A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

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International Conference on computer and Applications (ICCA)

作者： Mohamed Hossam Hassan Nabil Laila Hammam Hany Ayad Bastawrous Gamal A. Ebrahim Electrical Engineering Department The British University in Egypt Cairo Egypt Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlans Computer and Systems Engineerig Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively.

关键词： Deep learning Training Support vector machines Diabetic retinopathy Computational modeling Transfer learning Retina Convolutional neural networks Sugar Diseases

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Participants Evaluation of computer-Based Writing Exam Eligibility in High School Using Simple Additive Weighting Method 7

Participants Evaluation of Computer-Based Writing Exam Eligi...

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7th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2021

作者： Negoro, Wahyu Saptha Nasution, M. Irfan Aldy Ramadhanu, Agung Agustin, Fhery Muhatrii, Muhatri Amri, Syahputra Universitas Potensi Utama Faculty of Engineering and Computer Science Medan Indonesia Universitas Putra Indonesia 'YPTK' Faculty of Computer Science Information Systems Padang Indonesia

ISBN: (纸本)9781728175225

Admission of New Students at the Joint Selection of State Universities (SBMPTN) is a selection process for entering State Universities (PTN), where the selection uses a computer-based written exam (UTBK). The test results are the highest scores that become a reference for PTNs and the Ministry of Research, Technology and Higher Education sets the SBMPTN quota of 40% in each department. This is a big competition for participants to graduate in the desired major. As well as the obstacles faced by participants in the form of not knowing whether the results of the UTBK scores obtained were feasible or not at PTN. The method used in this study is Simple Additive Weighting (SAW), where the calculation provides certain weighting criteria so that each total weight value and result will be a recommendation for SBMPTN participants at SMAN 2 West Sumatra. The data used is divided into 2 groups, namely the IPA and Soshum group data. So that the recommended results are feasible in this study from the science and technology test group as many as 23 participants because they got a total average score of >0.80 and Soshum as many as 7 participants. The data of participants in the study were 40 students, the total recommended eligibility for the SBMPTN pathway was 30 participants and the results of comparison with the actual data contained 2 data processing results that had differences. So it can be explained that there are 38 participants from 40 test data who have the same decision, between the decisions made by the system and the decisions of the SBMPTN selection party, which is 95%. The calculation of this method can be applied in every UTBK selection on the SBMPTN pathway which is the reference for participants to enter higher education. © 2021 IEEE.

关键词： Decision support systems

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LayOut Loud: An AI-Powered Augmented Reality and Mobile Application for Room Interior Design and Layout Optimization 4

LayOut Loud: An AI-Powered Augmented Reality and Mobile Appl...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Cuevas, John Raphael G. Rili, John Kenneth R. Santillan, Anny Neolicia G. Agustin, Vivien A. Gonzales, Mary Grace N. Centeno, Criselle J. Regala, Richard C. College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Information Technology Manila Philippines College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Computer Science Manila Philippines Pamantasan ng Lungsod ng Maynila Faculty of College of Engineering and Technology Department of Information Technology Manila Philippines College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Information Technology Manila Philippines Pamantasan ng Lungsod ng Maynila College of Engineering and Technology Department of Computer Science Manila Philippines

ISBN: (纸本)9798331506490

LayOut Loud is an AI-powered augmented reality (AR) and mobile application designed to revolutionize room interior design by offering tailored, real-time solutions for layout optimization. The primary objective of this tool is to design AI-powered features that analyze user-provided room dimensions, furniture types, and style preferences. By leveraging advanced algorithms, LayOut Loud generates personalized design suggestions that cater to the individual needs and aesthetic tastes of users, ensuring that each interior design experience is customized and unique. A key goal of the application is to enhance user experience and decision-making through a clustering algorithm. This algorithm categorizes furniture pieces based on their suitability and appearance in living spaces, streamlining the selection process for users. By enabling the visualization of these items in a 3D model, users can make informed decisions about their interior design choices, ensuring that each piece contributes to the overall aesthetic and functional harmony of the space. Additionally, LayOut Loud focuses on improving its AR capabilities by allowing users to capture their room layouts and view virtual furniture arrangements in real time. This feature provides practical, interactive design solutions, empowering users to experiment with different configurations and instantly see how potential changes would appear in their actual living environments. Ultimately, LayOut Loud offers a user-friendly, immersive platform that transforms the way individuals approach interior design. © 2024 IEEE.

关键词： Architectural design

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Evaluation of different bidding strategies for a battery energy storage system performing energy arbitrage – a neural network approach

Evaluation of different bidding strategies for a battery ene...

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International Conference on European Electricity Market, EEM

作者： Pedro Santos Igor Rezende Tiago Soares Vladimiro Miranda Institute for Systems and Computer Engineering Technology and Science (INESC TEC) University of Porto Faculty of Engineering (FEUP) Porto Portugal

The rising potential for battery energy storage systems (BESS) to generate revenue in a market environment is addressed in this work, where a tool based on neural network predictions is proposed. The tool's main objective is predicting, based on historical data, the most lucrative out of three established bidding approaches for the participation of a BESS in the day-ahead energy market and thus aid the strategic bidding process of the BESS operator. Each of these bidding strategies reflects BESS’s operator approach concerning bidding frequency and the tolerated risk of loss of profit from having its bids rejected, leading to the development of a conservative (strategy A), an aggressive (strategy B), and a moderate strategy (strategy C). A case study was then used to test the tool for a full year allowing to ascertain the assertiveness of this tool in predicting the best strategy, which for this case was above 88%.

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Analyzing darknet traffic through machine learning and neucube spiking neural networks

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Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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