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Multi-stage biomedical feature selection extraction algorithm for cancer detection

SN applied sciences 2023年第5期5卷 1-12页

作者： Keshta, Ismail Deshpande, Pallavi Sagar Shabaz, Mohammad Soni, Mukesh Bhadla, Mohit kumar Muhammed, Yasser Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Engineering Pune India Arba Minch University Arba Minch Ethiopia Department of CSE University Centre for Research & Development Chandigarh University Punjab Mohali140413 India Department of Information Technology Ahmedabad Institute of Technology Ahmedabad India College of Technical Engineering Al-Farahidi University Baghdad Iraq

Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarray data set and the number of samples. Because of this, it is crucial to identify markers for gene array data. Existing feature selection algorithms, however, generally use long-standing, are limited to single-condition feature selection and rarely take feature extraction into account. This work proposes a Multi-stage algorithm for Biomedical Deep Feature Selection (MBDFS) to address this issue. In the first, three feature selection techniques are combined for thorough feature selection, and feature subsets are obtained;in the second, an unsupervised neural network is used to create the best representation of the feature subset to enhance final classification accuracy. Using a variety of metrics, including a comparison of classification results before and after feature selection and the performance of alternative feature selection methods, we evaluate MBDFS's efficacy. The experiments demonstrate that although MBDFS uses fewer features, classification accuracy is either unchanged or enhanced. © 2023, The Author(s).

关键词： Feature Selection

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Leveraging Topic Modeling and Extractive Summarization for Unlocking Insights from NeurIPS Papers

Leveraging Topic Modeling and Extractive Summarization for U...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Sheikh Sharfuddin Mim Doina Logofatu Gabriel Guerrero-Contreras Inmaculada Medina-Bulo Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany Department of Computer Engineering University of Cadiz Cadiz Spain

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

Efficiently comprehending vast volumes of text, particularly in scientific literature, is a formidable challenge for researchers. To alleviate this burden, this paper investigates the application of topic modeling and extractive summarization techniques to analyze and distill valuable insights from scientific documents. Focusing on the NeurIPS dataset, which encompasses a diverse array of scientific papers, we employ techniques such as Latent Dirichlet Allocation (LDA) and probabilistic Latent Semantic Analysis (pLSA) to categorize documents based on their content. Subsequently, we implement a domain-specific extractive summarizer that leverages Convolutional Neural Networks (CNNs) for feature extraction and semantic segmentation. The summarizer is trained to identify and rank sentences based on relevance, using binarized ROUGE-2 scores as a metric. Our results demonstrate the effectiveness of these methods in condensing scientific documents, facilitating quicker information retrieval and understanding. The paper also compares the performance of LDA and pLSA, highlighting LDA’s superiority in topic coherence and generalizability. By leveraging these methods, we aim to demonstrate their efficacy in processing large volumes of text, facilitating enhanced information retrieval and understanding in the realm of scientific literature.

关键词： Measurement Technological innovation Semantic segmentation Semantics Focusing Information retrieval Feature extraction

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Employing Deep Neural Networks for Real-Time Anomaly Detection and Mitigation in IoT-Based Smart Grid Cybersecurity Systems 3

Employing Deep Neural Networks for Real-Time Anomaly Detecti...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Quraishi, Aadam Rusho, Maher Ali Prasad, Anurag Keshta, Ismail Rivera, Richard Bhatt, Mohammed Wasim Intervention Treatment Institute Research HoustonTX United States University Of Colorado Boulder Department Of Lockheed Martin Engineering Management BoulderCO United States Gurugram India College Of Applied Sciences AlMaarefa University Computer Science And Information Systems Department Riyadh Saudi Arabia Escuela Politécnica Nacional Department Of Informatics And Computer Science Quito Ecuador Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

This research introduces a novel anomaly detection framework for IoT -based Smart Grid Cybersecurity Systems. Leveraging autoencoders, LSTM networks, GANs, SOMs, and transfer learning, our approach achieves superior precision, recall, and execution time compared to existing methods. Visualizations and an ablation study further validate the method's efficiency, emphasizing the critical roles of attention mechanisms and transfer learning. This comprehensive solution addresses the dynamic challenges of smart grid cybersecurity, offering a versatile and adaptive anomaly detection mechanism for real-world applications. This indicates the real-time efficacy of our anomaly detection method. Through our study of ablation and all aspects of computing, we discovered that attention processes and transfer learning facilitate faster problem solving in a dynamic smart grid. Our method is distinct and adaptable enough to address every problem arising from the discovery of anomalies in IoT-driven Smart Grid Cybersecurity Systems. © 2024 IEEE.

关键词： Anomaly detection

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A Hybrid SARIMAX Model in Conjunction with Neural Networks for the Forecasting of Life Insurance Industry Growth in Thailand 9

A Hybrid SARIMAX Model in Conjunction with Neural Networks f...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Huadsri, Supika Mekruksavanich, Sakorn Jitpattanakul, Anuchit Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao56000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798350318241

This article is conducted with the primary aim of comparing various forecasting models to identify the optimal model for forecasting the growth of the life insurance industry in Thailand. The models under consideration include the SARIMAX model, the Recurrent Neural Network (RNN), and a hybrid model introduced as RNN-SARIMAX. The dataset utilized for this investigation is extensive, consisting of monthly direct premium data from the Thai life insurance sector spanning January 2003 to December 2022. The dataset is segregated into two distinct segments. Initially, data from January 2003 to December 2021 is employed for constructing the forecasting model. Subsequently, data from January 2022 to December 2022 is utilized for a comparative analysis of the forecasting models, to determine the most suitable and accurate model for predicting the growth of the life insurance industry in Thailand. The evaluation criteria for comparing models include the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R-squared). The consistent findings from our analysis indicate that the SARIMAX model consistently outperforms both the RNN and RNN-SARIMAX models. © 2024 IEEE.

关键词： Recurrent neural networks

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RUBY - A versatile and customizable social robot

RUBY - A versatile and customizable social robot

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Andreas Kitzig Edwin Naroska Shanq-Jang Ruan Marcus Weberskirch Sinan Yavuz Department of Electrical Engineering and Computer Science Niederrhein University of Applied Sciences Krefeld Germany Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798350378771

ISBN: (纸本)9798350378788

The RUBYDemenz project investigated the effectiveness and optimization of personalized human-robot interaction, which serves as support and relief in the home care of people with dementia. The system, in the form of a puppet interacting with the user via voice, offers a low-threshold and playful integration into everyday life. It was developed by a consortium of various research institutions, companies, and social institutions and was carried out over a period of 42 months. The Niederrhein University of applied sciences developed the technical system of the puppet. The consortium owns the rights, plans, and developed major parts of the software. The development of the system included the construction of the hardware, mechanics, and electronics, the software, and the interfaces to enable numerous functions. The system is versatile, flexibly expandable, and cost-effective, making it ideal for studies with many field deployments. Due to this properties, it can be easily adapted to various application scenarios making it a perfect system platform for social robot research efforts.

关键词： Circuits and systems Social robots Asia Companies Software Hardware Optimization Dementia

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Hybrid Techniques of Multi-CNN and Ensemble Learning to Analyze Handwritten Spiral and Wave Drawing for Diagnosing Parkinson's Disease

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computer Modeling in engineering & sciences 2025年第5期143卷 2429-2457页

作者： Mohammed Al-Jabbar Mohammed Alshahrani Ebrahim Mohammed Senan Ibrahim Abunadi Sultan Ahmed Almalki Eman A Alshari Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Applied SciencesHajah UniversityHajah9671Yemen Department of Artifcial Intelligence Faculty of Computer Science and Information TechnologyAl-Razi UniversitySanaa9677 Yemen Department of Sofware Engineering Cllege of Computer and Information SciencesKing Saud UniversityRiyadhI1543Saudi Arabia Computer Science and Information Technology Department Thamar UniversityDhamar9676Yemen

Parkinson’s disease(PD)is a progressive neurodegenerative disorder characterized by tremors,rigidity,and decreased *** poses risks to individuals’lives and *** detection of PD is essential because it allows timely intervention,which can slow disease progression and improve *** diagnosis of PD is problematic because it is difficult to capture the subtle patterns and changes that help diagnose *** addition,the subjectivity and lack of doctors compared to the number of patients constitute an obstacle to early *** intelligence(AI)techniques,especially deep and automated learning models,provide promising solutions to address deficiencies in manual *** study develops robust systems for PD diagnosis by analyzing handwritten helical and wave graphical *** graphic images of the PD dataset are enhanced using two overlapping filters,the average filter and the Laplacian filter,to improve image quality and highlight essential *** enhanced images are segmented to isolate regions of interest(ROIs)from the rest of the image using a gradient vector flow(GVF)algorithm,which ensures that features are extracted from only relevant *** segmented ROIs are fed into convolutional neural network(CNN)models,namely DenseNet169,MobileNet,and VGG16,to extract fine and deep feature maps that capture complex patterns and representations relevant to PD *** and deep feature maps extracted from individual CNN models are combined into fused feature vectors for DenseNet169-MobileNet,MobileNet-VGG16,DenseNet169-VGG16,and DenseNet169-MobileNet-VGG16 *** fusion technique aims to combine complementary and robust features from several models,which improves the extracted *** feature selection algorithms are considered to remove redundancy and weak correlations within the combined feature set:Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS).These algorithms i

关键词： CNN XGBoost RF GVF fusion feature PD

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Acoustic cavitation bubble under influence of frequency ultrasound: Numerical analysis 3

Acoustic cavitation bubble under influence of frequency ultr...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Abu-Nab, A.K. Abu-Bakr, A.F. Abushaeer, Zain Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Moscow Institute of Physics and Technology Phystech School of Applied Mathematics and Informatics Moscow141700 Russia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Higher Institute of Engineering and Technology Department of Basic Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350323511

This work presents a numerical investigation of acoustic cavitation in Newtonian fluid based on frequency ultrasound. The model of ultrasonic cavitation dynamics is formulated by a modified Rayleigh-Plesset equation and an acoustic pressure equation. The exponential B-Spline collection method was employed to obtain the numerical solutions of the given cavitation bubble model, considering the effect of acoustic pressure. The behaviour of the cavitation microbubbles' radius and the internal pressure of acoustic cavitation were examined at different values of frequencies. The obtained results reveal the importance of frequencies in ultrasound, which play a vital role in changing the behaviour of cavitation bubbles. © 2023 IEEE.

关键词： Numerical methods

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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MATLAB Simulation of 500 W Direct Methanol Fuel Cell Stack 2nd

MATLAB Simulation of 500 W Direct Methanol Fuel Cell Stack

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2nd International Conference on Materials, Design and Manufacturing for Sustainable Environment, ICMDMSE 2022

作者： Babu, S. Prem Kumar, T. Divya, V.V. Paulinga Prakash, R. Jeriel, D. Department of Mechanical Engineering PSG College of Technology Coimbatore641004 India Department of Mechanical Engineering PSG Institute of Technology and Applied Research Coimbatore641062 India Department of Mathematics With Computer Applications PSG College of Arts & Science Coimbatore641014 India Department of Mechanical Engineering VEL Tech Multitech Chennai600062 India

ISBN: (纸本)9789811930522

This paper aims about the mathematical model analysis of 500 W direct methanol fuel cell (DMFC) using MATLAB SIMULINK. Three dynamic models, Input I-stdy-ideal, Input I-stdy, InputI-dynshrt4, were included with simulation time of 4900 ms, 3900 ms and 2040 ms, respectively. Ideal cell load with steady state, real fuel cell load with steady and real fuel cell load with transient state had been studied in detail. Pattern of output voltage, current density, power density and temperature distribution with respect to time has been found. Decreasing voltage pattern and increasing current density pattern are in line with typical VI (Voltage-Current density) curve. The optimum cell conditions obtained at 3100 ms are 25 V, 23 A/cm2 which gives the maximum power density of 575 W/cm2 at the operating temperature of 315 K. One of the major advantages of DMFC is obtaining maximum efficiency at the operating temperature which is closed to ambient conditions unlike proton exchange membrane fuel cell (PEMFC). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Current density

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Optical Character Recognition (OCR) in Handwritten Characters Using Convolutional Neural Networks to Assist in Exam Reader System 2

Optical Character Recognition (OCR) in Handwritten Character...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Lekshmy, P.L. Velmurugan, S. Kumari, Indra Kayalvili, S. Teja Sree, B. Karthik Kumar, P. LBS Institute of Technology for Women Department of Computer Science and Engineering Kerala India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Machine Learning Data Research Applied AI Daejeon Korea Republic of Department of Artificial Intelligence Tamilnadu Erode India S.R.K.R. Engineering College Department of Information Technology Andhra Pradesh Chinaamiram Bhimavaram India Coimbatore India

ISBN: (纸本)9798350371314

This work aimed to develop a character recognition method to facilitate the correction of answer cards in the Multiprova software through the development of a response card analysis flow that would culminate in the recognition of letters written by students and automatic correction of the tests. To isolate and identify the answers written on the answer cards, image segmentation techniques were used based on fixed marks printed on the cards. To recognize the letters and numbers written on the cards, trained three convolutional neural networks (for digits, letters and true or false). The results achieved (98.84% accuracy for digit CNN, 98.38% accuracy for letter CNN and 99.89% accuracy for true or false CNN) point out a great average success rate. © 2024 IEEE.

关键词： Convolutional neural networks

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