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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Vetriselvi, D. Sampathrajan, Rajeshkumar Deepa, A. Sujatha, V. Deepa, P. Pandi Murugan, S. Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Agni College of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

This research work presents a new method for improving individualized treatment for patients with Multiple Sclerosis (MS) by combining Internet of Things (IoT) wearables with deep learning (DL) methods, namely Long Short-Term Memory (LSTM) networks. MS is a long-term autoimmune disorder that affects the central nervous system. It is marked by unexpected episodes of worsening symptoms and periods of improvement, highlighting the need for tailored therapy. The widespread use of IoT devices presents unique possibilities for the ongoing monitoring of many physiological and activity measurements important to the development and symptoms of MS. Using LSTM networks, which are very proficient in analyzing temporal sequences, create a prediction model that may anticipate exacerbations of MS symptoms and customize therapies appropriately. Through the analysis of longitudinal data obtained from IoT devices, our model acquires knowledge about unique patterns of illness development and treatment response. This knowledge allows us to create customized care plans for individuals. These paths include proactive activities such as adjusting medication, making lifestyle changes, and seeking early medical advice. The goal is to reduce the worsening of symptoms, improve the quality of life, and optimize treatment results for individuals with MS. The proposed method shows potential for transforming the treatment of MS by offering proactive and patient-centered therapy adaptable to individual requirements and the dynamics of the illness. © 2024 IEEE.

关键词： Patient treatment

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A Simple Moving Average-Based Predictive IoT Architecture for Energy Consumption in Smart Cities

A Simple Moving Average-Based Predictive IoT Architecture fo...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： Delsi Robinsha S Amutha B Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamilnadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Efficient energy management systems which monitor and forecast along with optimal consumption management are fundamental requirements for smart city development. The implementation of Internet of Things (IoT) allows real-time data collection that leads to predictive energy management prospects. This work develops an energy consumption forecast system through a lightweight IoT infrastructure which utilizes Simple Moving Average (SMA) predictive methodology. Using real-time node data from households and industries the SMA approach delivers energy consumption predictions suitable for constrained resource conditions. A model evaluation based on Mean Absolute Percentage Error and R2 scores shows the model maintains reliable accuracy despite implementation across varying consumption ranges and low computational expenses. The presented study demonstrates how SMA technology helps address smart city energy management needs and delivers an implementation plan for optimizing urban energy systems sustainably.

关键词： Energy consumption Adaptation models Accuracy Smart cities Computational modeling Computer architecture Real-time systems Internet of Things Reliability Load modeling

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Comparative Study of EfficientNet and MobileNet Models for Lung Cancer Classification Using Chest CT Scan Images 2

Comparative Study of EfficientNet and MobileNet Models for L...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Muthulakshmi, M. Venkatesan, K. Harigaran, R. Jeevanantham, K. Sri Varshan, P. Vineeth, M.S. Rahayu, Syarifah Bahiyah Sakthivel, V. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Ece Chennai Campus India Faculty of Defense Science and Technology Department of Science Defense Malaysia Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Cse Chennai Campus India

ISBN: (纸本)9798350328202

Cancer is the second-leading cause of death in the world. Uncontrolled cell division of damaged cells causes lung cancer, leading to the formation of tumors. Lung cancer typically originates in the lungs, often in the bronchi or bronchioles. Among various cancer types, lung cancer stands out as one of the most dangerous and deadly forms. However, early diagnosis can significantly improve the patient survival rate. Computed Tomography (CT) is widely regarded as one of the best imaging techniques in the medical field for diagnosis. Nevertheless, in-terpreting CT images accurately, especially when cancer tumors are very small, can be challenging. This is where deep learning models come into play. Deep learning models, a subset of machine learning, play a vital role in interpreting these images for diagnosis. They excel at automatically analyzing vast amounts of CT scan data, enabling the early detection of lung cancer and assisting medical professionals in distinguishing between benign and malignant lesions, thereby contributing to improved patient outcomes. In this study, we have implemented different CNN versions of architectures of Efficientnet and MobileNet. We compared the results of all the models. The models are evaluated based on metricslike accuracy, F1-Score and recall. © 2024 IEEE.

关键词： Diagnosis

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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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Computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： Computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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Machine learning driven performance for hole transport layer free carbon-based perovskite solar cells

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npj Computational Materials 2024年第1期10卷 1014-1022页

作者： Sreeram Valsalakumar Shubhranshu Bhandari Anurag Roy Tapas K.Mallick Justin Hinshelwood Senthilarasu Sundaram Solar Energy Research Group Environment and Sustainability InstituteUniversity of ExeterPenryn CampusCornwallTR109FEUK Mechanical and Energy Engineering College of EngineeringImam Abdulrahman Bin Faisal UniversityDammam34212Saudi Arabia Faculty of Environment Science and Economy University of Exeter Penryn CampusCornwallTR109FEUK School of Computing Engineering and Design TechnologiesTeeside University Tees ValleyMiddlesbroughTS13BXUK

The rapid advancement of machine learning(ML)technology across diverse domains has provided a framework for discovering and rationalising materials and photovoltaic *** study introduces a five-step methodology for implementing ML models in fabricating hole transport layer(HTL)free carbon-based PSCs(C-PSC).Our approach leverages various prevalentMLmodels,andwe curated a comprehensive dataset of 700 data points using SCAPS-1D simulation,encompassing variations in the thickness of the electron transport layer(ETL)and perovskite layers,along with bandgap *** results indicate that the ANN-based ML model exhibits superior predictive accuracy for C-PSC device parameters,achieving a low root mean square error(RMSE)of 0.028 and a high R-squared value of *** novelty of this work lies in its systematic use ofMLto streamline the optimisation process,reducing the reliance on traditional trial-and-error methods and providing a deeper understanding of the interdependence of key device parameters.

关键词： learning layer perovskite

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Fractional Fourier Transform-Based OFDM System with low complexity equalizer for Underwater Acoustic Communications 25

Fractional Fourier Transform-Based OFDM System with low comp...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Attar, Hani Ababneh, Jafar Rachini, Ali Bahy, Ramy M. Hafez, Mohamed Solyman, Ahmed Zarqa University Faculty of Engineering Zarqa Jordan Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Department of Computer Science and Information Technology Jounieh Lebanon Technical Research Center Cairo Egypt Feqs INTI-IU-University Faculty of Engineering Nilai Malaysia Glasgow Caledonian University School of Computing Engineering and Built Environment Glasgow United Kingdom

ISBN: (纸本)9798331540012

Underwater communication systems have garnered significant interest due to the limitations of the wireless spectrum and the growing need for sustained, covert communication capabilities. However, existing systems face significant challenges from doubly dispersive channels that cause time and frequency fading, disrupting the orthogonality of conventional OFDM subcarriers and leading to severe inter-symbol interference (ISI), even with complex equalizers like MMSE. This paper proposes a Fractional Fourier Transform (FrFT)-based OFDM system, offering better adaptability in the time-frequency domain. The FrFT-OFDM system effectively concentrates power around the diagonal of the channel matrix, mitigating Doppler shifts and multipath fading, thus enhancing spectral efficiency and robustness. A low-complexity equalizer is introduced, reducing computational demands while maintaining high performance, ensuring practical real-time application in underwater environments. Simulation results show that the proposed DFrFT-OFDM system with the optimized equalizer surpasses traditional OFDM under severe channel dispersion, demonstrating improved bit error rates and signal-to-noise ratios by about 3 db. This confirms its capability for reliable underwater communication with reduced computational overhead by about 90% comparing the OFDM with MMSA and DFrFT-OFDM with bande MMSA, marking a significant step forward for high-performance data transmission in challenging aquatic environments. © 2024 IEEE.

关键词： Bit error rate

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Improvement of Swarm Intelligence Algorithm and Its Application in Logistics Network Routing

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1077-1094页

作者： Zhang, Xin Wei, Yanqiu Hashim, Zrdplwa School of Business Applied Technology College of Soochow University Kunshan 215325 China Fan Li Business School Shaoxing Vocational Technical College Shaoxing 312000 China Faculty of Engineering Computing Science Swinburne University of Technology Kuching93350 Malaysia

The Vehicle Routing Problem (VRP) is a key aspect of logistics network routing, and an excellent routing optimization strategy can effectively improve the service experience of users and reduce transportation costs. With the explosive growth of e-commerce, the logistics network routing system is faced with a huge number of demand points and customer-specified receiving time slots, which poses a huge challenge for routing optimization of large-scale delivery. To address the problems of high total cost and low effective vehicle utilisation in the logistics network routing system, this work introduces a new swarm intelligence method, the pigeon-inspired optimization (PIO), and improves it. Two improvement strategies are proposed to address the strengths and weaknesses of the PIO algorithm. Firstly, by combining the high swarm dispersion of the quantum evolutionary algorithm and the fast convergence of the PIO, the PIO method is upgraded by mixing the algorithms to achieve the effect of complementing each other’s strengths and improving the global exploration ability of the PIO algorithm;secondly, a Gaussian variation operator is added to the PIO algorithm to enhance its local exploitation capa-bility and prevent prematurity in order to retain the variety in future iterations. Each individual contains information on both client points and routes. The effectiveness of the improved pigeon flock intelligence optimisation algorithm is verified through test function simulations. The effectiveness and rationality of the improved PIO algorithm is verified in a case study based on the Solmon arithmetic example, which has some engineering application value. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Routing algorithms

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Towards the Prognosis of Patients in Coma using Echo State Networks for EEG Analysis

Towards the Prognosis of Patients in Coma using Echo State N...

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International Neural Network Society Workshop on Deep Learning Innovations and Applications, INNS DLIA 2023

作者： Bissaro, Lucas Z. Nascimento, Odelmo O. Destro Filho, João B. Jin, Yaochu Carneiro, Murillo G. Faculty of Computing Federal University of Uberlandia Uberlandia Brazil Faculty of Electrical Engineering Federal University of Uberlandia Uberlandia Brazil Faculty of Technology Bielefeld University Bielefeld Germany Department of Computer Science University of Surrey Guildford United Kingdom

The electroencephalogram (EEG) exam registers the brain's electrical signals using positioned electrodes over the scalp. The signals can be used in many medical applications, such as the prognosis and monitoring of diseases. However, EEG data also require efficient learning solutions in order to deal with their inherent noises and artifacts. In this paper, we propose simple, fast and accurate architectures of Echo State Networks (ESNs) capable of considering the sequential patterns of the exam. To be specific, we develop three ESNs based on a baseline, a single-reservoir and a multi-reservoir architectures, which are responsible for mapping temporal (or sequential) inputs into a high-dimensional space. To evaluate the ESN architectures, we addressed the problem of prognostic of patients in coma (PPC) from EEG records considering two formulations: binary and multi-class. Experiments on both scenarios considering the three ESN architectures as well as traditional techniques (without reservoir) were performed. The results revealed the potential of our single and multi-reservoir architectures over the baseline architecture and other techniques without the reservoir structure. Interestingly, the ESN single-reservoir architecture achieved the best predictive performance for both binary and multi-class formulations. © 2023 The Authors. Published by Elsevier B.V.

关键词： Electroencephalography

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Cognition Based Fog layer to Mitigate the challenges of Real-Time Medical Applications

Cognition Based Fog layer to Mitigate the challenges of Real...

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2022 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Kalingarani, G. Selvaraj, P. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamilnadu Kattankulathur603203 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Medical and healthcare applications in human life stand as two main things supporting scientific and technological development. Healthcare information systems have been dominated for decades by the existing Cloud technology. However, in cloud computing applications for healthcare systems there exists limitations like high-speed response time in fetching the patient data for providing a quick decision, and delay which acts as a high-risk factor in the patient's life. To solve the difficulties, fog computing is handled between the edge and cloud in the data center. Fog computing serves as an intermediary platform for delivering cloud services to network endpoints. For rapid service time, end devices can reach specified fog nodes in the fog layer. This study describes the architectural framework for a fog layer-assisted web of real-time healthcare applications and also justifies the limitations of the fog layer in applications in the medical field. © 2022 IEEE.

关键词： Fog

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