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Cascaded PFLANN Model for Intelligent Health Informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation

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Journal of Artificial Intelligence and Technology 2024年第2期4卷 124-131页

作者： Jagannath Dayal Pradhan L.V.Narasimha Prasad Tusar Kanti Dash Manisha Guduri Ganapati Panda Electronics and Communications Engineering C V Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering Institute of Aeronautical EngineeringHyderabadIndia School of Computing and Informatics University of LouisianaLafayetteLAUSA

Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-inspired computation,and the Internet of Medical *** has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of ***-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising *** the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection ***,the two main requirements of this are higher accuracy and lower computational complexity and,in many cases,these two requirements do not correlate with each *** problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy.A cascaded perceptual functional link artificial neural network(PFLANN)is used to capture the nonlinearity in the data for better classification performance with low computational *** proposed model is being tested for multiple respiratory diseases,and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.

关键词： AI-ML ASR FLANN health informatics neural network PFLANN

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Pioneering Pediatric Health - Machine Learning Ensembles for Appendicitis Detection 15

Pioneering Pediatric Health - Machine Learning Ensembles for...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vimal, Tejasvin Ganesh, Amruth J.M. Kumar, S. Yougesh Abhishek, S. Anjali, T. Department of Electronics and Communication Engineering Amrita School of Engineering Amritapuri India Department of Computer Science and Engineering Amrita School of Computing Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350370249

Children’s appendicitis is often diagnosed using outdated techniques that may not be as quick or accurate as they need to be. The present investigation employs sophisticated machine learning (ML) methodologies to mitigate these constraints and enhance the diagnostic results. We investigated the effectiveness of many ensemble machine learning (ML) models, such as Gradient Boosting Machine (GBM), CatBoost, LightGBM, AdaBoost, and Decision Trees, using a proprietary dataset that contains a diverse range of pediatric appendicitis cases. After extensive training and validation, these models showed amazing accuracy and precision rates—the best models reaching up to 99% accuracy. In addition to outlining each model’s effectiveness, our comparison study clarifies which models are applicable in clinical contexts. This research work addresses how these models might improve clinical processes, lower diagnostic mistake rates, and speed up and improve the accuracy of clinical decision-making. The findings of this study demonstrate the revolutionary potential of machine learning to improve pediatric healthcare by offering reliable instruments for intricate medical diagnosis. ©2024 IEEE.

关键词： Adversarial machine learning

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Stock price predictive analysis: An application of hybrid Barnacles Mating Optimizer with Artificial Neural Network

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International Journal of Cognitive computing in engineering 2023年第1期4卷 109-117页

作者： Mustaffa, Zuriani Sulaiman, Mohd Herwan Faculty of Computing Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Pahang Pekan26600 Malaysia

Artificial Neural Network (ANN) is an effective machine learning technique for addressing regression tasks. Nonetheless, the performance of ANN is highly dependent on the values of its parameters, specifically the weight and bias. To improve its predictive generalization, it is crucial to optimize these parameters. In this study, the Barnacles Mating Optimizer (BMO) is employed as an optimization tool to automatically optimize these parameters. As a relatively new optimization algorithm, it has been shown to be effective in addressing various optimization problems. The proposed hybrid predictive model of BMO-ANN is tested on time series data of stock price using six selected inputs to predict the next day’ closing prices. Evaluated based on Mean Square Error (MSE) and Root Mean Square Error (RMSPE), the proposed BMO-ANN exhibits significant superiority over the other identified hybrid algorithms. Additionally, the difference in means between BMO-ANN and other identified hybrid algorithms was found to be statistically significant, with a significance level of 0.05%. © 2023

关键词： Optimization

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Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

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Multimedia Tools and Applications 2024年第30期83卷 74757-74783页

作者： Prasad, Vadamodula Ganeshan, R. Rajeswari, R. Department of Computer Science & amp Engineering Lendi Institute of Engineering & ampTechnology Jonnada Village Andhra Pradesh Vizianagaram District 535005 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. This paper aims at ASD classification using MRI Images by Artificial Gannet Optimization enabled Deep Convolutional Neural Network (AGO_DCNN). Here, an MRI image is assumed that be subjected to pre-processing of the image and feature extraction phases as an input. The bilateral filter is employed to remove the noises from the input MRI image and in the pre-processing of an image stage Region of Interest (ROI) extraction is conducted. The pivotal region extraction phase receives a filtered image after which AGO is used to extract the pivotal region. AGO is a newly designed approach by merging two optimizations namely Artificial Ecosystem-based Optimization (AEO) and Gannet Optimization Algorithm (GOA). At the same time, features are extracted from the input image during the feature extraction stage. In a feature extraction stage, some features like texture and statistical features are extracted. At last, the classification of ASD is conducted using DCNN, wherein by AGO the classifier is tuned. Furthermore, AGO_DCNN attained better outcomes in terms of maximal True Positive Rate (TPR), True Negative Rate (TNR), and accuracy values of about 93.7%, 90.8%, 96.8% and False Positive Rate (FPR) and minimal False Negative Rate (FNR) and values of 78.7% and 74.7%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Detection of myocardial infarction diseases based on entropy, optimized stockwell time-frequency transform and extreme learning machine

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International Journal of Information Technology (Singapore) 2025年 1-7页

作者： Besfat, Henok Mezemr Galmecha, Demissie Jobir Singh, Ram Sewak Mishra, Satyasis Electronics and Communication Engineering Department College of Electrical Engineering and Computing Adama Science and Technology University P.Box. 1888 Adama Ethiopia Department of ECE Centurion University of Technology and Management Gajapati India

Early and accurate detection of myocardial infarction (MI) is pivotal in enhancing cardiac function, as it can result in a heart attack. Detecting MI arrhythmias from electrocardiogram (ECG) signals presents challenges in accurately extracting patterns, such as Q, R, S and T wave segments. This paper proposes an efficient automated MI detection based on optimized Stockwell transform (OptST), Renyi entropy, dispersion entropy, and extreme learning machine (ELM) algorithm. The OptST is employed to decompose the ECG signal to four frequency band. The renyi and dispersion entropy features are then extracted in each frequency band for training and validation of ELM model to classify the five MI diseases. To identify clinically significant features, a Wilcoxon rank sum test is conducted, and to test the robustness of the proposed method 5-fold cross-validation has been applied. The simulation results have demonstrated that the proposed model has achieved a highest accuracy of 96.86%, specificity of 98.04%, recall of 92.16%, and F-score of 92.18% for sine activation function at hidden number of 300. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Dispersion entropy ELM Myocardial infarction Renyi entropy Stockwell transform

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AI-Powered Demand Response Mechanisms for Sustainable Smart Grids 5

AI-Powered Demand Response Mechanisms for Sustainable Smart ...

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5th International Conference on electronics and Sustainable Communication Systems, ICESC 2024

作者： Krishnan, R. Santhana Ashokkumar, C. Nithila, Ezhil E. Soundiraraj, N. Malar, P.Stella Rose Rani, P. Alice Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Dindigul India Jp College of Engineeringology Department of Electronics and Communication Engineering Thenkasi India Sarah Tucker College Department of Tamil Tirunelveli India

ISBN: (纸本)9798350379945

The integration of renewable energy sources into smart grids is a critical strategy for achieving sustainable energy systems. This necessitates effective management of energy resources, demand, and grid operations to fully exploit the potential of renewables while ensuring grid stability and reliability. Demand response (DR) mechanisms play a key role in optimizing energy use and grid stability within smart grids. This research explores the application of artificial intelligence (AI) for developing advanced DR mechanisms. The proposed approach leverages machine learning techniques to analyze energy consumption patterns and predict future demand. This enables the implementation of intelligent DR programs that incentivize consumers to adjust their energy usage during peak demand periods. By optimizing energy consumption in response to grid conditions, AI-powered DR mechanisms can significantly contribute to the integration of renewable energy sources and the creation of a sustainable energy future. The research investigates the design and implementation of these AI-powered DR mechanisms. In the proposed research, Machine learning algorithms are employed to analyze historical energy consumption data and predict future demand with high accuracy. This enables the development of DR programs that are tailored to anticipated grid conditions. Also, AI algorithms continuously monitor grid conditions and dynamically adjust DR incentives in real-time. This ensures that DR programs effectively address fluctuations in energy demand and supply. The research explores methods to incentivize consumer participation in DR programs. This may involve offering rebates or other benefits to consumers who adjust their energy consumption during peak demand periods. The effectiveness of the proposed AI-powered DR mechanisms will be evaluated through extensive simulations. The simulations will compare the performance of the proposed approach with existing DR methods. Key metrics such as forecas

关键词： Demand response

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Noise Canceling-FPPT for PV Systems Under Fluctuating Irradiance Conditions 24

Noise Canceling-FPPT for PV Systems Under Fluctuating Irradi...

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24th EEEIC International Conference on Environment and Electrical engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

作者： Yuan, Jiang Tian, Zhongbei Ma, Jieming Man, Ka Lok Xi'an Jiaotong-Liverpool University Department of Computing Suzhou China University of Liverpool Department of Electrical Engineering and Electronics Liverpool United Kingdom

ISBN: (纸本)9798350355185

The Flexible Power Point Tracking (FPPT) algorithm is vital in managing the active power output of grid-connected photovoltaic (PV) systems to align with grid require-ments. However, conventional FPPT algorithms encounter chal-lenges with tracking misjudgments under fluctuating irradiance conditions. To enhance control of photovoltaic systems in such fluctuating environments, this paper proposes a novel Noise-Canceling FPPT (NC-FPPT) algorithm. The proposed algorithm distinguishes itself by its ability to extract the noise component within real-time irradiance measurements, accurately estimate the resultant PV power changes due to irradiance noise, and apply a power bias to cancel out noise-induced power changes, thereby minimizing the risk of misjudgment of flexible power points. Through experimental validation, the NC-FPPT algorithm has been shown to decrease the tracking error by up to 8.12 % and maintains power closer to ideal reference when compared to traditional FPPT algorithm. © 2024 IEEE.

关键词： Solar irradiance

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A hybrid approach based on companding and PTS methods for PAPR reduction of 5G waveforms

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International Journal of electronics Letters 2024年第4期12卷 330-342页

作者： Ramavath, Srinivas Samal, Umesh Chandra Appasani, Bhargav Khan, Mohammad S. School of Electronics Engineering Kalinga Institute of Industrial Technology (KIlT) University Bhubaneswar India Department of Computing East Tennessee State University Johnson United States

As a crucial enabler for the fifth-generation air interface, the filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) is being investigated by several researchers. This article suggests using partial transmit sequence (PTS) and companding to create a hybrid peak-to-average power ratio (PAPR) reduction strategy for FBMC/OQAM signals. In the novel PTS Companding scheme, there are several segments to the data block signal, and the overlapping factor determines the number of data blocks in each segment. To obtain the lowest signal power in each segment, we choose the best data block to broadcast and examine the next overlapping data block concurrently. After that, the segment FBMC/OQAM signal peaks are compressed using companding. Based on simulation results and analysis, the proposed hybrid PTS Companding method in FBMC/OQAM systems may reduce PAPR more effectively than conventional PTS and Companding schemes. According to simulation results, the hybrid process can achieve around 5 dB greater PAPR performance than the conventional PTS and companding schemes. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： BER CCDF compander FBMC OQAM PAPR PSD PTS

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Revolutionizing Face Detection: VisionaryNet's Breakthrough in Unraveling Facial Camplexity 2

Revolutionizing Face Detection: VisionaryNet's Breakthrough ...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Nallarasan, V. Arivazhagan, N. Lakshminarayanan, R. Sharmila, D. Sangolgi, Nagaveni B Anand, M. Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chengalpattu Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Sharnbasva University Department of Electronics and Communication Engineering Kalaburagi India Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu Kattankulathur India

ISBN: (纸本)9798350387933

The initial step in facial recognition and analysis systems often involves facial detection. Traditional methods like Haar cascades and directed gradient histograms relied on manually designed features, limiting their effectiveness in diverse image environments. However, the emergence of deep learning in computer vision has accelerated the development of face detection frameworks, significantly improving accuracy. Detecting small-scale faces, varying positions, occlusions, blurs, and partially obscured faces in uncontrolled settings remains a persistent challenge in face identification. To address this, we propose a new approach called 'Visionary Net.' This single-stage face detector has undergone refinements, boosting both detection speed and accuracy. We tested Visionary Net on two well-known datasets: Mega Face and CelebA. Notably, on the Mega Face benchmark, our method achieved an impressive AP of 48.5 at a speed of 15.2 FPS using single-scale inference and an exceptional AP of 52.7 with multi-scale inference. Implementing it with the PyTorch framework resulted in a remarkable 97.3% accuracy in successfully detecting faces. Our comprehensive experiments show that Visionary Net surpasses existing models, demonstrating superior detection and recognition capabilities across various evaluation metrics. This innovative algorithm holds significant promise for robust face detection in challenging real-world scenarios. © 2024 IEEE.

关键词： Face recognition

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Enhancing Driver Safety Using Deep Learning Models 5

Enhancing Driver Safety Using Deep Learning Models

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Shokeen, Ishaan Uday, Krishnendu M Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Computer Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

This proposed work emphasizes the critical role of eye movement detection, particularly open and closed eye states, in identifying drowsiness, providing a clear focus on the project's main technological approach and its importance for road safety. The proposed model ensures constant checks on the driver's safety and provides necessary observations on their behavioral activities. To ensure effective drowsiness detection, different methodologies such as Convolutional Neural Networks (CNN), customized Neural Networks and Recurrent Neural Networks (RNN) were employed for image processing. The CNN model achieved the best results. Two different data sources from Kaggle provided various training images for the model to operate in different scenarios. The proposed model aims to achieve accurate predictions across various case scenarios and to develop the most efficient model for real-time detection and alert systems, ensuring driver safety. Among the models our system gives the best result using CNN reporting an accuracy of 98.53%, and precision, recall and F-score of 98.71%, 98.04% and 98.37% respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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