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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Biplob, Md. Badiuzzaman Haque, Md. Mokammel Computer Science and Engineering Department Chittagong University of Engineering and Technology Chattogram Bangladesh Computer Science and Engineering Department Faculty of Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9789819989362

One of the most significant and difficult tasks in the modern world is rainfall forecast. Rainfall is a complicated and nonlinear phenomenon that requires sophisticated computer modeling and simulation to anticipate with any degree of accuracy. In many regions of the world, daily rainfall totals are dispersed based on various frequency distribution functions. Rainfall has become a significant factor in agricultural countries. We provide a machine learning-based framework for forecasting the total monthly precipitation. In this study, three Bangladeshi weather stations’ daily rainfall was forecasted with a 365-day lead time using backpropagation long short-term memory (LSTM), random forest, and neural network algorithms. The predicted results from the selected algorithms were compared with the observed data to determine prediction precision by mean absolute error (MAE) test results. We found that selected algorithms predicted daily rainfall with reasonable accuracy. Therefore, year-long, month-long, and day-long rainfall can be predicted using these models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(Information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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An IoT-Based Energy Conservation Smart Classroom System

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Intelligent Automation & Soft Computing 2023年第3期35卷 3785-3799页

作者： Talal H.Noor El-Sayed Atlam Abdulqader M.Almars Ayman Noor Amer S.Malki College of Computer Science and Engineering Taibah UniversityYanbu46421-7143MedinahKSA Department of Computer Science Faculty of ScienceTanta UniversityTantaEgypt College of Computer Science and Engineering Taibah UniversityMedinahKSA

With the increase of energy consumption worldwide in several domains such as industry,education,and transportation,several technologies played an inﬂuential role in energy conservation such as the Internet of Things(IoT).In this article,we describe the design and implementation of an IoT-based energy conser-vation smart classroom system that contributes to energy conservation in the edu-cation *** proposed system not only allows the user to access and control IoT devices(e.g.,lights,projectors,and air conditions)in real-time,it also has the capability to aggregate the estimated energy consumption of an IoT device,the smart classroom,and the building based on the energy consumption and cost model that we ***,the proposed model aggregates the estimated energy cost according to the Saudi Electricity Company(SEC)***,the model aggregates in real-time the estimated energy conservation percentage and estimated money-saving percentage compared to data collected when the system wasn't *** feasibility and beneﬁts of our system have been validated on a real-world scenario which is a classroom in the college of computer science and engineering,Taibah University,Yanbu *** results of the experimental studies are promising in energy conservation and cost-saving when using our proposed system.

关键词： Energy consumption energy conservation energy cost Internet of Things(IoT) smart classroom

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New improved model for joint segmentation and registration of multi-modality images: application to medical images

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8755-8770页

作者： Badshah, Noor Begum, Nasra Rada, Lavdie Ashfaq, Muniba Atta, Hadia Department of Basic Sciences and Islamiat University of Engineering and Technology Peshawar Pakistan Faculty of Engineering and Natural Science Bahcesehir University Istanbul Turkey Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Department of Mathematics Islamia College Peshawar Pakistan

Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively. © 2024 - IOS Press. All rights reserved.

关键词： Image segmentation

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Revolutionizing Diagnostic Precision: The Role of Neural Networks in Advancing Medical Imaging 1

Revolutionizing Diagnostic Precision: The Role of Neural Net...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Raymond, David Goureshettiwar, Palash Kumar, Praveen Faculty of Engineering and Technology Depertment of Computer Science and Medical Engineering Nagpur India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Name of Organization Nagpur India

ISBN: (纸本)9798350360707

The incorporation of neural networks into medical imaging has recently resulted in significant modifications to diagnosis. This article looks at the job of brain networks in clinical picture handling, featuring their enhancements in exactness, speed and consistency across different clinical spaces. The utilization of profound learning models, for example, CNN and RNN has become significant in picture examination, illness recognizable proof, and treatment arranging. The exhaustive review investigates the elements of brain network processes and their applications in different imaging modalities like radiology, pathology and neuroimaging. Also, it tends to the difficulties, development possibilities, and moral contemplations related with conveying brain networks in true medical services conditions. As brain networks keep on changing clinical pictures, this examination fills in as a complete aide, showing their significant effect on further developing diagnostics and reconsidering clinical imaging © 2024 IEEE.

关键词： Convolutional neural networks

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Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

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computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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Impact of Approximate QRS Identification on RR Interval-Based Arrhythmia Detection and Classification 17

Impact of Approximate QRS Identification on RR Interval-Base...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Nagatomo, Taiki Sato, Toshinori Graduate School of Engineering Fukuoka University Department of Electronics Engineering and Computer Science Japan Faculty of Engineering Fukuoka University Department of Electronics Engineering and Computer Science Japan

ISBN: (纸本)9798331507398

Wearable devices for arrhythmia diagnosis are battery-powered and require improved power efficiency. In our previous study, we applied approximate computing, which is effective for power reduction, to QRS identification algorithm and achieved significant power reduction. However, the impact of this approximation on arrhythmia detection and classification was not fully investigated. In this study, we investigate how the approximation - - specifically, the use of two approximate multipliers and eight approximate squarers applied to the Pan-Tompkins algorithm - - affects RR interval-based arrhythmia detection and classification. We use 48 samples from the MIT-BIH Arrhythmia Database to evaluate the accuracy of arrhythmia detection and classification. It was found that the approximate multiplier mul16s_GQU is the best choice, achieving nearly 100% accuracy and less than 1 % false negatives in abnormal/normal classification. Additionally, the most power-efficient approximate squarer, E10C1, achieves relatively high accuracy with less than 5 % false negatives. © 2024 IEEE.

关键词： approximate computing arrhythmia biomedical electronics electrocardiogram QRS complex

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Intelligent Real-Time Hypoglycemia Prediction for Type 1 Diabetes 2

Intelligent Real-Time Hypoglycemia Prediction for Type 1 Dia...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Mahdy, Nevien A. Elnokrashy, Ahmed F. Hammad, D.A. Mohamed, Wael A. Benha University Benha Faculty of Engineering Electrical Engineering Department Benha Egypt Nile University Faculty of Information Technology and Computer Science Computer Science Department Giza Egypt Benha University Benha Faculty of Engineering Basic Engineering Sciences Department Benha Egypt

ISBN: (纸本)9798350362633

Hypoglycemia in Type 1 Diabetes (T1D) refers to a condition where blood glucose (BG) levels drop to abnormally low levels, typically below 70 mg/dL. This can occur when there is an excessive amount of insulin relative to the blood glucose level, leading to an imbalance that can be dangerous and potentially life-threatening if not promptly treated. The availability of large amounts of data from continuous glucose monitoring (CGM), insulin doses, carbohydrate intake, and additional vital signs, together with deep learning (DL) techniques, has revolutionized algorithmic approaches for BG prediction in T1D, achieving superior performance. In our study, we employed a Long Short-Term Memory (LSTM) neural network architecture to predict hypoglycemia events in patients with T1D. For the training and testing, we utilized the OhioT1DM (2018) dataset. In addition, real-time data collected from an individual patient for the evaluation. This patient utilized the CGM FreeStyle Libre (FSL) system, along with a smartwatch to monitor step count. The LSTM model exhibited performance demonstrating exceptional levels of sensitivity, specificity, and accuracy scores of 97.09%, 94.17%, and 95.63%, respectively, when assessed using the Ohio test dataset. Our research provides strong evidence supporting the system's efficacy in managing hypoglycemia events in individuals diagnosed with T1D. © 2024 IEEE.

关键词： Classification Deep learning Glucose prediction Hypoglycemia Type 1 diabetes

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