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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Chowdhury, Safiul Haque Mamun, Mohammad Hossain, Muhammad Minoar Hossain, Mohammad Ibrahim Iqbal, Md. Sadiq Kashem, Mohammod Abul Bangladesh University Department of Computer Science and Engineering Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering and Technology Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350388282

Newborn weight refers to the weight of a baby at the time of birth. It is typically measured in units such as pounds and ounces or kilograms. The weight of a newborn is an essential metric used by healthcare professionals to assess the health of an infant and its development. This research emphasizes the prediction of newborn weight using Machine Learning (ML) and Explainable Artificial Intelligence (XAI). The proposed approach involves preprocessing a dataset encompassing various parameters of newborns and their mothers during pregnancy. The primary objective is to develop an automated system capable of accurately predicting the weight of a newborn. Employing a range of regression ensemble ML models, namely Bootstrap Aggregating regression (BAGGINGR), Random Forest Regression (RFR), Ensemble Voting Regression (VOTIINGR), Extreme Gradient Boosting Regression (XGBOOSTR), Stacked Generalization Regression (STACKINGR), and Gradient Boosting Decision Trees Regression (GBDTR). This research develops a cross-validation approach, and notably, the GBDT model emerges as the top performer, yielding impressive results with average metrics Mean Squared Error (MSE) of 247.26, Mean Absolute Error (MAE) of 12.29, R-squared (R2) of 0.23, Peak Signal-to-Noise Ratio (PSNR) at 20.99 dB, and Signal-to-Noise Ratio (SNR) at 48.65 dB. The outcomes of the research are interpreted through Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) in XAI, emphasizing the significance of interpreting these parameters. The proposed research has valuable insights to enhance the long-term health conditions of newborns, reduce mortality rates, and provide crucial support to healthcare professionals. © 2024 IEEE.

关键词： Machine learning

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Classification of plant diseases in images using dense-inception architecture with attention modules

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Multimedia Tools and Applications 2024年 1-26页

作者： Khan, Sultan Daud Basalamah, Saleh Naseer, Atif Department of Computer Science National University of Technology Islamabad Pakistan Department of Computer Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Umm Al-Qura University Makkah Saudi Arabia

Agriculture plays a vital role in providing food to a growing world population. However, plant diseases and pests result in 50% reductions in crop yields, which exacerbates poverty and threatens a sustainable food system. Currently, plant disease detection mainly relies on manual visual inspection and analysis of the field, which is a time-consuming and unreliable method. Modern technology, such as smart agriculture, can leverage machine learning, computer vision, and remote sensing technologies to overcome the limitations of traditional methods. Several machine learning algorithms have been used for plant disease classification and detection. However, these models suffer from limitations such as dependence on complex hand-crafted features for classification, and limited adaptability to new data. Deep learning models, on the other hand, have the ability to automatically learn hierarchical representations from raw data, making them inherently more informative and adaptable to changing data patterns. While existing deep learning approaches for plant disease detection have achieved some degree of success, these models still face challenges in achieving superior performance due to similar visual appearances of different plant diseases, intra-class differences, and variation in scale and resolution. To address these challenges, we proposed a framework that consists of hybrid network and attention module. Our hybrid module combines the benefits of DenseNet and InceptionNet to capture multi-scale features, while the attention module addresses the challenges of inter-class similarity and intra-class variability by isolating the most discriminative feature and disregarding extraneous and insignificant information. We performed experiments on publicly available challenging dataset. From Experimental results, we demonstrate the effectiveness of the proposed framework in achieving high accuracy in plant disease detection. © The Author(s), under exclusive licence to Springer Sci

关键词： Agriculture

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An intelligent active probing and trace-back scheme for IoT anomaly detection

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Digital Communications and Networks 2024年第1期10卷 168-181页

作者： Luying Wang Lingyi Chen Neal N.Xiong Anfeng Liu Tian Wang Mianxiong Dong School of Computer Science and Engineering Central South UniversityChangsha 410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA Artificial Intelligence and Future Networks Beijing Normal University&UICZhuhai 519087China Department of Information and Electronic Engineering Muroran Institute of TechnologyMuroran 050-8585Japan

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data *** Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.

关键词： Anomaly detection Internet of things Integrating data collection Mobile edge users Intelligent

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Hepatitis C Prediction Using Machine Learning and Deep Learning-Based Hybrid Approach with Biomarker and Clinical Data

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Biomedical Materials and Devices 2025年第1期3卷 558-575页

作者： Ripa, Rokiya Uddin, Khandaker Mohammad Mohi Alam, Mir Jafikul Rahman, Md. Mahbubur Department of Computer Science and Engineering Dhaka International University Dhaka 1205 Bangladesh Department of Computer Science and Engineering Southeast University Dhaka 1208 China Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka 1216 Bangladesh

Chronic liver damage is believed to be mostly caused by the Hepatitis C virus (HCV). About 90% of hepatitis C infections progress to chronic hepatitis. Acute HCV infection is a condition that frequently progresses to liver cirrhosis and eventually liver cancer;therefore, understanding this stage of the virus is essential. Molecular and serological testing approaches are often expensive and difficult to perform for diagnosing HCV infection. Machine learning technology can be effectively employed to identify patterns or associations for diagnosing HCV infection. The study utilized machine learning techniques to develop classification models for hepatitis C illness, aiming to anticipate the virus responsible for the infection. Our research integrates various machine learning algorithms, including Random Forest, Cat Boost, Bagging Classifier, SGD Classifier, Gaussian NB, Bernoulli NB, Multinomial NB, Linear Discriminant Analysis, ANN, and MLP. Prior to training the models, preprocessing methods, including normalization, filtering, and SMOTE, were used to improve the dataset’s attributes. The classification accuracy scores show encouraging results, with ANN scoring 79.63%, MLP scoring 46.29%, Bernoulli NB scoring 79.62%, Cat Boost scoring 98.14%, and Random Forest scoring 99.53%. Among classification methods, random forest demonstrates the highest accuracy in diagnosing HCV infection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification Deep learning Hepatitis C virus Machine learning Predictions

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai201804 China Ant Group Shanghai200122 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Fudan University School of Computer Science Shanghai200438 China Tongji University Department of Control Science and Engineering Shanghai201804 China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne3122 Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this article, we propose a hypergraph learning framework named learning from heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterward, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experimental results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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An improved LOS Guidance and Sail-Rudder Cooperative Controller for the Path Tracking of Unmanned Sailboats

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Qin, Hongde Xu, Peilong Zhang, Fumin Xue, Yifan Science and Technology on Underwater Vehicle Laboratory Harbin Engineering University Harbin China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay China

Unmanned sailboats are driven only by wind, making them good platforms for the synchronous observation of air-sea interfaces over a large range. Compared with traditional unmanned ships, the unmanned sailboat involves simultaneous sail and rudder control for path tracking in unpredictable marine environments. The system is characterized by strong coupling and nonlinearity, creating challenges for the design of controllers. This paper combines line-of-sight (LOS) guidance with the introduction of a sideslip angle observer and model predictive control. A high-precision path tracking strategy suitable for cooperative sail and rudder control for unmanned sailboats is proposed. First, considering the lateral error easily caused by the large sideslip angle of sailboats, a full-path fixed-time guidance strategy with double fixed-time sideslip angle observers (DFSO) is proposed. Second, different from the previous strategy of decoupling the sail and rudder to control the speed and heading, the proposed cooperative control framework uses Lyapunov-based model predictive control (LMPC). The sailing speed and heading angle are both accounted for in the objective function, and the stability is verified by Lyapunov theory. Finally, the feasibility and superiority of this proposed method are confirmed by numerical simulation experiments involving the path tracking of a four degree of freedom sailboat model integrated with wind and waves in an ocean environment. IEEE

关键词： Model predictive control

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Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

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Frontiers of Structural and Civil engineering 2024年第10期18卷 1507-1523页

作者： Umer Sadiq KHAN Muhammad ISHFAQUE Saif Ur Rehman KHAN Fang Xu Lerui CHEN Yi LEI School of Computer and Information Science Hubei Engineering UniversityXiaogan 432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan 432000China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Aviation Zhongyuan University of TechnologyZhengzhou 451191China School of Civil Engineering Central South UniversityChangsha 410083China

Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack *** deep-learning models are trained on 192 crack *** research aims to provide up-to-date detecting techniques to solve dam crack *** finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%*** study’s pre-trained designs help to identify and to determine the specific locations of cracks.

关键词： concrete dam borehole closed-circuit television deep learning models crack detection water resources management management

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Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

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computer Modeling in engineering & sciences 2024年第9期140卷 2397-2424页

作者： Hongchi Liu Xing Deng Haijian Shao School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89119USA

The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various *** methodologies have emerged as pivotal components of image preprocessing,fostering an improvement in the quality of remote sensing *** enhancement renders remote sensing data more indispensable,thereby enhancing the accuracy of target *** defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed *** response to this challenge,a novel UNet Residual Attention Network(URA-Net)is *** paradigmatic approach materializes as an end-to-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump *** essence of our methodology lies in local feature fusion within dense residual clusters,enabling the extraction of pertinent features from both preceding and current local data,depending on contextual *** intelligently orchestrated gated structures facilitate the propagation of these features to the decoder,resulting in superior outcomes in haze *** validation through a plethora of experiments substantiates the efficacy of URA-Net,demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image *** the RICE-1 dataset,URA-Net achieves a Peak Signal-to-Noise Ratio(PSNR)of 29.07 dB,surpassing the Dark Channel Prior(DCP)by 11.17 dB,the All-in-One Network for Dehazing(AOD)by 7.82 dB,the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing(OTM-AAL)by 5.37 dB,the Unsupervised Single Image Dehazing(USID)by 8.0 dB,and the Superpixel-based Remote Sensing Image Dehazing(SRD)by 8.5 *** noteworthy,on the SateHaze1k dataset,URA-Net attains preeminence in overall performance,yieldi

关键词： Remote sensing image image dehazing deep learning feature fusion

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

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computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali Department of Computer Science and Information Technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical *** primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune *** use of IIF for detecting autoimmune diseases is widespread in different medical *** 80 different types of autoimmune diseases have existed in various body *** IIF has been used for image classification in both ways,manually and by using the computer-Aided Detection(CAD)*** data scientists conducted various research works using an automatic CAD system with low *** diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)*** baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune *** technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the *** the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of *** augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL *** models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,***,DenseNet-121 shows the highest performance with suitable analysis of autoimmune *** overall performance highlighted that TL is a suitable and enhanced technique compared to its ***,the proposed technique is used

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

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