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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Ahmed, Asif Rahman, S. M. Rizwanur Chowdhury, Nur Mohammad Rahman, Md Jillur Uddin, Mohammed Fahim Khan, Md Tanvir Rahman Technische Universität Chemnitz Faculty of Computer Science Chemnitz Germany Louisiana Tech University Department of Computer Science Ruston United States University of Asia Pacific Department of Computer Science & Engineering Dhaka Bangladesh Maharishi International University Department of Computer Science IA United States

ISBN: (纸本)9798350367904

In daily rescue missions, Unmanned Aerial Vehicles (UAVs) are vital. Finding propeller damage in UAVs is a critical first step in guaranteeing safe operation and endurance. Because a faulty UAV might endanger people nearby, coexisting with humans and UAVs brings additional concerns. In Addition, by promptly and effectively identifying these problems, the risk to human life can be minimized to the greatest extent possible by providing neighbors with an early warning. The main purpose of this study is to develop an inspection model to classify healthy and broken propellers through audio sound generated by the drone propeller. In addition, different audio data augmentation mechanisms are used to increase the data volume size. We have applied two approaches to train the model: passive learning and active learning. Furthermore, four different CNN-based architectures are used to develop the acoustic inspection models which were trained with three different datasets to find the model's performances and their training accuracy and loss. In the passive deep learning approaches, the ResNet-50 inspection model has attained the best accuracy which is 88.54% by using the entire dataset. In contrast, the ResNet-50 inspection model obtained 85.86% model accuracy in the active deep learning approach, the highest accuracy among the iterations. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Short-Term Memory Algorithm 5

Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Sho...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Behera, Mandakini Mishra, Debahuti Sarangi, Archana Sarangi, Shubhendu Deemed to be University School of Computer Engineering Bhubaneswar India University Department of Computer Science & Engineering Bhubaneswar India University Department of Electronics and Telecommunications Bhubaneswar India University Faculty of Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

According to the survey of International Diabetes Federation (IDF) the number of persons with diabetes is continuously increasing. There are currently 3820 million diabetics worldwide, and during the next 15 years, this figure might double. Diabetes can create several life-threatening diseases, including heart attack, renal failure, and stroke. To sustain a healthy life, early diabetes identification is crucial. machine learning (ML) is a approach which automatically learns from experience to improve performance and produce more accurate predictions, that can be used to forecast diabetes. Deep learning is a branch of machine learning, and the human brain inspires both its architecture and the way it *** manuscript presents a hybrid method that combines peephole LSTM and 1D CNN to improve the accuracy of diabetes prediction. Performance is evaluated based on F1 score, accuracy, specificity, sensitivity, and epoch-wise accuracy and *** Pima Indian diabetes dataset is utilized for experimental validation. Experimental results demonstrate high prediction accuracy of 96.74% on training and 75.32% on testing for diabetes prediction. © 2025 IEEE.

关键词： Deep learning

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Intrusion Detection System Focused on Deep Learning for Mobile IoT Networks 24

Intrusion Detection System Focused on Deep Learning for Mobi...

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10th International Conference on Computing and Artificial Intelligence, ICCAI 2024

作者： Aljumah, Abdullah Ahanger, Tariq Ahamed College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Saudi Arabia School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW Australia

ISBN: (纸本)9798400717055

Detection mechanisms for intrusion play a key role in the identification of vulnerable behaviors that denigrate IoT networks. Mobile Adhoc Networks (MANETs) are wireless networks with the ability to transmit data without the need for infrastructure to run them. Recently, the IoT networking model has arisen, which is a superset of the above conceptualization. Its fragmented existence and minimal availability of resources pose one of the major issues to provide secure data communication. Intrusion Detection System (IDS) is indispensable for acclimatizing with difficulties. Researchers have traditionally used a heuristic approach focused on the heterogeneity of appropriately classified cases (CCIs), with a cumulative estimate of fluctuation (AMoF). It consists of 2 phases;phase-1 accumulates values by dedicated sniffers (DSs) to produces the CCI which is further transmitted to the supernode (SN). SN conducts the linear regression procedure over the CCIs acquired from DSs in phase-2 to separate the benevolent and vulnerable nodes. For various extreme network situations, the identification characterization is provided in this work using Gauss Markov (GM), and Random waypoint (RWP). In the enhanced velocity environment, detection rates surpass 98%, whereas, 90% is registered for limited speed. © 2024 ACM.

关键词： Network intrusion

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Cooperative Spectrum Sensing: A Multi-Channel Deep Learning Method 18

Cooperative Spectrum Sensing: A Multi-Channel Deep Learning ...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zha, Ziwei Jin, Ming He, Ming Jiang, Tao Yuan, Tianxi Qiu, Keyan Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350312201

In this paper, we studied the cooperative spectrum sensing (CSS) problem based on deep learning. The current CSS methods based on deep learning only extract general features of signal samples, without considering the specific details of real and imaginary parts of the signal samples. In order to fully extract features of the signal samples, we propose a multi-channel deep neural network (MCDNN) for CSS, the proposed method firstly utilizes three neural network branches to separately extract the features from real part, imaginary part, combination of real and imaginary parts of signal samples. Simulation results indicate that the detection performance of this method is better than other detection methods, especially in low signal-to-noise ratio (SNR) situations. Additionally, this method is robust to time-varying non-uniform received signals and noise power. © 2023 IEEE.

关键词： Deep neural networks

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A Safeguard Agent for Intelligent Health-care Environments

A Safeguard Agent for Intelligent Health-care Environments

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Alzahrani, Abdulkreem Al Baha University Faculty of Computer Science and Information Technology Department of Computer Engineering and Science Al Baha Saudi Arabia

ISBN: (纸本)9798350347050

Intelligent environments rely heavily on the Internet of Things, which can be targeted by malicious attacks. Therefore, the autonomous capabilities of agents in intelligent health-care environments, and the agents' characteristics (accuracy, reliability, efficiency and responsiveness), should be exploited to devise an autonomous intelligent agent that can safeguard the entire environment from malicious attacks. Hence, this paper contributes to achieving this aim by selecting the eight most valuable features out of 50 features from the adopted dataset using the Chi-squared test. Then, three well-known machine learning classifiers (i.e. naive Bayes, random forest and logistic regression) are compared in classifying malicious attacks from non-attacks in an intelligent health-care environment. The highest achieved classification accuracy was for the random forest classifier (99.92%). © 2023 IEEE.

关键词： Internet of things

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DBPR: Dynamic Bidirectional Propagation Relationship Graph Convolution Network for Fake News Detection on Social Media

DBPR: Dynamic Bidirectional Propagation Relationship Graph C...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Zihang Pan, Shanliang Yang, Ze Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350337020

With the emergence of social networks and digital media, there has been an increasing proliferation of channels through which people receive information, potentially resulting in the widespread dissemination of fake news. Previous approaches to detecting fake news have primarily focused on mining textual features, while neglecting the dynamic spatiotemporal characteristics of news propagation. In this paper, we propose a novel Dynamic Bidirectional Propagation Relationship (DBPR) model that integrates sequential propagation graphs and backtracking trees in the temporal dimension, incorporating positional encoding based on the temporal order of news propagation to more effectively capture its dynamic nature. Extensive experiments conducted on two publicly available datasets demonstrate that our proposed approach achieves state-of-the-art performance, particularly by providing valuable insights for early detection of fake news. © 2023 IEEE.

关键词： fake news detection graph neural network news propagation social media

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Butler Matrix Based on Dual-Mode Transmission Lines for Millimeter Wave Dual-Polarized Applications

Butler Matrix Based on Dual-Mode Transmission Lines for Mill...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Dong, Chunli Shen, Shuhao Hu, Yang Sun, Liying Ye, Min You, Yang Lu, Yunlong Huang, Jifu Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9781733509657

This paper proposes a dual-mode Butler matrix based on the air suspended line (ASL) for millimeter-wave (mm-wave) dual-polarized antenna arrays. The ASL, which can simultaneously support TEM-mode and TE10-mode, is used to design 90° hybrid coupler, 0 dB crossover, phase shift, and construct the dual-mode Butler matrix with high integration and low loss. A 4×4 dual-mode Butler matrix which is operating in mm-wave band is designed. Simulation results show that the reflection coefficients of this design are better than -15 dB in the range of 26.5 to 29.5 GHz, and have stable amplitude and phase response. The proposed butler matrix has wide application potential in 5G mm-wave dual-polarized multi-beam antenna arrays. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Butler matrix

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