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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

SN computer Science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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CardioNetFusion: A Deep Learning and Explainable AI Integrated System for Cardiovascular Disease Detection 24

CardioNetFusion: A Deep Learning and Explainable AI Integrat...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Raja, Gunasekaran Essaky, Selvam Muthu, Surya Raj Suresh, Babith Sarish Cho, Jaehyuk Yoo, Seohyun Easwaramoorthy, Sathishkumar Veerappampalayam Anna University NGNLab Department of Computer Technology Chennai India Jeonbuk National University Department of Software Engineering Division of Electronics and Information Engineering Jeonju-si Korea Republic of Sunway University School of Engineering and Technology Malaysia

ISBN: (纸本)9798331530631

Early detection of Cardiovascular Diseases (CVD) plays a vital role in effective treatment and management. However, traditional methods for analyzing ECG signals are often limited in both accuracy and interpretability. In this work, we introduce a novel model, CardioNetFusion, designed to address these limitations by incorporating advancements in Deep Learning (DL) and Explainable AI (XAI). The model leverages the strengths of Convolutional Neural Networks (CNN), MobileNetV2, and VGG16 to improve the robustness of ECG signal interpretation. To further enhance transparency and provide healthcare professionals with actionable insights, saliency maps are utilized to visualize model predictions. Additionally, by integrating an Application Programming Interface (API) within an IoT-based sensor fusion framework, the model supports real-time cardiovascular health monitoring. Achieving a classification accuracy of 98.7% in detecting arrhythmias, CardioNetFusion surpasses existing methodologies and offers a practical, reliable solution for early CVD diagnosis and management. Traditional methods of ECG analysis often have limitations in terms of diagnostic accuracy and interpretability. This paper proposes a CardioNetFusion model to address the challenges of conventional methods of ECG data analysis by leveraging advances in Deep Learning (DL) and Explainable AI (XAI). The proposed CardioNetFusion model integrates Convolutional Neural Networks (CNN), MobileNetV2, and VGG16 models to improve the robustness of ECG signal interpretation. Additionally, we utilized saliency maps to enhance model transparency and provide clear, actionable insights for healthcare professionals. Integrating Application Programming Interface (API) in the IoT-based sensor fusion approach to our proposed model facilitates seamless real-time monitoring of cardiovascular health. With an impressive 98.7% accuracy in arrhythmia classification, CardioNetFusion outperformed existing methods and ensured reliab

关键词： Convolutional neural networks

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A Multi-Dimensional Deep Learning Approach for Enhanced Cardiovascular Disease Diagnosis using ECG Signals 8

A Multi-Dimensional Deep Learning Approach for Enhanced Card...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Evangelin Sonia, S.V. Nedunchezhian, R. Rajalakshmi, M. Kavya, S.P. Parveen, H. Summia Jerusha Miraclin Dulcie, B. Karunya Institute of Technology and Sciences Division of Cse Coimbatore India Coimbatore Institute of Technology Computer Science and Engineering Coimbatore India Coimbatore Institute of Technology Information Technology Coimbatore India Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar college of Engineering Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

The diagnosis of cardiovascular disease is essential for timely clinical intervention and patient prognose. Although electrocardiogram is performed frequently for cardiovascular disease diagnosis, its interpretation requires experts. This study first introduces a Multi-Dimensional Deep Neural Network aimed to improve the accuracy and efficiency for cardiovascular disease diagnosis from electrocardiogram data. The Multi-Dimensional Deep Neural Network architecture of our electrocardiogram signal model, which coalesces convolutional and recurrent neural networks (layers) to learn spatial/temporal features. We developed the model on a large annotated dataset of electrocardiogram recordings using training and validation datasets, yielding personal performance compared to baseline methods as well as known deep learning models. We summarize the various statistics-sensitivity, specificity;overall accuracies-that are calculated for our Multi-Dimensional Deep Neural Network indicating that our model is both robust and reliable in terms of diagnostics. This research could lead to more advanced neural networks in medical diagnostics, which may provide clinicians with an effective tool for the clinical diagnosis of cardiovascular diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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The Effectiveness of CNN-Based Models in Macroscopic Motion Direction Detection 6

The Effectiveness of CNN-Based Models in Macroscopic Motion ...

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Qiu, Zhiyu Chen, Tianqi Hua, Yuxiao Todo, Yuki Graduate School of Natural Science & Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan

ISBN: (纸本)9798331527426

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical pattern recognition capabilities, CNNs have demonstrated superior performance over traditional machine learning methods. One particularly critical application of CNNs is in motion direction detection, which is essential for enhancing security in surveillance systems, ensuring safe navigation in autonomous vehicles, and providing valuable insights in sports analytics. In this study, our primary goal was to investigate how different types of input data impact the accuracy of motion direction detection. We applied five distinct preprocessing methods to test the effect of input data on accuracy. Furthermore, we conducted experiments in various object and background scenarios to evaluate adaptability of simple 4-layer CNN, EfficientNetBO and ResNet50 in detecting motion direction. Finally, we tested the noise resistance of these three models. The results indicate that CNNs are capable of extracting the macroscopic motion direction of objects in images. Our study highlights the potential of CNN-based models for efficient and effective motion direction detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

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computers, Materials & Continua 2023年第3期74卷 6263-6287页

作者： Arjmand Khaliq Syed Abdul Rahman Kashif Fahad Ahmad Muhammad Anwar Qaisar Shaheen Rizwan Akhtar Muhammad Arif Shah Abdelzahir Abdelmaboud Electrical Engineering Department University of Engineering and TechnologyLahore54700Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia Department of Information Science Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan Sub CampusPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance *** trend promotes the development of precise and efficient control schemes for individual *** research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)*** framework under consideration is developed in four *** begin,MATLAB Simulink was used to develop a detailed mathematical and electromechanical *** research presents a modified SVPWM inverter control *** tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are *** the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control *** reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data *** the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed *** speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 *** maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust *** to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

关键词： Space vector pulse width modulation linear induction motor proportional-integral controller indirect vector control electromechanical dynamic modeling

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A Comparative Performance Analysis of Android Malware Classification Using Supervised and Semi-supervised Deep Learning 16

A Comparative Performance Analysis of Android Malware Classi...

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16th International Conference on Security of information and Networks, SIN 2023

作者： Hossain, Md Sharafat Hossain, Md Delwar Rahman, Md Mahbubur Taenaka, Yuzo Islam, Md Saiful Kadobayashi, Youki Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh Division of Information Science Nara Institute of Science and Technology Nara Ikoma Japan Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350343212

Mobile phones were originally designed for commu-nication, wherein they have evolved into multifunctional devices used for financial transactions, social media, and more, making them an integral mode of communication for the dweller's world. However, the mostly used OS in mobile devices, Android consist of various vulnerabilities and lack sufficient security measures, leaving them susceptible to malware injection. Moreover, the attackers develop sophisticated malware, which is challenging to detect by traditional detection approaches. Henceforth, an effective malware detection method is imperative to ensure the safety and security of Android systems. In this study, we address the dynamic analysis of Android malware using supervised and semi-supervised deep neural network techniques to tackle existing challenges. Our investigation is conducted on the CCCS-CIC-AndMal-2020 dataset and the results reveal that our proposed supervised models (1D CNN, MLP, RNN and LSTM) outperform state-of-the-art supervised models significantly with an accuracy of 99.76%. Additionally, we explore semi-supervised approach using limited label data, where our label spreading approach showcases a highly effective detection accuracy of 97.26%, approaching that of a fully supervised approach. © 2023 IEEE.

关键词： Android malware

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Exploring A Novel Data Augmentation Strategy for Enhanced In-Vehicle Security Analysis 5

Exploring A Novel Data Augmentation Strategy for Enhanced In...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Shibly, Kabid Hassan Borhan, Rownak Akter, Lubana Based, Md. Abdul Nara Institute of Science and Technology Division of Information Science Japan Dhaka International University Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

In the sphere of automotive cybersecurity, navigating the complexities of behavior management within structured security databases poses significant challenges due to the heterogeneous distribution of data. This uneven data spread obscures a minority of identifiable attack instances amidst a larger pool of unlabeled data, thereby diminishing the performance of machine learning algorithms, particularly in aspects of data deterioration and equilibrium. Notwithstanding, the domain of semi-supervised learning, which leverages both labeled and unlabeled data for model development, remains relatively unexplored within the cybersecurity context. This work presents the Prototype Mixing Generative Network (PMGN), integrated with Multivariate Imputation by Chained Equations (MICE) imputation method and class conditioning, to tackle these problems. Specifically designed for semi-supervised learning, this method includes a new data augmentation technique to mitigate the inherent data imbalances found in security databases. In application across diverse security datasets, the PMGN methodology demonstrates superior performance over conventional methods, underscoring its enhanced efficacy. A major step forward in the field of semi-supervised learning for in-vehicle cybersecurity has been made with the integration of PMGN. Crucially, this research aligns with Industry 5.0 and Sustainable technology objectives by fortifying vehicular digital frameworks, a critical element in the evolution towards sustainable and intelligent transportation systems. © 2023 IEEE.

关键词： Cybersecurity

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Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

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computer Systems Science & engineering 2023年第6期45卷 3053-3077页

作者： Ernesto Lee Furqan Rustam Hina Fatima Shahzad Patrick Bernard Washington Abid Ishaq Imran Ashraf Department of Computer Science Broward CollegeBroward CountFloridaUSA Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Division of Business Administration and Economics Morehouse CollegeAtlantaGAUSA Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

With the increasing usage of drugs to remedy different diseases,drug safety has become crucial over the past few *** medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different *** diversification is both helpful and danger-ous as such medicine proves to be more effective or shows side effects to different *** clinical trials,side effects are reported when the medicine is used by the mass public,of which several such experiences are shared on social media platforms.A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been *** analysis of drug reviews has a large poten-tial for providing valuable insights into these ***,this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques.A dataset acquired from the‘***’contain-ing reviews of drug-related side effects and reactions,is used for experiments.A lexicon-based approach,Textblob is used to extract the positive,negative or neu-tral sentiment from the review *** classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory(CNN-LSTM)*** CNN is used at thefirst level to extract the appropriate features while LSTM is used at the second *** well-known machine learning models including logistic regression,random for-est,decision tree,and AdaBoost are evaluated using term frequency-inverse docu-ment frequency(TF-IDF),a bag of words(BoW),feature union of(TF-IDF+BoW),and lexicon-based *** analysis with machine learning models,long short term memory and convolutional neural network models,and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 *** also

关键词： Drug safety analysis lexicon-based techniques drug reviews sentiment analysis machine learning CNN-LSTM

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Performance-Guaranteed Solutions for Multi-Agent Optimal Coverage Problems using Submodularity, Curvature, and Greedy Algorithms 63

Performance-Guaranteed Solutions for Multi-Agent Optimal Cov...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Welikala, Shirantha Cassandras, Christos G. Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Boston University Division of Systems Engineering and Center for Information and Systems Engineering BrooklineMA02446 United States

ISBN: (纸本)9798350316339

We consider a class of multi-agent optimal coverage problems in which the goal is to determine the optimal placement of a group of agents in a given mission space so that they maximize a coverage objective that represents a blend of individual and collaborative event detection capabilities. This class of problems is extremely challenging due to the non-convex nature of the mission space and of the coverage objective. With this motivation, greedy algorithms are often used as means of getting feasible coverage solutions efficiently. Even though such greedy solutions are suboptimal, the submodularity (diminishing returns) property of the coverage objective can be exploited to provide performance bound guarantees. Moreover, we show that improved performance bound guarantees (beyond the standard (1-1 / e) performance bound) can be established using various curvature measures of the coverage problem. In particular, we provide a brief review of all existing popular applicable curvature measures, including a recent curvature measure that we proposed, and discuss their effectiveness and computational complexity, in the context of optimal coverage problems. We also propose novel computationally efficient techniques to estimate some curvature measures. Finally, we provide several numerical results to support our findings and propose several potential future research directions. © 2024 IEEE.

关键词： Optimal detection

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An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

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computer Systems Science & engineering 2023年第3期44卷 2457-2468页

作者： Mahesh B.Shelke Jeong Gon Lee Sovan Samanta Sachin N.Deshmukh G.Bhalke Daulappa Rahul B.Mannade Arun Kumar Sivaraman Department of Computer Science and Information Technology Dr.Babasaheb Ambedkar Marathwada UniversityAurangabadMaharashtra431004India Division of Applied Mathematics Wonkwang University460Iksan daeroIksan SiJeonbuk54538Korea Department of Mathematics Tamralipta MahavidyalayaTamlukWest Bengal721636India Department of Electronics and Telecommunication Engineering AISSMSCOEPuneMaharashtra411001India Department of Information Technology Govemment College of EngineeringAurangabadMaharashtra431005India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

In today’s digital world,millions of individuals are linked to one another via the Internet and social *** opens up new avenues for information exchange with *** analysis(SA)has gotten a lot of attention during the last *** analyse the challenges of Sentiment Analysis(SA)in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which wefirst produced an annotated dataset composed of Marathi text acquired from microblogging websites such as *** also choose domain experts to manually annotate Marathi microblogging posts with positive,negative,and neutral *** addition,to show the efficient use of the annotated dataset,an ensemble-based model for sentiment analysis was *** contrast to others machine learning classifier,we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation(cv),outcomes as 97.77%,f-score is 97.89%.

关键词： Sentiment analysis machine learning lexical resource ensemble classifier

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