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Flying Objects classification Based on Micro-Doppler Signature Data From UAV Borne Radar

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Mandal, Priti Roy, Lakshi Prosad Das, Santos Kumar Natl Inst Technol Rourkela Rourkela 769008 Odisha India

Unmanned aerial vehicles (UAVs) have been widely used in many facets of contemporary society over the past ten years due to their accessibility and affordability. The rise in drone usage brings up privacy and security issues. It is essential to be vigilant for unauthorized UAVs in restricted areas. In this work, a hybrid Convolutional Neural Network-Shuffled Frog Leap (CNN-SFL) algorithm is proposed for classifying various flying objects, such as drones, helicopters, and artificial birds based on micro-Doppler signature (MDS) collected from HB100 radar mounted on UAV. Various array positioning and configuration, such as uniform linear array (ULA), uniform rectangular array (URA), and uniform circular array (UCA), are taken into account when analyzing the accuracy for avoiding performance loss due to a significant angle of arrival (AoA) of the received signal. Further, the activities of drones are also classified, and accuracy is assessed in comparison to existing algorithms. The results demonstrate that the proposed technique outperforms in all cases. In the endfire direction, URA performs better as compared to the other configurations and in other directions, ULA performs better.

关键词： Drones Radar classification algorithms Arrays Radar antennas Doppler radar Autonomous aerial vehicles classification drone flying objects micro-Doppler signature (MDS) radar antenna array

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Biomarkers for ovarian cancer for the accurate detection and classification using computational algorithms and interpreting using explainers

Biomarkers for ovarian cancer for the accurate detection and...

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Computational Intelligence for Security, Communication and Sustainable Development (CISCSD), International Conference on

作者： Iffath Jahan Ashi Srivastava Ashwini Kodipalli Trupthi Rao Artificial Intelligence and Data Science Global Academy Of Technology Bangalore India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

Ovarian cancer remains a significant challenge in oncology due to its high mortality rates, often attributed to late-stage diagnosis and lack of effective screening methods. Biomarkers are essential for the precise identification and categorization of ovarian cancer, providing the opportunity for early diagnosis and individualized treatment plans. In this work, we examine how computational methods can be used to better diagnose and classify ovarian cancer by utilizing biomarker data. Through a rigorous assessment of the literature and bioinformatics analysis, a panel of biomarkers with promising uses in diagnosis and prognosis is found. Next, we employ numerous machine learning techniques, including as random forests and support vector machines, and deep learning architectures, to develop prediction models that have been trained using proteomic and genomic data. We use explainable AI techniques, such LIME and SHAP, to improve the interpretability of these models by offering clear insights into the decision-making process. The computational algorithms are effective in precisely identifying and categorizing subtypes of ovarian cancer according to biomarker profiles. Additionally, by establishing a correlation between computational predictions and treatment outcomes and patient outcomes, we clarify the clinical significance of these findings. This work highlights the potential of computational approaches to improve ovarian cancer diagnosis, and highlights the significance of interpretable models in enabling clinical translation and decision-making.

关键词： Machine learning algorithms Computational modeling Biological system modeling Decision making Biomarkers Predictive models Prediction algorithms classification algorithms Bioinformatics Ovarian cancer

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Using Synthetic Gaussian Noise to Explore Stochastic Resonance in Cancer Subtype classification

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IEEE ACCESS 2025年 13卷 31729-31736页

作者： Liang, William Boston Latin Sch Boston MA 02129 USA

Stochastic resonance (SR) allows models to detect weak signals or features that might otherwise be lost in noise, thereby improving the classification performance in Artificial Intelligence (AI) models. This study represents one of the first applications of SR in enhancing AI-driven cancer diagnostics. We compared eight common AI classification models (random forest, logistic regression, Naive Bayes, XGBoost, support vector machine, gradient boost, AdaBoost, and K-Nearest Neighbor) using performance metrics to evaluate their effectiveness in distinguishing cancer subtypes at varying noise levels. We trained our machine learning models using a dataset published by the University of California, Irvine's Machine Learning Repository, which consists of 20,531 Ribonucleic Acid sequence gene expression features and 801 samples representing the five cancer subtypes. While previous studies on this dataset focused on feature selection, our study explores the controlled addition of synthetic Gaussian noise at five different levels through SR to uncover weak signals in the data. Furthermore, we selected 80 significant genes by evaluating the Analysis of Variance f-values and removing constant features. Our results indicate that models enhanced with SR can classify gene expression with high accuracy (93-98%) when noise levels are increased to sigma = 4. Logistic regression and support vector machine models performed the best in noise-free conditions. However, model accuracy decreased when noise levels exceeded sigma = 4, declining to 83-93% when sigma = 8 across all models, showing machine learning vulnerability to unrestrained noise. The results of this study suggest that controlled noise implementation can enhance cancer diagnostic accuracy through SR.

关键词： Cancer Boosting Gene expression Random forests Data models Machine learning Accuracy Prediction algorithms Feature extraction classification algorithms classification Gaussian noise gene expression feature selection machine learning stochastic resonance

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Extended Target Tracking Utilizing Machine-Learning Software-With Applications to Animal classification

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 376-380页

作者： Malmstrom, Magnus Kullberg, Anton Skog, Isaac Axehill, Daniel Gustafsson, Fredrik Linkoping Univ S-58183 Linkoping Sweden Swedish Def Res Agcy FOI S-58183 Linkoping Sweden Uppsala Univ S-75310 Uppsala Sweden

This letter considers the problem of detecting and tracking objects in a sequence of images. The problem is formulated in a filtering framework, using the output of object-detection algorithms as measurements. An extension to the filtering formulation is proposed that incorporates class information from the previous frame to robustify the classification. Further, the properties of the object-detection algorithm are exploited to quantify the uncertainty of the bounding box detection in each frame. The complete filtering method is evaluated on camera trap images of the four large Swedish carnivores, bear, lynx, wolf, and wolverine. The experiments show that the class tracking formulation leads to a more robust classification.

关键词： Signal processing algorithms classification algorithms Cameras Target tracking Filtering algorithms Standards Loss measurement Multi-object tracking object detection environmental monitoring deep learning Kalman filters

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Meta-Learning algorithms for Few-Shot classification in Imbalanced Datasets

Meta-Learning Algorithms for Few-Shot Classification in Imba...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Raman Kumar Laith H. Fezaa Ahmed Alkhayyat Bhagwati Lal Pareek Jatinder Kumar Abhilasha Jadhav School of Mechanical Engineering Rayat Bahra University Mohali India Department of Optical Techniques Al-Zahrawi University College Karbala Iraq College of Technical Engineering The Islamic University Najaf Iraq Sangam University Bhilwara Rajasthan India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Jaipur India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This work aims to examine meta-learning on few-shot learning schemes, specifically on the assessment of how well current approaches manage imbalance in the examined datasets. Meta-learning to improve model performance with a small amount of data by learning from related tasks is tested against benchmarks such as MAML, ProtoNet and RelationNet. An important insight of our research is that these methods are still facing important issues, such as scalability or the problem of domain generalization, as well as the major issue of the extreme imbalance of the data distribution. To overcome these problems, we suggest a new solution, which employs elaborate methods and is characterized by increased correspond and F1 coefficient, as well as the degree of balanced accuracy at different degrees of unbalanced data. However, the proposed study also points out the main challenges for future research including new and efficient algorithms for high-dimensional and real-time scenario, better methods for handling massive shift, as well as the possibility to increase the interpretability of models. The research also highlights the possibilities of applying meta-learning in the few-shot classification, but at the same time also highlight the imperfections and the research needs to be continued in order to further improve the existing gaps and to meet the practical requirements. This study adds to the overall knowledge base concerning meta learning approaches and provides an initial frameworkfor work to be conducted in the future in an effort to produce methods that are considerably more portable, generalizable and also more comprehensible in nature across the broader field of study.

关键词： Metalearning Training Accuracy Scalability MIMICs Knowledge based systems Data models Real-time systems classification algorithms Overfitting

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Enhancing CNN classification with Lamarckian Memetic algorithms and Local Search

Enhancing CNN Classification with Lamarckian Memetic Algorit...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Akhilbaran Ghosh Rama Sai Adithya Kalidindi Tata Consultancy Services Limited Mumbai India Shiv Nadar University Dadri Uttar Pradesh India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Optimization is critical for achieving optimal performance in deep neural networks (DNNs). Traditional gradient-based methods, such as stochastic gradient descent (SGD) and ADAM, often face challenges like local minima entrapment and slow convergence rates. This paper explores population-based metaheuristic optimization algorithms for image classification networks. We propose a novel approach integrating a two-stage training technique with population-based optimization algorithms and local search capabilities to enhance solution quality and convergence speed. In the first stage, a global search is conducted using a population-based algorithm to explore the solution space extensively, allowing the network to escape local minima and find promising regions. In the second stage, a local search fine-tunes the solutions from the global search, ensuring improved accuracy and stability. Our experiments show that the proposed method outperforms state-of-the-art gradient-based techniques like ADAM in accuracy and computational efficiency, particularly in scenarios with high computational complexity and numerous trainable parameters. The results suggest that our approach offers a robust and efficient alternative for weight optimization in convolutional neural networks (CNNs). Future work will explore integrating adaptive mechanisms for parameter tuning and extending the method to other types of neural networks and real-time applications.

关键词： Training Accuracy Signal processing algorithms Feature extraction classification algorithms Convolutional neural networks Optimization Genetic algorithms Convergence Image classification

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Comparison of Various Computational algorithms for Accurate Electrical Fault Detection and classification, and Interpreting the Results Using LIME and SHAP Explainers

Comparison of Various Computational Algorithms for Accurate ...

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Engineering and Technology (PICET), Parul International Conference on

作者： Nidhi R Nisha N R Ashwini Kodipalli Trupthi Rao Dept. of AI & DS Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

The effective identification and categorization of electrical faults using machine learning techniques is investigated in this study. To determine which method is most efficient, it examines a number of computational strategies, such as decision trees and support vector machine. The study uses diverse datasets to train and evaluate each algorithm's performance, using metrics like accuracy, precision and recall. The study also addresses interpretability by incorporating LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive Explanations) explainers. These explainers offer perceptions into the process of making decisions of machine learning models, enabling a deeper understanding of features influencing fault predictions. The findings have implications for the practical implementation of machine learning-based fault detection systems.

关键词： Support vector machines Machine learning algorithms Accuracy Fault detection Computational modeling Machine learning Predictive models Prediction algorithms Electrical fault detection classification algorithms

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Diabetes Mellitus classification Using Machine Learning algorithms with Hyperparameter Tuning

Diabetes Mellitus Classification Using Machine Learning Algo...

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Vishnu Vardhana Reddy Karna Viswavardhan Reddy Karna Ravinder Beemagani Aravinda Babu Tummala Satya Veerendra Arigela Varaprasad Janamala Aymen Flah Electronics and Communication Engineeirng Aditya University Surampalem India Department of AIML R V College of Engineering Bengaluru India Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Electronics and Communication Engineeirng Chaitanya Bharati Institute of Technology Hyderabad India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Electrical and Electronics Engineering School of Engineering and Technology Christ University Bangalore Karnataka India Energy Processes Environment and Electrical Systems Unit National Engineering School of Gabes University of Gabes Gabès Tunis Tunisia MEU Research Unit Middle East University Amman Jordan Private Higher School of Applied Sciences and Technology of Gabes University of Gabes Gabès Tunisia ENET Centre VSB—Technical University of Ostrava Ostrava Czech Republic Applied Science Research Center Applied Science Private University Amman Jordan

ISBN: (数字)9798331529987

ISBN: (纸本)9798331529994

Diabetes Mellitus is a prevalent condition globally, marked by elevated blood sugar levels resulting from either insufficient production of insulin or the body cells' inability to respond appropriately to released insulin. For people with diabetes to lead healthy, normal lives, early identification and treatment of the condition are essential. With the need to move away from current traditional procedures, towards a noninvasive methodology, machine learning and data mining technologies can be very useful in the classification of diabetes. Creating an effective machine learning model for the classification of diabetes mellitus was the primary goal of this research. This work is primarily carried out on combined Pima Indian diabetes dataset and German Frankfurt diabetes dataset. The class imbalance issue has been resolved using Synthetic Minority Oversampling Technique. One-hot encoding is applied to convert categorial features to numerical and various single and ensemble classifiers with the best hyperparameters obtained using GridSearchCV method were employed on the pre-processed dataset. With an AUC of 0.98 and maximum accuracy of 98.79%, the Random Forest ensemble technique outperformed the other models, according to the experimental results. As a result, the algorithm might be used to predict diabetes and alert doctors to serious cases that call for emergency care.

关键词： Machine learning algorithms Accuracy Medical services Encoding Diabetes classification algorithms Insulin Random forests Tuning Testing

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Robust Road Surface classification Using Time Series Augmented Intelligent Tire Sensor Data and 1-D CNN

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IEEE ACCESS 2025年 13卷 76508-76515页

作者： Kim, Seokchan Kim, Yeong-Jae Lee, Dongwook Lee, Hanmin LG Elect H&A NPI VPD Project Chang Won 51533 South Korea Korea Inst Machinery & Mat Dept Ind Machinery DX Daejeon 34103 South Korea Kongju Natl Univ Dept Mech & Automot Engn Cheonan 31080 South Korea Kongju Natl Univ Global Inst Mfg Technol GITECH Cheonan 31080 South Korea

Tire-road friction coefficient information is an essential factor in the driving stability and safety of a vehicle. In recent years, there has been a lot of research on using the vibration characteristic of tires to estimate the road surface condition from its features. However, since tire vibration characteristics vary depending on conditions such as tire pressure, load, and driving status, it is still difficult to develop a road surface classification algorithm that is robust to various situations. To overcome this limitation, this paper proposes a road surface classification algorithm using a one-dimensional convolutional neural network (CNN) based on acceleration signals obtained through an intelligent tire sensor attached inside the tire. Moreover, a time series data augmentation method is applied to ensure that the learning network has the robustness to perform well under different tires and driving conditions than that in the training dataset. A road surface classification algorithm is trained using a dataset of accelerations measured on dry asphalt, wet asphalt, and basalt tile roads, and the performance of the trained algorithm is validated through test scenarios considering different tire conditions and vehicle types. Furthermore, the performance of different CNN architectures is compared and the algorithm with the best performance is suggested. The robustness to different tires and driving conditions makes the proposed algorithm practical for estimating road surface conditions in real vehicles.

关键词： Tires Roads classification algorithms Accelerometers Asphalt Vibrations Time series analysis Friction Robustness Convolutional neural networks Intelligent tire sensor road surface classification robustness convolutional neural network time series augmentation

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Partial Discharge Source classification Using Machine Learning algorithms

Partial Discharge Source Classification Using Machine Learni...

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Power Engineering Conference (SAUPEC), Southern African Universities

作者： Lucas T. Thobejane Bonginkosi A. Thango Department of Electrical Engineering University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

This article proposes a machine-learning algorithm for the automatic classification of single-source partial discharge (PD) in power transformers. PD testing provides valuable information of the state and deterioration of the insulation systems of transformer windings and core. A PD testing setup based on the IEC 60270 International standard is used to test PD from transformers of varying size and operational age. PD is recorded at different voltage levels applied to the transformer under test. Where the majority of PD classification literature has focused on laboratory developed artificial PD models, this work uses practical power transformers as a basis for testing and collecting the PD database. The PD database collected from this testing is utilized for the training, validation and testing of the machine learning algorithm. In this article, a comparative analysis of various trained machine learning algorithms for classifying PD is performed. The results of the classification show very pleasing performance from the tested classifier algorithms, with Bilayered Neural Network achieving a 96.97% validation accuracy of and a test accuracy of 97%.

关键词： Partial discharges Machine learning algorithms Accuracy Databases Voltage Machine learning classification algorithms Standards Testing Power transformer insulation

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