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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Uma Maheswari. V Niveditha Reddy Mandala Roopika Ponnur Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Multi-classification of pulmonary diseases poses a significant challenge, particularly when diseases share similar radiological presentations like lung cancer, pneumonia, and COVID-19. While chest CT scan images are essential for diagnosis, manual interpretation by radiologists is time-consuming and difficult, even for experts. Deep learning algorithms offer promise in this domain, demonstrating high accuracy in classifying these diseases from CT images. This review examines recent advances in using deep learning for multiclassification of pulmonary diseases. It discusses various preprocessing techniques, deep learning architectures to enhance model performance. Additionally, a comparative analysis of different deep learning models on publicly available CT datasets is presented. The findings suggest that deep learning models could serve as valuable tools for multiclassification of pulmonary diseases, potentially improving diagnostic accuracy and efficiency. Nonetheless, further research is required to develop and validate these models on large, diverse datasets and ensure their robustness in real-world clinical settings.

关键词： Deep learning Surveys COVID-19 Image segmentation Accuracy Pneumonia Computed tomography Image edge detection Lung cancer classification algorithms

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Deep CNN and Adaptive Habitat Biogeography-Based Optimizer algorithms for Rotating Machine Fault Detection and classification

Deep CNN and Adaptive Habitat Biogeography-Based Optimizer A...

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International Conference on Microelectronics, ICM

作者： Issam Attoui Nadir Fergani Research Center in Industrial Technologies CRTI Cheraga Algeria

ISBN: (数字)9798350379396

ISBN: (纸本)9798350379402

With the abundance of condition monitoring data, research on data-driven methods for rotating machine fault diagnosis has garnered increased attention. However, existing approaches often struggle with extracting representative features from raw data and assume feature distribution consistency between training and testing domains, which does not hold in many real-world scenarios. This study proposes a novel methodology for diagnosing faults in rotating machines by utilizing the automatic feature extraction capabilities of deep learning and the improved performance of ensemble learning, specifically through the integration of deep convolutional neural networks (DCNN) and an adaptive habitat biogeography-based optimizer for hyperparameter optimization of the deep CNN. Experimental validation conducted on a dataset collected from URMM/CRTI test bench demonstrates the effectiveness of the proposed methodology.

关键词： Fault diagnosis Deep learning Training Rotating machines Fault detection Habitats Feature extraction classification algorithms Convolutional neural networks Testing

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 1487-1492页

作者： Palomo, Jaime de la Pena, David Munoz Alamo, Teodoro Univ Seville Escuela Super Ingn Dept Ingn Sistemas & Automat Seville 41092 Spain

Most employed Bayesian algorithms, such as quadratic discriminant analysis, linear discriminant analysis or naive Bayes, rely on Gaussian assumptions. In this letter we introduce a novel approach to Bayesian classification based on dissimilarity functions. Dissimilarity functions provide a measure of the distance of a sample to a given set and can be used to obtain empirical probability distributions that allows the fitting of non-Gaussian probability distributions. We prove that the resulting set of classifiers, based on dissimilarity functions, includes, but is not limited to, quadratic discriminant analysis classifiers. We also demonstrate the properties of the proposed approach analyzing their performance over publicly available datasets.

关键词： Bayes methods Probability density function Gaussian distribution Probability distribution Monte Carlo methods classification algorithms Vectors Bayesian classification dissimilarity function quadratic discriminant analysis machine learning

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Grasshopper Optimization Algorithm and Neural Network Classifier for Detection and classification of Barley Leaf Diseases

IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY

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IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY 2024年第1期5卷 530-541页

作者： Dorgham, O. Abu-Shareah, G. Alzubi, O. Al Shaqsi, J. Aburass, S. Al-Betar, M. A. Al Balqa Appl Univ Prince Abdullah bin Ghazi Fac Informat & Commun Te Al Salt 19117 Jordan Sultan Qaboos Univ Informat Syst Dept Muscat 123 Oman Al Balqa Appl Univ Fac Artificial Intelligence Dept Intelligent Syst Al Salt 19117 Jordan Maharishi Int Univ Dept Comp Sci Fairfield IA 52557 USA Luther Coll Dept Comp Sci Decorah IA 52101 USA Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates

The prevalence of plant diseases presents a substantial challenge to global agriculture, significantly impacting both production levels and economic stability in numerous countries. This study focuses on the early detection of two prevalent diseases affecting barley leaves: net blotch and spot blotch. We introduce a novel model designed for the accurate detection and classification of these diseases. The model employs advanced pre-processing techniques, including the transformation of images into the CIELAB color space and the segmentation of affected areas, to enhance disease identification accuracy. Key shape properties characterizing the diseased regions are extracted and analyzed to distinguish between the two diseases. A critical component of our approach is the feature selection phase, aimed at identifying the most pertinent and informative features, thereby minimizing classification errors and maximizing model accuracy with a minimal set of shape properties. To optimize this process, we have incorporated the Grasshopper Optimization Algorithm, which effectively identifies the optimal shape properties for feature selection. The final classification is executed using a Back Propagation Neural Network Classifier. The efficacy of our model was tested using images of barley afflicted with the specified diseases. The results were compelling, with the model achieving a remarkable accuracy rate, largely attributable to the integration of the grasshopper optimization algorithm in the feature selection stage.

关键词： Diseases Feature extraction classification algorithms Accuracy Optimization Support vector machines Training detection classification neural network feature selection grasshopper optimization plant diseases image processing

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Galaxy Clustering and classification using Machine Learning algorithms and XAI

Galaxy Clustering and Classification using Machine Learning ...

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International Conference on Information Technology Research (ICITR)

作者： Amasha Elvitigala U dani Navaratne Samadhi Rathnayake Kapila Dissanayaka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Kandy Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Colombo Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

This paper presents a novel approach to galaxy clustering and analysis using machine learning (ML), coupled with Explainable Artificial Intelligence (XAI) methods. Traditional astronomical research often relies on manual analysis of galaxy clusters, which can be time-consuming and potentially biased. Our study aims to automate this process using advanced computational methods, focusing on clustering algorithms such as Gaussian Mixture Models (GMM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). We also employ classification algorithms including Random Forests, Decision Trees, and Support Vector Machines (SVM). The application of XAI techniques enhances the interpretability and trustworthiness of our models' outputs. By leveraging data from the Sloan Digital Sky Survey (SDSS), we demonstrate the efficacy of our approach in identifying and analyzing galaxy clusters, potentially revolutionizing the field of astronomical research.

关键词： Surveys Support vector machines Accuracy Machine learning algorithms Explainable AI Noise Clustering algorithms classification algorithms Random forests Gaussian mixture model

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Improved PointNet classification Network Using Clustering algorithms

Improved PointNet Classification Network Using Clustering Al...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Wei Xu Jianxun Zhang Yujiang Pan Faculty of Information Technology Engineering Tianjin University Of Technology And Education Tianjin China

ISBN: (数字)9798331506896

ISBN: (纸本)9798331506902

PointNet was a breakthrough in neural networks for directly processing point cloud data, ensuring transformation and input invariance with a simple, efficient structure. However, it neglects the relationships between individual points’ features. This paper introduces an improved model, KMeans-PointNet, which combines PointNet with K-Means clustering. By clustering point cloud data and extracting global features from each cluster, the model enhances local feature utilization. Experiments on the ModelNet40 dataset show that KMeans-PointNet improves overall recognition accuracy by 3% and classification accuracy by 2% compared to the original PointNet.

关键词： Point cloud compression Analytical models Accuracy Computational modeling Neural networks Clustering algorithms Feature extraction Knowledge discovery classification algorithms Distributed computing

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Fourier Decomposition-Based Automated classification of Healthy, COPD, and Asthma Using Single-Channel Lung Sounds

IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS

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IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2024年第3期6卷 1270-1284页

作者： Koshta, Vaibhav Kumar Singh, Bikesh Behera, Ajoy K. Ranganath, T. G. Natl Inst Technol Raipur Dept Biomed Engn Raipur 492010 India All India Inst Med Sci Dept Pulm Med & TB Raipur 492009 India

Subjective discrimination of asthma and Chronic Obstructive Pulmonary Disease (COPD) is challenging as they share overlapping symptoms and are subject to personal interpretation. Hence, there is a demand for an alternative diagnostic system devoid of any subjective interference. The current study introduces Fourier Decomposition Method (FDM) based models utilizing Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) to identify patients with asthma and COPD by analyzing lung sound signals. The signals were decomposed into Fourier intrinsic band functions (FIBF) using three filter banks: dyadic, equal energy, and uniform band. Four statistical attributes, namely: Shannon entropy, log entropy, median absolute deviation and kurtosis, are calculated from relevant FIBF. Support vector machine (SVM), k-nearest neighbor (kNN) and ensemble classifier (EC) optimized with Bayesian optimization are used for classification of asthma vs COPD and normal vs adventitious sound, respectively. The highest accuracies achieved using DCT with 10-fold cross-validation are as follows: 99.4% (Asthma vs COPD), 99.1% (Asthma vs COPD vs Normal), 99.4% (COPD vs Normal) and 99.7% (Asthma vs Normal). Similarly, the highest accuracies reported by DFT with 10-fold cross-validation are: 99.4% (Asthma vs COPD), 99.6% (Asthma vs COPD vs Normal), 99.4% (COPD vs Normal) and 99.8% (Asthma vs Normal).

关键词： Lung Chronic obstructive pulmonary disease Asthma Entropy Frequency division multiplexing Support vector machines classification algorithms COPD Bayesian optimization Fourier decomposition method filter bank DCT DFT

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Research on Seabed Slope classification and Survey Path Optimization Based on Genetic algorithms and Plane Fitting Method

Research on Seabed Slope Classification and Survey Path Opti...

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Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Yonghang Hong Yexin Cheng Ziyu Peng Business School Hohai University Nanjing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

Responding to the surge in maritime exploration and the increasing need for precise seabed mapping, this paper introduces a route planning method enhanced by an optimization algorithm to improve accuracy and efficiency. We develop three route planning models: north-south, east-west, and diagonal east-west directions. Utilizing a genetic algorithm for regional optimization and a planar fitting model to determine the shortest path in each region, our analysis reveals that the diagonal east-west direction yields the shortest route. The findings indicate improvements in route length, reduced missed areas, and lower overlap rates. This study offers a potent optimization algorithm for route planning, setting the stage for future advancements in the field.

关键词： Surveys Image processing Fitting Solids Planning classification algorithms Partitioning algorithms Surges Optimization Genetic algorithms

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Binary classification of Epilepsy Using Classical Machine Learning algorithms and Ensemble Learning Techniques

Binary Classification of Epilepsy Using Classical Machine Le...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Samarth Agrawal Krishnay Mudgal Shreya Agarwal Sanidhya Sharma Department of Computer Science Ajay Kumar Garg Engineering College Ghaziabad Uttar Pradesh India Department of Mathematics Indian Institute of Technology Ponda Goa India Department of Computer Science Jaypee Institute of Information Technology Noida Uttar Pradesh India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Epilepsy is a neurological disorder characterized by recurrent seizures. Seizures are caused by abnormal electrical activity in the brain. Symptoms of epilepsy can vary widely, depending on the type of seizure and the part of the brain affected. Some people with epilepsy may experience only occasional seizures, unconsciousness, and temporary confusion while others may have frequent seizures that are difficult to control. Early detection of Epilepsy is possible through several form of input data, ranging from Video, Audio, Text, several physiological data (such as Heartbeat & Blood pressure) and ECG. In this paper we have used ECG recoding data and several Machine Learning algorithms to detect Epilepsy. In our Experiment, we have used ‘UCI Epileptic Seizure Recognition Dataset’ and to counter the Problem of Imbalance data set we have used SMOTE analysis. We have also done various Hyper parameter tuning processes to improve the overall result of the performance of the model. We have found that the Gradient Tree Boosting Algorithm Has Outperformed K- Nearest Neighbor (K-NN), Naive Bayes, Logistic Regression, Decision Tree (D.T.), Random Tree classifiers with 97.41% Accuracy, ROC = 0.974, and RMSE = 0.16.

关键词： Machine learning algorithms Accuracy Epilepsy Electrocardiography classification algorithms Ensemble learning Kernel

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Enhanced classification of Exoplanets Using Machine Learning: Integrating Feature Selection, Ensemble algorithms, and Explainable AI Insights

Enhanced Classification of Exoplanets Using Machine Learning...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Ishvarya G Kavya Sree Kammari Sharanya Vanraj Thambi Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research delves into employing machine learning (ML) algorithms for classifying exoplanets, focusing on enhancing classification accuracy through feature selection. By utilizing data from the KOI planet candidates, the study addresses the challenge of handling large datasets by employing feature selection techniques like the chi-square method, improving the classification process’s effectiveness. Principal Component Analysis (PCA) was employed for data visualization, aiding in understanding the underlying structure and relationships within the exoplanet dataset. In the implementation phase, the study initially employed standalone classifier models such as KNN, SVM, Random Forest, Naive Bayes, Decision trees and CNN. Following this, ensemble algorithms including XGBoost, GBM, CatBoost, and AdaBoost were utilized to enhance classification performance further. Among these, Random Forest emerged as the top performer, achieving an F-measure of 0.95, surpassing the others. Additionally, the study incorporates explainable AI (XAI) techniques to interpret the importance of individual features in the models’ predictions, shedding light on crucial features essential for accurately classifying KOI planets. These insights serve as a foundation for developing more advanced and dependable ML models for exoplanet classification, facilitating future research endeavors that may incorporate additional datasets and features.

关键词： Machine learning algorithms Planets Extrasolar planets Predictive models Nearest neighbor methods Feature extraction Prediction algorithms classification algorithms Random forests Principal component analysis

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