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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

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Interpretable Motor Sound Classification for Enhanced Fault Detection leveraging Explainable AI 3

Interpretable Motor Sound Classification for Enhanced Fault ...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Khan, Shaiq Ahmad Khan, Faiq Ahmad Jamil, Akhtar Hameed, Alaa Ali Natl Univ Comp & Emerging Sci Dept Comp Sci Lahore Pakistan Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL USA Natl Univ Comp & Emerging Sci Dept Comp Sci Islamabad Pakistan Istinye Univ Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9798350372977;9798350372984

In industries, machines communicate through sounds, decoded by predictive maintenance to prevent issues. Understanding motor sounds is crucial for seamless industrial operations. This research undertakes a comprehensive exploration of machine learning models, specifically Artificial Neural Network (ANN), Support Vector machine (SVM), K-Nearest Neighbor (KNN), and Random Forest, applied to motor sound data for classifying instances as either healthy or faulty. The ANN, boasting an 81.22% accuracy, reveals commendable precision and recall values for both classes, indicating its robust predictive capabilities. However, there is room for improvement, particularly in accurately classifying healthy motors. SVM marginally outperforms the ANN with an accuracy of 81.32%, showcasing balanced precision and recall for both classes. Notably, KNN, while exhibiting a slightly lower accuracy of 80.22%, excels in recall for the healthy class, emphasizing its efficacy in correctly identifying healthy motor sounds. Random Forest attains an accuracy of 81.32%, featuring notably high recall for the healthy class (0.91), underscoring its proficiency in capturing instances of healthy motor sounds. In-depth metrics provide nuanced insights into the strengths and specificities of each model, offering a foundation for informed decisions based on application priorities and requirements. The study contributes not only quantitative metrics but also interpretability tools, including LIME and SHAP, to enhance transparency and elucidate the intricate patterns within motor sound data.

关键词： gear motors faults diagnosis machine learning motor sounds

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Advancing Fake News Detection: From Traditional machine learning to Transformer-based Approaches 3

Advancing Fake News Detection: From Traditional Machine Lear...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhinand, I.V. Ajal Krishna, K.P. Krishna, Ajay Jayadev, J. Reji Rahmath, K. Divya, T. Dept. of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannur A. P. J. Abdul Kalam Technological University kerala Thiruvananthapuram India

ISBN: (纸本)9798331527549

Fake news on online platforms is spreading at a rapid rate, posing great threats to social, political, and economic stability in linguistically diverse regions like Kerala, India. This paper reviews comprehensively the techniques of fake news detection from traditional machine learning methods to transformer-based models. Special attention is given to the detection of fake news in regional languages like Malayalam, characterized by complex morphology and syntax. The study investigates applications of ensemble-based learning as well as state-of-the-art transformer models, BERT, and XLNet, focusing on their ability to capture nuanced linguistic features and contextual relationships. Evaluation results show how significantly better the transformer-based models fare than traditional methods with respect to accuracy and robustness. This review also captures current limitations - such as resource constraints for regional languages - as well as future opportunities in developing scalable, multilingual fake news detection systems. © 2025 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence-Based Detection of Autism Spectrum Disorder Using Linguistic Features 3

Artificial Intelligence-Based Detection of Autism Spectrum D...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Li, Juliana Harker Sch San Jose CA 95124 USA

ISBN: (纸本)9798350372977;9798350372984

Artificial intelligence (AI) technology has recently shown promising results in detecting Autism Spectrum Disorder (ASD), but it faces significant challenges because it is still in early stages of development. This study proposes to use linguistic features in the detection of ASD using artificial intelligence. In order to systematically understand different AI model performances, two types of AI models have been explored and evaluated. Five classical ML models were used: logistic regression, Gaussian naive Bayes, random forests, k-nearest neighbors (K-NN), and support vector machines (SVMs), and two deep learning models were used: multilayer perceptron (MLP) and a convolutional neural network (CNN). All model development work is based on analyzing speech transcripts of 64 children total aged 3 to 6 from two data banks, CHILDES and ASDBank, including 30 children with ASD and 34 typically developing (TD) controls. The first step was to examine the annotations of the transcriptions, then extract various linguistic features from the texts. AI models were trained to determine whether a child has ASD based on the characteristics of the transcripts analyzed. There are three major findings of this study. First, neural network models will outperform classical models in ASD detection by around 5%-10%. The models multilayer perceptron and convolutional neural networks achieved 83% and 84% accuracy, respectively. Second, among the classical machine learning models, logistic regression, SVMs, K-NN, and random forests all achieved accuracies in the range 77%-80%, while Gaussian naive Bayes performed the worst by around 10%. Third, the linguistic feature with the highest importance in ASD detection is MLU, and the features MLU, MLT, rUtts, and rTNW show significant distinctions between age and gender demographics in addition to having high importance in ASD detection. Overall, this research recommends using deep learning for clinical applications of ASD detection.

关键词： Autism Spectrum Disorder Natural Language Processing Artificial Intelligence machine learning Deep learning Linguistics

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Adversarial Attacks and Defenses on Deep learning Models 3

Adversarial Attacks and Defenses on Deep Learning Models

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Jhajharia, Kavita Shrivastavaa, Yash Kumar Manipal University Jaipur Information Technology Jaipur India

ISBN: (纸本)9798350354171

Deep learning, a pioneering field in machine learning, has significantly enhanced the analysis of medical images. These models utilize multi-layered neural networks to detect complex patterns, enhance tasks such as diagnosis, segmentation, and treatment planning within the images. Despite the remarkable advancements in detecting diseases and segmenting organs, deep learning models pose significant risks from adversarial attacks. These attacks manipulate input data, causing the models to make incorrect predictions and potentially leading to serious errors in medical diagnosis. This paper examines the impact of adversarial attacks on deep learning systems, focusing on medical imaging and the MNIST dataset. We implemented Fast Gradient Sign Method (FGSM) on convolutional neural network model. The findings of the study shows that accuracy of brain tumor detection was initially at 67% with correct classification but after the adversarial attack the model misclassified the tumor with 853% confidence under the perturbation with epsilon value of 0.3. for MNIST dataset the accuracy was decreased with every epsilon value increase, the prediction of digit input image was correctly classified with 87 % confidence, but after perturbation attack the model misclassified the input by 83% confidence. These findings highlight the distinct challenges of defending against adversarial attacks in medical imaging, focusing on the need for robust defense mechanisms. © 2024 IEEE.

关键词： Adversarial machine learning

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Detecting Ethnic Conflict in Social Media with Transformers and Augmented data 3

Detecting Ethnic Conflict in Social Media with Transformers ...

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3rd international conference on machine learning and data engineering, icmlde 2024

作者： Surkov, Anton Koltsova, Olessia HSE University 55 Sedova St. St.Petersburg192148 Russia

Chest X-ray pathology prediction play a very important role in early disease detection, enabling timely intervention and improving patient outcomes. Detection of ethnic conflict mentioning, discussion, or verbal participation therein in user-generated content is a socially important task, as such content has been proven related to ethnic clashes on the ground. Yet this task has not been studied. One of the reasons is the lack of relevant datasets which calls for the usage of data augmentation techniques, still uncommon for NLP. We propose a solution for Russian language by fine-tuning a pretrained transformer encoder enhanced with several standard and novel data augmentation approaches. The highest quality of F1-macro = 0.8 is obtained with fine-tuned ROBERTA model combined with our novel augmentation technique which generates new training data by randomly swapping ethnonyms. This eliminates classification algorithms' over-reliance on rare ethnonyms and prevents overfitting. Although the contribution of augmentation is modest, when exposed to a relevant adversarial attack, our model turns out to be the most sustainable with its quality advantage over the baseline reaching 0.05 on the target class. This advantage is achieved by training the model on the texts with randomly replaced ethnonyms which eliminates the model's over-reliance on ethnonyms occurring exclusively or mostly in a single class in the training set. Thus our approach is expected to be useful for elimination of similar effects in the tasks such as aspect-based sentiment analysis with large numbers of aspects. We also conduct error analysis and conclude which categories of texts usually cause inaccurate prediction © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Tweets

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To predict the deposition of air pollutants on trees Leaf in high traffic area using machine learning Algorithm 3

To predict the deposition of air pollutants on trees Leaf in...

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3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Yadav, Neelam Singh, Sunil K Sharma, Dinesh Chandigarh College of Engineering and Technology Cse Deptt. Chandigarh India Chandigarh College of Engineering and Technology Ece Deptt. Chandigarh India

ISBN: (纸本)9798331528201

Globally, as contemporary society continues to grow and urbanization picks up speed, the issue of air pollution is becoming more and more pressing. The two most pressing issues that need to be resolved by humans right now are techniques for forecasting the level of air quality and figuring out the appropriate governance. An increasing number of machine-learning techniques have been applied in recent years to address the air quality prediction issue. Prediction results are, however, significantly impacted by the unpredictability of environmental changes and the challenge of accurately anticipating quantitative values. This research proposes to use a machine learning method to predict air pollution on tree leaves in heavy traffic. The pollutant deposition level is determined using high-resolution camera photos of the roadside tree's leaf. In this paper, we used Linear Regression algorithms with optimization methodologies PSO, DPSO, and FODPSO to predict pollutant levels from leaf pictures, and the AQI value from (HSPCB) values. As a result, the Fractional-Order Darwinian PSO outperforms all other PSO algorithms in terms of accuracy, stability, and speed, making it a preferred choice when accuracy and execution time are of concern. © 2024 IEEE.

关键词： Air quality

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Day Ahead Load Demand Forecasting based on LSTM machine learning Model 3

Day Ahead Load Demand Forecasting based on LSTM Machine Lear...

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3rd international conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Bareth, Rashmi Yadav, Anamika National Institute of Technology India Department of Electrical Engineering Raipur India

ISBN: (纸本)9798350349207

load demand Forecasting is a crucial part in modern energy management systems, grid operators in resource allocation and maintaining grid stability. This research paper introduces an innovative methodology for load demand forecasting employing Long Short Term Memory (LSTM) networks, a specific type of recurrent neural network (RNN) adepted by capturing long-Term dependencies within sequential data. This work proposed a LSTM-based load forecasting model that utilizes one year of historical electricity consumption data from the Chhattisgarh state of India. The model is designed to predict day ahead load demand for the subsequent year. As the energy sector advances towards a smarter and more sustainable future, the integration of sophisticated LSTM based forecasting techniques holds significant promise. The technique facilitates the optimization of energy distribution, encourage the seamless integration of renewable energy sources, and the resilience of power grids. The model performance analysis is presented by utilizing Mean Absolute Error (MAE) values as the evaluation metric. © 2024 IEEE.

关键词： Long short-term memory

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Effect of Pesticides on Crop Health Monitoring Based on NDVI Using machine learning 3

Effect of Pesticides on Crop Health Monitoring Based on NDVI...

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3rd IEEE Delhi Section Flagship conference, DELCON 2024

作者： Pawar, Suvarna Badure, Aman Bade, Adikrishna Kolhe, Sai Takwane, Prasad Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518592

The aim of this research paper is to evaluate the crucial impact of pesticides. This control measure is an important agricultural input for improving crop health. However, due to the nature of agriculture, we need developments in machine learning which are urgently required to analyze it. The utilization of pesticides is important in preventing severe damage from pest and disease outbreaks. This work suggests the application of remote sensing finding devices like drones with multispectral imaging, which can be used to obtain Normalized Difference Vegetation Index (NDVI), with the aim of measuring the status of the crops' health as a function of the chlorophyll content of the plants. The model being presented is based on the use of IoT sensors, drone images, and machine learning algorithms to supervise the energy status of the plants in the field. Preliminary imaging data based on NDVI will highlight the images of drones conditions. However, in this research offers solutions to the problem of diverse resolution images that need to be registered in order to create a mosaic. In the end, the results will illustrate how pesticides impact the efficiency of crops, which will help in developing sustainable agriculture solutions that are environmentally friendly. © 2024 IEEE.

关键词： crop health machine learning NDVI pesticides Precision agriculture

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Predicting Thyroid Disease using machine learning: A Comparative Study of Algorithms 3

Predicting Thyroid Disease using Machine Learning: A Compara...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Katore, Khushi Balvir, Parul Daronde, Subodh Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331509675

Predicting thyroid illness has become a significant problem in recent times. Thyroid illness is getting more frequent in women over thirty, but it is also becoming more prevalent in men, women, and children. Long-term effects include difficulties with the heart, eyes, fertility, and pregnancy. Therefore, it remains essential to assess the thyroid data in instruction to detect the disease at an initial stage and take preventative measures against the lethal thyroid cancer condition. Two prevalent thyroid illnesses that affect the thyroid's ability to produce thyroid hormones and control the body's metabolism are hyperthyroidism and hypothyroidism. data cleaning procedures were utilised to make the data simple sufficient for analytics to demonstration the likelihood of affected role having thyroid illness. Thirteen clinicopathologic characteristics from the Kaggle data set are employed in this paper's analysis and classification models for thyroid illness with the goal of predicting the recurrence of well-differentiated thyroid cancer. An important factor in the process of illness prediction is machine learning. It is important to guarantee a strong foundation of information that container be integrated and employed as a hybrid model for multifaceted knowledge tasks, such medical diagnostic and prognosis tasks. Additionally, we suggested many machine learning methods and diagnostics for thyroid prevention in this research. machine learning Algorithms, support vector machine (SVM), K-NN, Decision Trees, Logistic Regression , Random Forest , XGBoost were used to forecast the projected risk on a patient's chance of obtaining thyroid disease. © 2025 IEEE.

关键词： Decision trees

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Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation 3

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Manju Bala, P. Priyadharshini, S. Usharani, S. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Data Science India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering India IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350391565

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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