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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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Mango Internal Defect Detection using NIR Spectroscopy data 3

Mango Internal Defect Detection using NIR Spectroscopy Data

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

作者： Guru, D.S. Nandini, D. Department of Studies in Computer Science University of Mysore Manasagangotri Karnataka Mysore570006 India

Near-Infrared (NIR) Spectroscopy is a crucial non-destructive technique for the internal defect detection of mangoes in effectively identifying spongy tissue. In this study, we are proposing a method for classifying mango spectroscopy data into two classes such as internal defective and healthy mangoes. The proposed method uses savitzky-golay (savgol) filter as a pre-processing step for smoothening the wavelength or spectroscopy data because it effectively smooth's noisy data by computing derivatives that enhance critical features which are crucial for further processing. Then we have adapted partial least square discriminant analysis (PLS-DA) with cross decomposition for the effective reducing dimensionality by transforming the original data into a lower-dimensional space, as spectroscopy data typically involves high-dimensional features due to the large number of wavelengths measured and they suffer from multi-collinearity problem. These issues are addressed by increasing the correlation among the spectral data (predictors) and the defect classification (response variable). This approach helps in not only preventing overfitting, but also ensuring that the model generalizes well to new unseen data. After determining new axis or principal components that sum ups the variations in the data, learning algorithms such as random forest (RF), support vector machine (SVM), logistic regression (LR), are applied for the purpose of classification. For experimental analysis the dataset of 76 mango samples of the spectroscopy data is considered [3] and achieved an average accuracy of 93.78% using SVM, 93.72% with Logistic Regression and 93.42% with Random Forest for the lower range wavelength. This study also presents the results for the higher range wavelength. Moreover, other dimensionality reduction methods such as LDA (Linear Discriminant analysis) and PCA (Principal Component analysis) are compared against proposed method. An extended analysis is also done with advanced fe

关键词： Principal component analysis

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A machine learning Model to Classify Social Media Content 3

A Machine Learning Model to Classify Social Media Content

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3rd international conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024

作者： Chetty, Naganna Alathur, Sreejith Saldana, Ashika Ruth Gupta, Amit Hasan, Mohammad Kamrul NMAM Institute of Technology Nitte University Department of Information Science & Engineering Mangaluru India Indian Institute of Management Department of Information Systems Kozhikode India K.R. Puram Hobli Bangalore East Taluk Kyndryl Solutions Private Limited Rachenahalli Village Bengaluru India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Centre for Cyber Security Universiti Kebangsaan Malaysia Faculty of Information Science & Technology Selangor Malaysia

ISBN: (纸本)9798350391244

The world has witnessed the exponential growth of internet users. The dissipation of toxic content through social media platforms has recently increased, leading to global communal activities. This work aims to develop a model to classify online toxic content. The data/tweets are collected from multiple sources and combined to train the classification model. The dataset is pre-processed to eliminate stop words, punctuations, digits, blank spaces and perform stemming. The data has been split into training and testing parts with a ratio of 75:25 respectively. The model is trained using different machine learning algorithms on extracted features from the dataset. The random forest algorithm has been shown better performance by categorizing tweets into racist, sexist, and normal than other algorithms used. The accuracy of various models is compared and tabulated. Further, the model can be enhanced by using deep learning algorithms and quality datasets. © 2024 IEEE.

关键词： Tweets

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Exploring Strategies for Privacy-Preserving machine learning in Distributed Environments 3

Exploring Strategies for Privacy-Preserving Machine Learning...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Dodda, Suresh Kumar, Anoop Kamuni, Navin Ayyalasomayajula, Madan Mohan Tito IIT Roorkee India AI-ML BITS Pilani WILP United States Aspen University PhoenixAZ United States

ISBN: (纸本)9798350372120

machine learning (ML) with distributed privacy preservation is growing in significance as it focuses on facilitating multi-party learning without requiring actual data sharing. This is especially helpful for companies that want to work together but are unable to do so because of ethical, regulatory, or budgetary constraints on sharing data. In order to address these issues, this study examines three privacy-preserving algorithms: regularized logistic regression with Differential Privacy (DP), stochastic gradient descent (SGD) with differentially private updates, and a distributed Lasso that distributes gradients among data centers. The study emphasizes the relationship between error rate and privacy through these algorithms. In order to improve error rates for large datasets, both DP algorithms modify their sensitivity dependent on the amount of data, highlighting the significance of training data volume in model performance in the study. Results demonstrate that using the SGD;error rate can be reduced by employing random projections in advance. © 2024 IEEE.

关键词： Large datasets

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The personalized university English learning system in computer-driven research 24

The personalized university English learning system in compu...

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3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Ming, Jing School of Public Fundamental Courses Wuhan Institute of Design and Sciences Hubei Wuhan China

ISBN: (纸本)9798400712692

The aim of this study is to develop a computer-assisted personalised university English learning system to improve learning efficiency and effectiveness. machine learning algorithms were used to analyse student data and build a personalised learning model. Experimental results show that the system significantly outperforms traditional methods in vocabulary acquisition (22% improvement), listening comprehension (18% improvement) and oral fluency (15% improvement). The conclusion suggests that a personalised learning system based on artificial intelligence and big data can effectively improve university English teaching, and provide a new direction for the future development of educational technology. During the Eastern Han period, the production of everyday pottery, pottery sculptures, architectural bricks and tiles, a variety of ceramic sculptures, living statues, horses, acrobatics and other funerary objects, as well as new products such as large lofts, chariots and horses, became the main features of the industry. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Educational robots

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2024 3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

2024 3rd International Conference on Advancement in Electric...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

ISBN: (纸本)9798350388282

The proceedings contain 180 papers. The topics discussed include: optimizing diabetes prediction accuracy: a comprehensive approach with advanced preprocessing and diverse machine learning classifiers;designing a graphics processing unit for 2D rendering on FPGA for educational purpose;an ensemble approach of transfer learning and vision transformer to identify COVID-19 from chest X-rays;advancing water vending industry through RFID and IoT empowerment;voltage harmonics mitigation of non-linear loads using model predictive control in a three phase system;design and analysis of a 6G terahertz aeronautical antenna based on graphene;circularly polarized single layer large scale microstrip patch array antenna for wireless communications;and enhancing autism spectrum disorder diagnosis through a novel 1D CNN-based deep learning classifier.

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Unraveling Dementia Severity: A Deep learning Approach for Brain MR Image-Based Prediction and Misclassification Analysis 3

Unraveling Dementia Severity: A Deep Learning Approach for B...

引用

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

作者： Nair, Rekha R. Babu, Tina Singh, Tripty Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Alzheimer's disease is the most prevalent cause of dementia, and its early diagnosis is crucial to prevent the progression to severe stages where cognitive abilities are severely impaired. This research paper presents an innovative approach to predict the severity of dementia through classification and grading. The research introduces an innovative adaptation of the DEMNET model, referred to as the DEMENtia network model. The research implements a novel methodology leveraging Convolutional Neural Networks (CNNs) to identify significant patterns within unorganized web-based data collections. The investigation employs a dataset com- prising four categories, obtained from the Kaggle platform. The developed model demonstrates exceptional performance, achieving 99.9% accuracy during training, 97.4% accuracy in testing, and an overall precision of 0.975. The DEMENtia network model suc- cessfully categorizes individuals into four groups: those without dementia, and those with moderate, mild, or very mild dementia. The model achieves a remarkable accuracy of 99.20% in classifying the Moderate demented class, a significant advantage over existing approaches. To understand this behavior, conducted an in-depth analysis by visualizing the pixel intensity distribution over the space. The proposed model validity has been confirmed through validation by a team of neurologists, ensuring its potential for real-world clinical applications. By accurately predicting dementia severity, the proposed model can aid in early diagnosis and treatment planning, contributing to improved patient care and management. © 2025 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Enhancing Power Grid Security by Detecting Relay Setting Manipulation Attacks with machine learning 3

Enhancing Power Grid Security by Detecting Relay Setting Man...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Varghese, Samya Rebecca Jiji Venu, Ankita Vijaya Chandrakala, K.R.M. Gireesh Kumar, T. Arun Shankar, V.K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Coimbatore India Danfoss Industries Private Limited United States

ISBN: (纸本)9798350372120

As power grids undergo extensive digitalization, they become susceptible to diverse cybersecurity threats, including potentially manipulating protective relay settings. Traditional detection methods struggle to identify these attacks effectively, posing a significant risk of widespread disruptions. This research introduces a machine learning-based approach for detecting and categorizing attacks that manipulate relay settings. The experiments evaluate the efficacy of various algorithms, including Random Forest, XGBoost, and Decision Trees, and Random Forest emerged as the best-performing model, showcasing the capabilities of machine learning to enhance the security of power grid operations. © 2024 IEEE.

关键词： Anomaly detection

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Breast Cancer Classification using Metaheuristic Optimization and machine learning 3

Breast Cancer Classification using Metaheuristic Optimizatio...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chandra, Avinash Kadam, Ayush Sangram Jain, Palak Kumawat, Mohit Patil, Hemprasad Yashwant Gawas, Mahadev Anant Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India State Higher Education Council Directorate of Higher Education State Government of Goa Goa India

ISBN: (纸本)9798350372120

This research utilizes advanced machine learning techniques, specifically focusing on Decision Tree, Naive Bayes, Random Forest, and Ada Boost models, to conduct a thorough examination of breast cancer prognosis. Our findings reveal compelling insights into the effectiveness of diverse machine learning models in assessing breast cancer. The Decision Tree model achieves an impressive accuracy of 93.5%, accompanied by a precision rate of 86.73%. The Naive Bayes model demonstrates high accuracy, reaching 95.5%, with a precision of 91.32%. Notably, the Random Forest model stands out with exceptional performance, achieving an impressive accuracy of 98.3% and a precision of 96.5%. The Ada Boost model also proves to be robust, boasting an accuracy of 97.0% and a precision of 93.37%. After performing metahueristic hyperparameter tuning with differential evolution algorithm and derived feature engineering we have achieved accuracy of 98.83% and 98.41% precision. This study serves as a valuable resource for healthcare professionals, researchers, and policymakers, providing essential insights into the strengths and limitations of various machine learning models in the context of breast cancer prognosis. © 2024 IEEE.

关键词： Decision trees

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Smartphone Usage and Next App Prediction for Smart Interventions: A Quantum LSTM Approach 3

Smartphone Usage and Next App Prediction for Smart Intervent...

引用

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

作者： Harikrishnan, V.S. Ganesh, K. C-DAC Tidel Park No.4 Rajiv Gandhi Salai Taramani Chennai600113 India

This work focuses on analyzing Smartphone Problematic Usage by predicting the next mobile app a user is likely to engage with, using advanced Artificial Intelligence (AI) algorithms. The project examines app usage patterns across various mobile applications and leverages Deep learning models to forecast user interaction dynamics and engagement patterns for mobile apps, identifying apps that contribute to excessive use. By incorporating Quantum Cognition, which applies Quantum Probability Theory to human decision-making, this research explores a comparison between Quantum Deep learning (qLSTM) and Traditional Deep learning (LSTM). The qLSTM model, designed to align more closely with human cognitive processes, demonstrates superior accuracy and computational efficiency, effectively predicting app usage and flagging problematic apps. To mitigate excessive usage, the system triggers timely interventions, warning users when they engage with apps for extended periods. The study employs a real-world dataset of 90,717 app usage records and evaluates the performance of qLSTM and LSTM models using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results demonstrate that qLSTM achieves an MAE of 3.170, while LSTM yields an MAE of 4.839, with the quantum approach consistently outperforming traditional methods. Additionally, this work discusses the ethical considerations of user intervention, weighing the benefits of reducing overuse against respecting user autonomy. The research highlights the potential of Quantum-Enhanced machine learning to address challenges in digital well-being and lays the groundwork for future advancements in quantum-based predictive analytics. © 2025 The Author(s).

关键词： Deep learning

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Ensemble-based machine learning Classification for Early Detection of Autism Spectrum Disorder 3

Ensemble-based Machine Learning Classification for Early Det...

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

作者： Subhash, Ambika Rani Ashwin Kumar, U.M. School of Computer Science and Engineering REVA University Bengaluru India Department of Information Science and Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (纸本)9798331527549

A complicated neurological illness, autism spectrum disorder (ASD) impacts social skills, communication, and other behavioral elements. Because the symptoms of ASD might be confused with those of other mental health conditions, diagnosing it can take a long time. The goal of this study is to develop a categorization model that can forecast the risk of ASD in toddlers, kids, teens, and adults. To improve speed, we used a variety of machine learning methods and ensembled them. Four publicly accessible non-clinical ASD screening datasets from Kaggle and the UCI library were used to assess the models. These datasets included 1,054 cases with 19 features for toddlers and different characteristics for older age groups. The classifiers' accuracy was almost 99%, according to the results. © 2025 IEEE.

关键词： Adversarial machine learning

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