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40 articles for “Supervised learning Neural network”
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Vehicle Insurance Fraud Detection Using Supervised Neural Network Model
Abstract: Vehicle insurance fraud is a serious problem that causes huge financial losses to insurance companies. In recent years, developing fraud detection models using machine-learning techniques has been of great interest. This study proposes a novel approach for vehicle insurance fraud detection using Supervised Neural network model on the Kaggle Vehicle Insurance Fraud Detection dataset. The initial step in this study involves performing data pre-processing and feature extraction on the dataset, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 10, Issue 1, 2023 · pp. 32–39 Read article
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Vegetation Classification Based on Leaf Pattern
Abstract: In this current world, vital species of plants are going to be lost day by day. To command, this phenomenon, steps are first discovering then restoring and retain them. Among them, spotting of plant is giving up challenging. Usually, plants are recognizing from their leaves. In this paper, a deep neural network model is implemented which will extract the hidden pattern form leaf images. A classifier named as Convolutional Neural …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 8, Issue 3, 2021 · pp. 9–16 Read article
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Design and Arduino Implementation of Artificial Neural Network Based Intelligent Power Management System of Bangladesh
Abstract: The research contrivance is to maintain and control load detachment or shedding in local distribution area and also utilize different types of power generation units such as conventional and non-conventional energy sources. The artificial neural network will anticipate, predict and exploit idea when generation is insufficient to meet the load demand. If the demand for the load is more than a generation, the artificial neural network will acknowledge the specific …
Published in Journal of Power Electronics and Power Systems · Vol. 7, Issue 3, 2017 · pp. 17–29 Read article
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Detecting Hateful Content on Social Media: A Survey
Abstract: Spreading hatred over social media is a growing menace. Whether in the form of discrimination, cyberbullying or trolls, malicious posts have pestered users throughout the globe. To overcome this nuisance, an automated hate text detector has almost become a necessity. Despite a few inherent limitations that hate speech detection has, there is work going on in this field,andthe scope for progress remains. Besides various techniques used in the state-of-the-art, this …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 7, Issue 3, 2020 · pp. 1–9 Read article
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A Review Paper on Comparison of Algorithms for Supervised Machine Learning
Abstract: AbstractSupervised learning attempts to create calculations that help deliver general speculations, which is able to do forecasts about future occasions. The objective of supervised learning is to manufacture a model of the segregation of class names in terms of predictor components. The classifier produced accordingly is then used to allocate class marks to the testing data where the estimations of the indicator elements are known, yet the estimation of the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 4, Issue 3, 2017 · pp. 11–16 Read article
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AI-Driven Psychological Profiling on Social Media: Mechanisms, Ethical Breaches, and Regulatory Challenges in Data Inference
Abstract: This literature review examines AI-driven psychological profiling on social media, analyzing 21 academic studies that focus on machine learning techniques such as supervised learning, deep neural networks, sentiment analysis, and natural language processing. These methodologies infer mental health indicators—such as depression, anxiety, and stress—from users' digital footprints, encompassing linguistic patterns, engagement metrics, and temporal behaviors. While these tools offer potential for early detection of psychological distress, they also raise significant …
Published in Recent Trends in Social Studies · Vol. 2, Issue 1, 2025 · pp. 1–7 Read article
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Network Intrusion Detection System Using Decision Tree
Abstract: This paper presents a novel approach to network intrusion detection systems (NIDS) using advanced decision tree algorithms to address critical limitations in existing IDS solutions. Traditional IDSs often struggle with high false positive and negative rates, lack of scalability, and poor interpretability. Our proposed IDS leverages decision trees to enhance detection accuracy, interpretability, and scalability, thereby improving network security. Decision trees are chosen for their adaptive learning capabilities, transparent decision-making …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 22–33 Read article
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Hybrid approach for emotion detection in text using spatial-temporal features
Abstract: In recent years, the incredible exploratory growth of online data in social media mainly Twitter has to lead to growing attention of researchers towards Affective analysis of social media streams. Around 500 million tweets are generated per day all around the globe. Classification of these tweets into different affective classes is an arduous task. We propose a novel approach of classifying a tweet into Ekman’s six basic emotion classes using …
Published in Journal of Advancements in Robotics · Vol. 5, Issue 1, 2018 · pp. 34–44 Read article
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Using Machine Learning to Guess Photochemical Reaction Pathways
Abstract: Photochemical reactions are crucial to many activities in the fields of energy conversion, environmental cleanup, and synthetic chemistry. However, predicting their causes and results effectively is still very hard since they entail excited electronic states, nonadiabatic transitions, and complicated potential energy surfaces. Machine learning (ML) has been a powerful technique to go along with classic quantum chemistry methods in the last few years. It offers better prediction capability and lower …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 3, Issue 2, 2025 · pp. 01–12 Read article
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Advancement in Image Classification: Media Player Control Using Hand Gestures
Abstract: We explore the development of picture categorization methods in this paper, with an emphasis on how they are used to manipulate media players with hand gestures. Our investigation focuses on the development of machine learning techniques, particularly on supporting vector machines (SVM) and convolutional neural networks (CNN). SVMs are used to identify and authenticate people from digital photos or video clips, but CNNs are great at face detection, which is …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
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Comparison of K-nearest Neighbor and Artificial Neural Network Classifiers for the Detection of Breast Cancer
Abstract: Breast cancer is the most common type of cancer seen in women in the present day, which is also considered a life-threatening disease. If this cancer can be detected in its early stage it can be a lifesaver for many people around the world. Machine Learning techniques have become one of the hotspots for predicting the early diagnosis of breast cancer. This research work experiments with the two most popularly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 78–83 Read article
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Accelerating Unsupervised Feature Learning: Parallelized Training of Denoising Autoencoders
Abstract: Unsupervised representation learning has become a cornerstone of contemporary machine learning, enabling algorithms to extract informative features from un-labelled, high-dimensional data. This work investigates the efficacy of stacked denoising autoencoders (SDAEs) trained via parallelized stochastic gradient descent (SGD) as a scalable approach to feature extraction. By strategically leveraging multi-threaded computation, our study systematically examines the trade-offs between increased parallelism, training efficiency, and the preservation of model accuracy. Experiments on the …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 56–64 Read article
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Using Machine Learning for Key phrase Extraction in Digital Libraries
Abstract: Machine learning has revolutionized various aspects of information retrieval, including key phrase extraction in digital libraries. Key phrase extraction is crucial for summarizing and categorizing vast amounts of textual data, enabling efficient search and retrieval processes. This study explores the application of machine learning techniques for automatic key phrase extraction in digital libraries. We review various supervised and unsupervised learning algorithms, including deep learning models, that are employed to identify …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 8–13 Read article
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A Machine Learning Approach to Forecasting Outcomes in Limited Overs Cricket
Abstract: This study explores the application of machine learning techniques to forecasting outcomes in limited overs cricket matches, with a particular focus on One Day Internationals (ODIs). The research investigates how classification algorithms can be effectively utilized to analyze both contextual and dynamic factors that influence match results, including venue details, toss decisions, team strength, and historical performance records. By employing a structured methodology encompassing feature selection, data preprocessing, model training, …
Published in Recent Trends in Sports · Vol. 2, Issue 2, 2025 · pp. 09–19 Read article
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Analysis of Various Diabetes Prediction Techniques Based on Machine Learning
Abstract: AbstractData mining is a method of mine from the underdone data for maintains useful information. In organize to obtain fundamental knowledge it is important to remove bulky quantity of data. This method is consist of many important components like data cleaning, data selection, data integrity, pattern evaluation with data mining engine and database with graphical user interface. Diabetes is a chronic disease that is also known as Non‐Insulin Dependent Diabetes …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 8, Issue 1, 2021 · pp. 17–22 Read article
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Image Classification using Convolutional Deep Neural Networks
Abstract: Thousands of images are generated every day, which implies the necessity to classify and access them by an easy and faster way. The main objective of classification is to identify the features occurring in the image. Neural networks (NNs), inspired by biological neural system, are a family of supervised machine learning algorithms that allow machine to learn from training instances as mathematical models. NNs have been widely applied in the …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 5, Issue 3, 2018 · pp. 7–14 Read article
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Crop Yield Prediction Using Machine Learning Algorithm Based on Climate Variables
Abstract: India's economy is based primarily on agriculture, as over 50% of the country's population depends on it for their livelihood. The long-term viability of agriculture is seriously threatened by variations in the weather, climate, and other environmental factors. Because machine learning provides tools for decision assistance in agricultural yield prediction, including guidance on which crops to plant and when to plant them during the growing season, it is essential to …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 49–52 Read article
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Deep Learning for Earth Observation Using Satellite Imagery: A Comprehensive Review
Abstract: Earth observation (EO) satellites provide continuous, large-scale information about the Earth's land, oceans, atmosphere, vegetation, infrastructure, and environmental conditions. The rapid growth of multispectral, hyperspectral, synthetic aperture radar (SAR), thermal, and high- resolution satellite missions has generated large volumes of heterogeneous spatial and temporal data. Conventional image-processing and machine-learning techniques often require manually designed features and may have difficulty representing the complex spatial, spectral, temporal, and multimodal characteristics of satellite …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 15, Issue 2, 2026 Read article
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Hybrid HMM/ANN Models for Improving Offline Handwritten Text Recognition
Abstract: AbstractIn this approach, it proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing the unconstrained offline handwritten texts. Handwritten image normalization from a scanned image includes several steps, usually it begin with image cleaning, page skew correction, and line detection. For handwritten text line image several pre-processing steps to reduce variation in writing style are performed like slope and slant removal and character size …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 3, Issue 3, 2016 · pp. 16–23 Read article
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A Detailed Survey of Machine Learning Applications, Methods, and Future Prospects in Agriculture
Abstract: Agriculture is undergoing a digital transformation driven by machine learning (ML) and artificial intelligence. The integration of ML techniques with data from sensors, drones, satellites, and IoT devices has enabled precision agriculture, early disease detection, optimized resource use, and improved yield prediction. This paper presents a comprehensive review of machine learning applications in modern agriculture, covering key areas such as crop monitoring, soil analysis, irrigation scheduling, pest, and disease detection, …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 1, 2026 · pp. 39–45 Read article