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65 articles for “Unsupervised”
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Unsupervised Probabilistic Debugging
Abstract: We presented an unsupervised probabilistic approach for the debugging of the programs; where the focus is on diagnosing the wrong answers based on the proposed unsupervised semantic parsing algorithm. This approach can be implemented over other programming language. The parsing of the test cases are heuristically employed with the proposed parsing algorithm which reduces the computational complexity and rationally give better performing algorithms.
Published in Recent Trends in Programming languages · Vol. 1, Issue 3, 2014 · pp. 13–15 Read article
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Emotion mining using Unsupervised Learning
Abstract: Social networks are considered as the most abundant sources of affective information for Sentiment and Emotion Classification. Emotion Classification is the challenging task of classifying emotions into different types. Emotion is a mental state observed by behavioral or developmental changes. Emotions being universal, the automatic exploration of emotion is considered as the difficult task to be performed. A lot of the research is being conducted in the field of automatic …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 3, 2018 · pp. 24–34 Read article
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Country Cluster Visualization Based on Agricultural Imports: Unsupervised Learning Approach
Abstract: The clustering algorithm used in this analysis makes it easier for policymakers to understand performance metrics. K-means clustering has been demonstrated to be a tool for analyzing countries' agricultural imports and a visualization tool for interpreting the results in this study. Authors have used agricultural imports from 190 nations from Knoema, a web-based open data platform. Cereals, meat, and coffee imports from 190 nations in 2016 are included in the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 1–7 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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Unsupervised Learning Techniques in Neural Network for Face Recognition
Abstract: This paper represents the review of systematic comparison between of unsupervised learning algorithms for face recognition. In neural network, different algorithms are used for face recognition. Each algorithm has different accuracy factors. Commonly these methods asset a set of base images and serve faces as linear combo of images. In our proposed review, firstly we discuss about the face detection using neural network along with the concept of digital image …
Published in Journal of Advancements in Robotics · Vol. 7, Issue 1, 2020 · pp. 25–29 Read article
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An Unsupervised Common Sense-based Learning Framework for Emotion Detection and Classification in Textual Social Data
Abstract: AbstractEmotions are inherent to human behavior and generally expressed in response to the stimuli from the external environment or some internal processes. With the widespread use of online social platforms, people often express themselves using tweets, blogs and posts which generally include affective text. The topic of emotion detection and recognition has been widely studied in computational science and in fact a separate area “Affective Computing” is dedicated to its …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 4, Issue 3, 2017 · pp. 44–51 Read article
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Context Inference: Light-Weight Online Unsupervised Posture Detection by Smartphone Accelerometer
Abstract: This paper proposes a mild-weight online classification method to locate the user-centric postural movements, inclusive of sitting, standing, walking, and jogging, through smartphones. These moves are named user states considering that they're inferred after the evaluation of records acquired thru the accelerometer sensor built-in smartphones. To distinguish one-person country from some other, much research may be observed in the literature. But, this has a look at differs from others via …
Published in Journal of Control & Instrumentation · Vol. 11, Issue 3, 2020 · pp. 12–20 Read article
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A Review on MR Brain Image Segmentation Based on Different Techniques
Abstract: In past few years, the growth in Magnetic Resonance Imaging (MRI) provided a new way to detect and diagnose the brain related problems such as Alzheimer, schizophrenia and brain tumor. Many supervised and unsupervised techniques are available for image segmentation. In medical field supervised and unsupervised segmentation both are available but unsupervised is in more demand then supervised because it requires external assistance. Whereas unsupervised segmentation reflects better results. In …
Published in Journal of Operating Systems Development & Trends · Vol. 2, Issue 2, 2015 · pp. 9–14 Read article
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Early Detection of Alzheimer’s Disease Using Machine Learning Techniques
Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative condition impacting a large global population. Detecting AD early is critical for timely intervention and effective management. Conventional diagnostic approaches involve cognitive assessments and neuroimaging, which are often lengthy, costly, and prone to human error. In this paper, we propose a novel approach for early detection of AD using machine learning techniques applied to multimodal data, including neuroimaging, cognitive assessments, and biomarkers. Our …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 32–43 Read article
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Efficient Clustering Techniques for Data Stream Mining
Abstract: Data mining mainly works on a massive database for storing heavy amount of data. It is generally essential for extracting the meaning insights from the massive, continuously growing database. The traditional method often struggles with sheer volume and the dynamic nature of the modern data. Data stream mining allows for the real-time analysis, means insights are generated as the data arrives, and not after the long batch process. This continuous …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 26–32 Read article
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Intelligent Systems: A study on AI and Machine learning
Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are dynamic branches of computer science that focus on developing systems capable of executing tasks commonly associated with human intelligence. These activities encompass making choices, resolving issues, understanding language, identifying patterns, and learning through experience. Artificial Intelligence refers to the broad area of designing systems and frameworks that enable machines to perform tasks resembling human thought and behavior. This field integrates diverse technologies …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 Read article
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Applications of Machine Learning Algorithms in Health Data Science (HDS) for Next Research Directions: A Survey Report
Abstract: At present time, data science is the big trend in computer science. The functioning of this technology is purely based on other advanced technology known as machine learning (ML). Data science and ML are subsets of artificial intelligence (AI). When a process of data science is used in healthcare systems, the new system is known as health data science (HDS). HDS is a branch of data science used to handle …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 16–21 Read article
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Extractive Text Summarization: An Application Based Study
Abstract: Text summarization is an essential tool for extracting important information from lengthy texts or documents. Text Summarization has two main methodologies namely: Extractive Summarization and Abstractive Summarization. This study concentrates on extractive summarising, which selects significant sentences straight from the source material to create a summary. It is a popular option for many practical applications since it frequently produces summaries that are more accurate in terms of substance. In abstractive …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 41–48 Read article
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An Analytical Review of Machine Learning Methodologies
Abstract: Machine Learning (ML) is a dynamic and rapidly developing area of computer science that enables the system to learn from data and improve its performance without clear programs. Rooted in statistical theory and computer algorithms, ML has become a major technology that progresses in artificial intelligence. It strengthens the detection of the recommendations and speech for extensive applications from autonomous vehicles and medical diagnoses. This paper has reviewed the basics …
Published in Recent Trends in Mathematics · Vol. 3, Issue 1, 2026 · pp. 13–21 Read article
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Automatic Fake News Detection: A Review
Abstract: Misinformation has exploded in the last two decades with the development of digital technologies. The damage caused by it to social harmony, peace, social trust, justice, and democracy has increased the necessity of automatic identification of bogus news and intervention. As more and more content is being available, a person may not deliver much attention to a news piece and may assume it to be reliable just from reading headlines. …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 10, Issue 1, 2023 · pp. 29–35 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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Educating Compilers to Learn: Utilizing Machine Learning for More Brilliant Code Optimization
Abstract: This study explores the use of machine learning (ML) approaches to compiler optimization. The now-traditional static compilation techniques are transformed into adaptive, dynamic systems capable of making context-specific advancements. Traditional compilers rely mostly on heuristic or rule-based optimization techniques. While these techniques work well in general cases, they consistently fail to adapt well within the limits of code structures that modern machines display. This limitation is especially acute in today's …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 50–54 Read article
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AI for Cybersecurity: Deploying Machine Learning for Network Traffic Anomaly Detection
Abstract: The growing sophistication of cyberattacks and the growth of network traffic necessitate sophisticated anomaly detection methods. This study overviews the use of artificial intelligence (AI) and machine learning (ML) to counter these challenges, as noted in current studies. It analyses supervised learning (SVM, Decision Trees), unsupervised learning (K-means, DBSCAN), and deep learning (CNNs, RNNs, Auto-encoders) approaches, considering their strengths and weaknesses. The research integrates current developments in AI/ML-based network anomaly …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 2, 2025 · pp. 1–10 Read article
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Data-Driven Design Framework for Biofunctional Polymer Composite Materials
Abstract: This paper introduces a knowledge-based design platform of biofunctional polymer composite substances through the combination of machine learning, materials informatics, and digital twins applications. The framework allows the effortless forecasting and maximization of mechanical, biological and degradation characteristics based on supervised, unsupervised and deep learning models. A materials database is accompanied by the AI algorithms to find the best material compositions and microstructure-property relationships. Experimental validation proves to be more …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Graph based Word Sense Disambiguation for Gujarati
Abstract: Due to high Semantic Ambiguity that is associated with language, Word Sense Disambiguation is an open problem of Natural Language Processing and Ontology in Computer Linguistics. For instance, the Gujarati word પુવૅ (Purva) can mean East and Past. The task of automatically assigning the most suitable meaning to a polysemous word can be defined as Word Sense Disambiguation. Several approaches have been done, from Dictionary-and Knowledge-based method that uses Lexical …
Published in Journal of Advancements in Robotics · Vol. 5, Issue 2, 2018 · pp. 35–43 Read article