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36 articles for “Bayesian method”
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TextSum: An Automatic Text Summarization Tool using Bayesian Classification
Abstract: Automatic text summarization is the process of summarizing text from one or more documents in order to help the reader to determine if [s]he has to read the document(s) in full. Although automatic summarization is around for many years, there is a renewed interest in this area from the government, industry, and academia. The reason is the availability of large quantities of information due to world wide web and the …
Published in Recent Trends in Programming languages · Vol. 1, Issue 1, 2014 · pp. 1–3 Read article
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Diabetic Level Prediction in Humans: Using Supervised Machine Learning Methods
Abstract: Diabetes could be an unremitting malady or a metabolic sick wellbeing cluster where an individual endures from a pervasive sum of insulin-producing blood glucose within the body or since the cells of the body do not react to affront. Steady diabetes hyperglycemia is the damage to long term organs, particularly lungs, kidneys, nerves, the heart and the veins are connected to weakness and misfortune. The objective of this investigation is …
Published in Recent Trends in Parallel Computing · Vol. 7, Issue 1, 2020 · pp. 1–6 Read article
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Evaluation of Credit Risk of Bank Customers with a Hybrid Approach of Data Mining Techniques
Abstract: Credit risk poses the most significant threat to financial and monetary institutions. Banks strive to offer loans that generate high returns while minimizing risk. Achieving this requires the ability to accurately identify and classify credit customers, both individuals and legal entities, according to their likelihood of fully meeting their obligations. This classification is done using relevant financial and non-financial criteria. The primary goal of this study is to assess the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 63–81 Read article
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Image Preprocessing and Analysis on Eye Fundus Images Segmentation by Using Density Clustering Methods
Abstract: In order to do an automated evaluation of various retinal illnesses such as Diabetic retinopathy, Glaucoma, and Macular Edema, fundus images must be pre-processed first. For many reasons, it's difficult to accurately detect the optic disc. Many blood vessels cross the optic disc, making it difficult to discern the disc's boundaries in fundus images. Lesion regions in diabetic retinopathy look very much like an optic disc's colour and texture, so …
Published in Recent Trends in Sensor Research & Technology · pp. 11–18 Read article
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Image Preprocessing And Analysis On Eye Fundus Images Segmentation By Using Density Clustering Methods
Abstract: Abstract: In order to do an automated evaluation of various retinal illnesses such as Diabetic retinopathy, Glaucoma, and Macular Edema, fundus images must be pre-processed first. For many reasons, it's difficult to accurately detect the optic disc. Many blood vessels cross the optic disc, making it difficult to discern the disc's boundaries in fundus images. Lesion regions in diabetic retinopathy look very much like an optic disc's colour and texture, …
Published in Recent Trends in Sensor Research & Technology · Vol. 8, Issue 3, 2021 · pp. 11–18 Read article
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Seasonal ARIMA for Forecasting Sea Surface Temperature of the North Zone of the Bay of Bengal
Abstract: The behavior of the sea surface temperature (SST) of the north zone of the Bay of Bengal plays an important role for understanding climate changes over Bangladesh. The monthly average of SST of this zone is used in this study which is obtained from January 1900 to December 2009. Box and Jenkins method is used to fit a seasonal auto regressive integrated moving average (ARIMA) model used in forecasting SST …
Published in Research & Reviews : Journal of Statistics · Vol. 2, Issue 2, 2013 · pp. 23–31 Read article
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Machine Learning Assisted Optimization of Nanoscale MOSFET Parameters Using TCAD Simulation
Abstract: This paper presents a machine learning (ML) assisted framework for the multi-objective optimization of nanoscale bulk n-channel metal-oxide-semiconductor field-effect transistors (nMOSFETs) with a 10 nm physical gate length, high-k HfO₂ gate dielectric, and TiN metal gate. Technology computer-aided design (TCAD) simulations employing drift-diffusion transport, Shockley-Read-Hall recombination, Lombardi mobility degradation, and density- gradient quantum correction models are used to generate a parametric dataset of 2,400 device configurations spanning gate length (L), …
Published in Journal of Microelectronics and Solid State Devices · Vol. 13, Issue 1, 2026 · pp. 10–19 Read article
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Improving Polymer Composite Properties Through Reinforcement Learning Guided Prototyping A Novel Approach for Material Engineering
Abstract: Innovative approaches integrating reinforcement learning (RL) and machine learning (ML) into the fields of polymer composite prototyping and soft actuator manufacturing for applications. This new an algorithm utilizing RL optimizes polymer composite fabrication parameters to enhance material properties efficiently. By iteratively adjusting parameters based on predefined objectives, the RL agent guides the prototyping process, promising to revolutionize polymer composite engineering. A finest control method for locked loop control of Shape …
Published in Journal of Polymer & Composites · Vol. 12, Issue 4, 2024 · pp. 208–218 Read article
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Security Vulnerability Assessment Of A Hypothetical Lng Gas Treating Plant Using A Spreadsheet Template
Abstract: The upsurge of terrorism and attacks on chemical plants in several nations, including Nigeria, has necessitated the need for vulnerability studies. This study uses the Security Vulnerability Assessment, Prevention, and Prediction approach to analyze the vulnerabilities of a hypothetical chemical plant using a developed spreadsheet template. With a safety barrier method, an attack model based on security barriers was developed. External, internal, interior, critical, and fail-safe security barriers were proposed …
Published in Emerging Trends in Chemical Engineering · Vol. 10, Issue 3, 2023 · pp. 1–12 Read article
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Behaviors–Observations–Beliefs Model for Enhancement Using Cognitive Reasoning
Abstract: A unified trust management scheme that enhances the security in MANETs using uncertain reasoning, in the existing scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation …
Published in Journal of Open Source Developments · Vol. 3, Issue 1, 2016 · pp. 10–18 Read article
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Speaker Segmentation Using Non Linear Energy Operator Based Variance Spectral Flux
Abstract: AbstractSpeaker segmentation is considered to be a process that attempts to find speaker segment boundaries in a given audio stream. It can be used in various applications of speaker diarization, speaker indexing, word count etc. Classification of speech and non-speech can be obtained by traditional method of variance spectral flux (VSF). In this paper, we investigate the new techniques to perform the speaker segmentation task for multiple speakers in one …
Published in Current Trends in Signal Processing · Vol. 7, Issue 3, 2017 · pp. 1–6 Read article
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Thermo-Structural Machine Learning Framework for Malicious Sample Detection and Validation in Polymer Composite Materials
Abstract: The increased use of polymer composites in aerospace, automotive, biomedical and industrial applications has increased the urgency of developing dependable methods to detect malicious samples with counterfeited resins, unauthorized additives, recycled components, hidden flaws, or purposefully degraded physical properties. Most current machine learning techniques have focused either on isolated spectral analysis or detecting flaws in materials; as such, they are unable to perform joint verification of both chemical authenticity and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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A study on IoT and AI for Predictive Modeling and Control of Infectious Disease Transmission
Abstract: Background: The global response to novel and recurring infectious diseases is frequently hindered by surveillance systems that are slow, siloed, and reactive. Traditional epidemiology relies on retrospective analysis of clinical reports, often missing the critical early phase of autocatalytic spread. The urgency of modern public health necessitates a shift toward real-time, predictive intelligence. Methods: This study investigates the development and deployment of a synergistic paradigm integrating the Internet of Things …
Published in International Journal of Pathogens · Vol. 2, Issue 2, 2025 Read article
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Digital Twin Assisted Intelligent Prediction of Polymer Composite Degradation Under Environmental Exposure
Abstract: Polymer matrix composites (PMCs) deployed in aerospace, marine, automotive, and renewable-energy structures are continuously subjected to coupled environmental stressors — ultraviolet (UV) radiation, moisture ingress, thermal cycling, and mechanical loading — that progressively degrade their mechanical performance. Conventional accelerated ageing tests and empirical lifetime models are time-consuming, destructive, and poorly suited to in-service, asset-specific degradation forecasting. This paper proposes a Digital Twin (DT) assisted intelligent prediction framework that fuses a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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AI-Designed Functionally Graded Polymer Composites for Multifunctional Thin Films
Abstract: The design of multifunctional polymer composite thin films requires simultaneous optimization of mechanical, optical, barrier, and thermal properties—objectives often in conflict when using conventional homogeneous materials. This study presents an artificial intelligence-driven framework for designing functionally graded material (FGM) architectures in polymer nanocomposite thin films. We integrated machine learning with physics-based modeling to optimize compositional gradients across film thickness, achieving superior performance compared to homogeneous and discrete multilayer alternatives. A …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1026–1041 Read article
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AI-Based Discovery of High-Performance Energy Storage Polymer Composites: A Comprehensive Review
Abstract: The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1083–1097 Read article