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334 articles for “feature based model”
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Size-biased Sujatha Distribution with Properties and Application to Model Flood Data
Abstract: In this study, a size-biased version of the Sujatha distribution was proposed to model flood data. The descriptive statistical properties based on moments and the reliability properties of the distribution are discussed in detail along with their derivation and graphical presentation. An interesting feature of the proposed distribution is that it is a member of the exponential family of distributions. A sequential probability ratio test was performed using the proposed …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 1, 2024 · pp. 31–46 Read article
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Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms
Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article
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Design a Framework for Cybersecurity Using Steganography
Abstract: AbstractNowadays with the rapid increase in transmitting data’s through computers, securing these transmitted data is mandatory. The most common and desired technique used to secure these data is Cryptography. Cryptography consists of two steps, namely Encryption and Decryption. But they are not much safe compared to steganography. This study aims to design a framework for cyber security using steganography to increase the security level of data to be transmitted, using …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 6, Issue 1, 2019 · pp. 1–6 Read article
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Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches
Abstract: Stock price prediction is a crucial task in financial analysis, aiding investors and traders in making informed decisions. This study investigates the use of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, for predicting stock prices based on historical market data. The dataset, sourced from Yahoo Finance, consists of time-series stock price data, which is preprocessed, feature-engineered, and visualized to improve prediction accuracy. The model's performance is assessed using …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 29–40 Read article
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Bias Detection and Accuracy Enhancement in Voice-based Banking Authentication Using Deep Learning
Abstract: Biometric systems have become an integral part of how many people access banking services today, and voice verification systems can be a secure and easy-to-use source of banking authentication that does not require any physical contact with the bank or any other person. From the security perspective, these systems would normally provide an effective means of identifying an individual but frequently exhibit bias with respect to demographics such as the …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 2, 2026 Read article
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A Hybrid Multi-Physics Ensemble Deep Learning Framework for Simultaneous Prediction of Thermal and Electrical Conductivity in Functional Polymer-Based Nanocomposites
Abstract: Polymer-based nanocomposites have become promising materials for applications in energy storage, flexible electronics, biomedical devices, aerospace components, and advanced engineering because of their tunable thermal and electrical properties. Reliable prediction of these properties is essential for accelerating material design; however, existing analytical models and conventional machine learning techniques often fail to represent the complex interactions among filler characteristics, polymer matrices, processing conditions, and interfacial transport phenomena. This work presents HMEP-Net, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1062–1082 Read article
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Standard for Exchange of Products (STEP) based feature Recognition System for Prismatic Parts
Abstract: Feature extraction is considered as the bridge between Computer Aided Design (CAD) and Computer Aided Process Planning (CAPP). This work is concerned with understanding the problem of how to manipulate and extract information from CAD solid models by using Standard for Exchange of Product data model (STEP) files and its further use in downstream manufacturing operations. Emphasis is placed on recognizing and classifying manufacturing features from the Boundary Representation (BREP) …
Published in Journal of Production Research & Management · Vol. 1, Issue 1-2-3, 2011 · pp. 73–91 Read article
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Machine Learning Based Smart Aquaponics Farming System
Abstract: For many years, researchers have been studying nutrient management in aquaponic systems. Most have concentrated on adequate nutrition control in an aquaponic setup, but there has been relatively little study on commercial scale applications. For plant growth, it is necessary to measure the level of nutrients present in the soil mixture. In our model, the input data was sourced on some interval of time basis from three commercial aquaponic farms. …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 10, Issue 1, 2022 · pp. 30–37 Read article
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Time Series Methods in Meteorology: A Review of Predictive Models and Applications
Abstract: The accurate prediction of time series data holds substantial significance in various fields, enabling informed decision-making and resource optimization. In this study, temperature variations over time are predicted using the Autoregressive Integrated Moving Average (ARIMA) model. Reliable temperature projections are more important now than ever because of climate change and its effects. For time series prediction problems, the ARIMA model—which is well-known for its ability to capture temporal dependencies in …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 13, Issue 2, 2024 · pp. 35–46 Read article
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LLM-based Chatbot for Course-based Question Answering
Abstract: The “LLM-based Chatbot for Course-Based Question Answering” project addresses the pressing need for tailored and efficient learning tools in education. By using a state-of-the-art Large Language Model (LLM) with a diverse dataset, including textbooks, professor slides, and web scraping data, the chatbot offers accurate and contextually enriched responses to students' course-related queries. Using recent advances in language modeling, this work presents a Longformer-based Language Model (LLM) for constructing a smart …
Published in International Journal of Electronics Automation · Vol. 1, Issue 2, 2023 · pp. 30–41 Read article
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Application of Constraint Satisfaction framework to zero trust security Model
Abstract: The zero-trust era has dawned with the advent of security models which addresses the gap between established security models. As cyber-security risks are advancing with the various natures of attacks, the threat has to be minimized by application of robust security models based on mathematical foundations. This study attempts to rehash few challenges existing in popular trust computing models and tries to bring out the characteristic features of constraint satisfaction …
Published in Journal Of Network security · Vol. 10, Issue 3, 2022 · pp. 1–6 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 · pp. 1–8 Read article
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Innovative Eyewear for the Visually Impaired
Abstract: Object detection systems are essential tools for identifying and locating objects within images or videos. When integrated into spectacles or wearable devices, these systems provide users with real-time information about objects present in their surroundings. This functionality serves diverse purposes, such as assisting visually impaired individuals in navigating their environment or offering augmented reality data to workers during tasks. Region-based Convolutional Neural Networks (RCNN) represent a prominent machine learning model …
Published in International Journal of Optical Innovations & Research · Vol. 2, Issue 1, 2024 · pp. 27–34 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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Iris Recognition System Using Principal Component Analysis based Back-Propagation Learning Neural Networks
Abstract: This paper deals with the iris recognition system using Back-Propagation learning neural network algorithm where Principal Component Analysis technique has been used to reduce the dimension of the iris feature vector. Automated iris localization and segmentation methods have applied to effectively isolate the iris region from pupil and sclera. Circular Hogue transform has used to detect the iris/sclera boundary and pupil/iris boundary. To remove the upper and lower eyelids effects …
Published in Current Trends in Information Technology · Vol. 3, Issue 3, 2013 · pp. 5–11 Read article
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AI- based Prediction of Misinformation Virality Before Wide Dissemination using Attention-based Multi-modal
Abstract: Misinformation on social media has emerged as a critical global challenge, impacting public health, democratic institutions, and societal trust. While existing research has largely concentrated on detecting misinformation after it begins circulating, predicting its virality before wide dissemination remains an underexplored area, limited work addresses predicting its virality before wide dissemination. This paper presents a conceptual framework using attention-based multi-modal deep learning models to estimate the virality of misinformation posts …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 3, 2025 Read article
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Penalization for Unscheduled Interchange based on Availability Based Tariff in Competitive Electricity Market
Abstract: This paper presents a technique for the penalization of unscheduled interchange within deregulated power sector, it derives its features from a successful frequency linked tariff scheme operational in India and known by the name of Availability Based Tariff (ABT). In this paper, combined model consisting of the pool, bilateral and multilateral transaction of deregulated power sector has been considered, the power market is mostly scheduled for 15 min, one or …
Published in Trends in Electrical Engineering · Vol. 3, Issue 3, 2013 · pp. 51–61 Read article
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Identification of English Dialects and Emotions using Spectral and Prosodic Features of Speech Signal Processing
Abstract: AbstractIn this paper, the authors have explored speech features to identify English dialects and emotions. A dialect is any distinguishable variety of a language spoken by a group of people. Emotions provide naturalness to speech. Speech database considered for dialect identification task consists of spontaneous speech spoken by male and female speakers. The emotions considered in this study are anger, disgust, fear, happy, neutral and sad. Prosodic and spectral features …
Published in Journal of Computer Technology & Applications · Vol. 4, Issue 2, 2013 · pp. 10–17 Read article
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Video Anomaly Detection using Kalman Based Support Vector Technique
Abstract: AbstractThe digital video processing technology has boomed since last many years. The video processing is done using sum of absolute differences and with the image processing block set. We first calculate motion vectors between successive frames and use them to reduce redundant information. The method uses optical flow algorithm to calculate changes in the intensity of the pixels of the images. Median filtering is used for background extraction which is …
Published in Current Trends in Signal Processing · Vol. 8, Issue 2, 2018 · pp. 4–11 Read article
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A Review on Lung Cancer Prediction Using Machine Learning
Abstract: Lung cancer continues to be a major contributor to cancer-related mortality across the globe. Timely diagnosis and reliable prediction models play a crucial role in enhancing treatment outcomes and survival rates for patients. The present study focuses on the utilization of machine learning (ML) methods for the prediction of lung cancer. Using datasets that incorporate clinical records, imaging modalities, and genetic profiles, the research assesses the predictive capabilities of multiple …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 3, 2025 · pp. 1–11 Read article