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1781 articles for “R S Data analysis” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
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A Hybrid Machine Learning Approach for Cardiovascular Disease Prediction
Abstract: Heart disease ranks among the top causes of death globally. Accurately predicting cardiovascular conditions has become a key challenge in the realm of clinical data analysis. It has been shown that machine learning is an effective means of assisting with predicting and decision-making based on the large volume of data produced by the medical industry. In this study, we describe a unique approach that increases the prediction accuracy of heart-related …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 69–75 Read article
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Stress Analysis of a Mechanical Joint from Recorded Data of a Single Component of Displacement
Abstract: The ability to fully stress analyze a pinned/bolted aluminum connector, including the contact stresses at the pin-plate interface, using measured displacement information is demonstrated. This is accomplished by using recorded data from only either one or the other of the two in-plane components of displacement. The relatively small pin-hole clearance results in the pin contacting the edge of the hole for an arc distance exceeding 180 degrees. Reliability of experimental …
Published in Journal of Experimental & Applied Mechanics · Vol. 6, Issue 3, 2015 · pp. 23–41 Read article
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Smart Tracks: Navigating Urban Transit with IoT-enabled Bus Networks
Abstract: Public transportation is a lifeline for countless individuals globally, providing essential connectivity for daily commutes to work and school. However, numerous challenges such as bus delays and inefficient space on the buses often hinder the travel experience. It's crucial to resolve these issues to achieve an efficient public transport system. A promising solution lies in the adoption of an IoT-based application. With this technology, one can track the real-time location …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 1–8 Read article
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Comparative Analysis of Data Augmentation Techniques in CNN-based Classification of Atelectasis
Abstract: This research delves into the critical issue of atelectasis, its causes, and potential complications if left untreated. Leveraging deep learning algorithms, particularly convolutional neural networks (CNN), the paper explores their application in medical image analysis, focusing on the detection of atelectasis using the “chestX-ray8” database. The study compares various data augmentation techniques for improved accuracy, showcasing the importance of augmentation in enhancing model generalization. Through meticulous experimentation and evaluation, the …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 1–8 Read article
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A Review of Machine and Deep Learning Techniques for Cyber Security
Abstract: Nowadays in the digital landscape, cyber threats and attacks are increasing in an exponential manner, posing server risks to organizations and critical infrastructures. Data breaches often result from sophisticated threat models that exploit vulnerabilities in networks, systems and user behaviors. Cyber solutions are increasingly incorporating machine learning and deep learning to prevent and mitigate these security issues. These technologies have the potential to detect anomalies, classify threats and predict potential …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 01–07 Read article
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Real-Time Analysis of E-Waste Monitoring Using Data Visualization in Power BI
Abstract: The exponential growth of electronic waste (e-waste) in India poses significant environmental and public health challenges, necessitating robust monitoring, management, and disposal strategies. This project, Real-time analysis of e-waste generated across different countries in the world and also survey report of India, seeks to provide a comprehensive assessment of e-waste production patterns, utilizing real-time data to capture dynamic shifts in waste generation and collection. By leveraging Power BI for advanced …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 2, 2025 · pp. 1–7 Read article
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Vegetation Analysis with Reference to Topographic Variables using Remote Sensing Data
Abstract: AbstractThe study was carried out in Kalsa Watershed, Uttarakhand, India to analyze the vegetation composition, topographic attributes and distribution of different vegetation types in relation to topography. Remote Sensing data of Indian satellite IRS 1D-LISS III sensor was used to map vegetation and land use. Survey of India (SOI) topographic map was used to generate the Digital Elevation Model (DEM). A total of 10 vegetation and land use classes were …
Published in Research & Reviews : Journal of Ecology · Vol. 3, Issue 1, 2014 · pp. 31–40 Read article
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A Random Forest Approach to Navigating Cryptocurrency Market Fluctuations
Abstract: This study looks at the main elements influencing daily price variations to improve our analysis and prediction of Bitcoin values. Our forecasting algorithm is based on comprehensive data that we have collected and analyzed over the last few years. Because the Random Forest algorithm provides more accurate forecasts than previous techniques, that is why we chose it. Predicting the price swings of cryptocurrencies, like Bitcoin, can be challenging due to …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 2, 2024 · pp. 7–11 Read article
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Using the R Software to Conduct Network Meta-Analysis on Diabetes Data” h
Abstract: A meta-analysis's goal is typically to quantify the total treatment effect and draw conclusions regarding the differences in effects between the two treatments. Meta-analysis is a quantitative method for combining the findings of several studies in the social and medical sciences.. A meta-analysis might be of three typical forms. Network-wide, pairwise, and multivariate meta-analyses are all possible. The integrated assessment of more than two treatments is generally made possible by …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 2, 2024 · pp. 57–76 Read article
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Fault Detection in Solar PV Systems Integrated with the Power Grid: Evaluating Logistic Regression through Confusion Matrix Analysis
Abstract: This paper proposes a method for failure detection in grid-integrated solar photovoltaic (PV) systems using logistic regression and real-time sensor data. The approach effectively classifies and identifies seven distinct fault types. The developed model demonstrates a high fault identification accuracy, ranging from 93% to 96.5% across various fault types and operational conditions. By leveraging logistic regression, the system utilizes key independent variables that significantly influence the classification process. Additionally, the …
Published in Journal of Power Electronics and Power Systems · Vol. 15, Issue 2, 2025 · pp. 45–52 Read article
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Adverse Drug Reaction Associated with Vitamins: An Analysis of Spontaneous Reports from Pharmacovigilance Programme of India Database
Abstract: The objective of this study was to describe adverse events with the use vitamins and to identify risk factors. A retrospective study was carried out from the spontaneous adverse drug reaction (ADR) reports for the period April 2011 to March 2013 by National Coordination Centre, Pharmacovigilance Programme of India (PvPI). 29 reports of vitamins were received by NCC-PvPI, which comprised approximately 0.064% of the total 45,297 reports received at NCC-PvPI. …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 5, Issue 3, 2015 · pp. 12–18 Read article
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Interest Level Prediction in Rental Properties Using Data Science
Abstract: A key component of forecasting home prices and rental patterns is real estate market analysis. Data science, data mining methodologies, and statistical models are some of the strategies that have been created in recent years to solve this problem. A few problems are still required to be resolved, such as the obstacles caused by the availability and quality of the data; the presence of outliers, missing values, and inconsistent formats …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 1, 2024 · pp. 28–34 Read article
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Integrating Sensor Technologies and Machine Learning for Detection and Mitigation of Structural Deformity and Slope Failure in Opencast Mines
Abstract: With furtherance in the mining industry, accidents due to slope failure are frequent in mining sites. Slope instability, a complex process, seriously threatens the miner’s life and properties. The damage inflicted by slope failures in the recent past has pulled the attention of authorities toward implementing disaster risk reduction measures. This research aims to develop an innovative approach that combines sensor technologies and machine learning techniques to detect and mitigate …
Published in Journal of Communication Engineering & Systems · Vol. 13, Issue 3, 2023 · pp. 38–45 Read article
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Development and Validation of HPTLC Methods for Estimation of Trimebutine Maleate in Pharmaceutical Formulation
Abstract: A simple and rapid high-performance thin-layer chromatographic (HPTLC) method was developed and validated for quantitative determination of trimebutine maleate in single dose formulation. Trimebutine maleate was chromatographed on silica gel 60 F 254 TLC plate using methanol–toluene (8:2 v/v) as mobile phase. The plates were developed to a distance of 8 cm at ambient temperature. Trimebutine maleate was quantified by densitometric analysis at 271 nm. The method was found to …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 8, Issue 1, 2017 · pp. 17–22 Read article
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Data-Driven Machine Learning Approach for Vehicle Fuel Economy Prediction and Performance Monitoring Using Real-World OBD Data
Abstract: Modern passenger vehicles generate large volumes of operational data through On-Board Diagnostics (OBD) systems, enabling continuous observation of vehicle performance under real-world driving conditions. However, much of the existing research mainly analyses previously recorded data and does not provide predictive mechanisms for monitoring vehicle performance under dynamically varying operating conditions. This study presents an AI and machine learning–based method for predicting and monitoring real-world vehicle performance and fuel economy using …
Published in Trends in Machine design · Vol. 13, Issue 2, 2026 · pp. 47–66 Read article
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A Data-Driven Analysis of Machine Learning Classification Models for Reliable Crop Yield Prediction
Abstract: The adoption of ML technologies in agriculture is reshaping farming practices, empowering producers to make informed, data-oriented decisions that improve yields, sustainability, and long-term resilience. In mango cultivation, ML analyzes data from weather, soil, and pests to optimize irrigation, fertilization, and pest control. Predictive analytics help forecast ideal farming practices, minimizing resource wastage and improving yield. Real-time monitoring and image-based disease detection allow timely interventions to maintain plant health and …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 1, 2026 · pp. 12–17 Read article
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MIRDcalc & OLINDA/EXM Dosimetry Software Analysis by SPECT/CT Scan Data of Lu-177 DOTATATE Radionuclide Therapy of NET Patients
Abstract: Accurate dosimetry is essential in nuclear medicine for optimizing radionuclide therapies and ensuring patient safety. In Radiopharmaceutical dosimetry the Medical Internal Radiation Dosimetry (MIRD)Society is the pioneer in organ-level dosimetry providing the fundamental basis for commonly used clinical and research dosimetry software like MIRDOSE and OLINDA/EXM. Recently, in MIRD Pamphlet No. 28, Part 1, the MIRD committee of the Society of Nuclear Medicine and Medical Imaging presented a new Software …
Published in Journal of Nuclear Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 48–63 Read article
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Spatiotemporal Analysis of Waterbodies in Ranchi District, Jharkhand using Satellite Data: An Input for Jal Shakti Abhiyan
Abstract: Keeping in the view of water conservation the Government of India has initiated the Jal Shakti Abhiyan, which needs the inventory of waterbodies for further planning and monitoring. Lakes and Ponds in year 2005 and year 2018 were mapped in Ranchi district at 1:5000 scale using high resolution satellite data and reservoirs at 1:50,000 scale using moderate resolution satellite data. Seasonal fluctuation in water extent of reservoirs was estimated using …
Published in Journal of Remote Sensing & GIS · Vol. 11, Issue 1, 2020 · pp. 27–35 Read article
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Land Surface Temperature Analysis in an Urbanising Landscape through Multi-Resolution Data
Abstract: Rapid changes in the land use and land cover of a region have become a major environmental concern in recent times. This has lead to unsustainable development with the reduction of green spaces and also changes in local climate and formation of urban heat islands (UHIs). Monitoring and management of land use dynamics would help in land use planning and mitigation of environmental impacts.The main goal of this paper is …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 1, Issue 1-3, 2012 · pp. 12–21 Read article
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Fractal Analysis of Vertical Component of VLF Electric Field Related to Major Shallow Earthquakes (5.5 < M ≤ 5.9, depth ˂ 10 km) occurred in Indian Ocean and China
Abstract: Recording of amplitude of vertical component of VLF electric field is continued at Chaumuhan, Mathura (27.5° N, 77.68° E) at the frequency f = 3.012 kHz from 24 March 2011 with the help of vertical antenna. In this paper, fractal analysis of VLF data is carried out for the April 2012 and July 2013 months and the fractal dimension is computed using Higuchi’s method to study the influence of three …
Published in Journal of Polymer & Composites · Vol. 11, Issue 7, 2023 · pp. 46–52 Read article