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1688 articles for “accuracy”
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Tropical Change LULC Forestry Data Assessment to Classify Accuracy and Kappa Statistics
Abstract: Forests can be categorized as tropical evergreen, feedstock forests, and so forth. In other classes, the goal of this mission is to compute regions of various woodland kinds in India using photograph processing techniques. This information is very beneficial for organizations such as woodland and environmental protection departments to recognize if the wooded area areas are growing or no longer and therefore take the necessary steps. In order to calculate …
Published in Journal of Open Source Developments · Vol. 7, Issue 1, 2020 · pp. 19–23 Read article
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A Real-time Visualization Framework to Enhance Prompt Accuracy and Result Outcomes Based on the Number of Tokens
Abstract: In the rapidly evolving domain of artificial intelligence (AI), the efficacy of user-generated prompts has emerged as a critical factor influencing the quality of model-generated responses. Current methodologies for prompt evaluation predominantly rely on post-hoc analysis, which often leads to iterative prompting and increased computational overhead. Furthermore, the challenge of “prompt hallucinations,” where AI models produce irrelevant or nonsensical responses, persists as a significant impediment to effective AI utilization. The …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 45–53 Read article
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Accuracy Improvement for Propeller Cavitation Noise Prediction Using UDF
Abstract: Recently, there has been an increase in demand for propulsion systems with higher hydrodynamic performance and lower underwater-radiated noise, as environmental issues are gaining more attention in addition to the traditional military necessity. It is important to reduce cavitation noise when designing propellers of the ships, especially for oceanographic research vessels because they use acoustic instruments and cavitation noise can interfere with their operation. It is well known that, when …
Published in Journal of Experimental & Applied Mechanics · Vol. 16, Issue 2, 2025 · pp. 18–26 Read article
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Speed Control of DC Motor with High Accuracy
Abstract: The aim of developing this project is to control the speed of DC motor. The main advantage in using a DC motor is that the speed-torque relationship can be varied to almost any useful form. To achieve the speed control an electronic technique called pulse width modulation (PWM) is used which generates high and low pulses. These pulses vary the speed in the motor. For the generation of these pulses …
Published in Journal of Microelectronics and Solid State Devices · Vol. 2, Issue 3, 2015 · pp. 29–37 Read article
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Speed Control Using AT89c51 with High Accuracy
Abstract: The aim of developing this project is to control the speed of DC motor. The main advantage in using a DC motor is that the speed-torque relationship can be varied to almost any useful form. To achieve the speed control an electronic technique called pulse width modulation is used which generates high and low pulses. These pulses vary the speed in the motor. For the generation of these pulses, a …
Published in Journal of Semiconductor Devices and Circuits · Vol. 2, Issue 2, 2015 · pp. 28–35 Read article
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A Case based Practical Approach for Novel Data Transformation to Enhance Accuracy of Decision Tree Ensembles
Abstract: AbstractIf we talk about any real world situation then we can see that all the situations dramatically changes as the time passes. If we say this statement in some technical form, concepts changes gradually. This situation is called as Concept Drift that is the core of any approach. Until we cannot get the accurate output for a given input parameter, the concepts will concurrently change. To overcome this situation we …
Published in Journal of Computer Technology & Applications · Vol. 5, Issue 1, 2014 · pp. 1–6 Read article
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Fuzzy Logic System Based MPPT to Improve the Accuracy, Reliability and Power Quality of Grid Integrated Photovoltaic (PV) System
Abstract: Abstract: In this piece of research, a Fuzzy logic based system is designed for output voltage and frequency control of a Grid Integrated PV System. Photovoltaic (PV) energy generation system provides the opportunity to capture more power than other turbines. Distributed power generation systems based on renewable energy sources are attracting the market. In this regard, grid-connected photovoltaic (PV) plants are becoming a common technology to generate energy and its …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 8, Issue 3, 2020 · pp. 1–9 Read article
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An Accuracy Improvement in Facial Verification through Siamese Neural Networks
Abstract: Due to the ever-growing number of digital photos on the Internet, the task of automatically analysing images to extract semantic content has become quite important in recent years. In order to successfully organize the photographs, image indexing and image retrieval strategies need top-notch, effective image analysis and pattern recognition algorithms that can extract relevant semantic data. Face recognition plays a critical role in a variety of real-world applications, including AI-driven …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 10, Issue 2, 2023 · pp. 1–9 Read article
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Fuzzy Algorithm Based Sentiment Analysis: Enhancing Accuracy and Interpretability
Abstract: Sentiment analysis is the technique of determining the point of view, polarity, and intensity of a text. A poll found that 93% of consumers read online evaluations before making a decision. Manually analysing these judgements is challenging. To determine the review's overall sentiment or thinking polarity, sentiment analysis may be employed. This study's goal is to classify product assessment qualities as positive, negative, or neutral. Polarity labelling, feature extraction, and …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 10, Issue 2, 2023 · pp. 1–7 Read article
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Alzheimer disorders diagnosis system design using machine learning for EEG signal
Abstract: The diagnosis of Alzheimer's disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered …
Published in Journal of Control & Instrumentation · Vol. 14, Issue 4, 2023 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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Deep Learning-Based Pneumonia Diagnosis: A Comparative Review of Models and Metrics
Abstract: Pneumonia is a common viral infection that affects a large percentage of people worldwide. It is more common in developing and impoverished areas because of factors like poor sanitation, crowded living quarters, pollution in the environment, and restricted access to medical facilities. In order to improve survival chances and gain access to therapeutic therapies, pneumonia must be diagnosed as soon as possible. A type of artificial intelligence called deep learning …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 3, 2024 Read article
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Optimizing Sampling Techniques Using Fuzzy Set Theory: A Comprehensive Approach
Abstract: Sampling is a critical process in statistics, used to estimate population parameters without needing to examine the entire population. Traditional sampling methods, such as simple random sampling, stratified sampling, and cluster sampling, face limitations when applied to complex or heterogeneous populations with imprecise boundaries. These methods often fail to accurately represent populations with overlapping characteristics or missing data, resulting in sampling bias and reduced accuracy. To address these challenges, this …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 1, 2025 · pp. 29–43 Read article
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Artificial Intelligence in Diagnostics: Advancements, Challenges, and Future Prospects
Abstract: AI is changing (and will change) healthcare as we know it, and diagnostics might be the specialty that feels the most discomfort. Artificial intelligence-based analytical systems are facilitating the detection, diagnosis, and treatment of a variety of diseases, with better accuracy, speed, and results. Now, this abstract investigates the role of AI in diagnostics, scouring its elements, landmark techniques, transformative impact and future overview. This article explains AI and discusses …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 3, 2025 · pp. 8–17 Read article
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X-Ray Insight: Deep Learning-Enhanced Detection and Grading of Knee Osteoarthritis
Abstract: Osteoarthritis (OA) is the most prevalent form of arthritis affecting the knee. It is a degenerative joint disease characterized by the gradual deterioration of cartilage, typically impacting individuals aged 50 and above, although it can also occur in younger people. The condition progresses slowly, with symptoms intensifying over time, leading to significant pain and discomfort. Early diagnosis and intervention can significantly alleviate pain and enhance the quality of life for …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 2, Issue 2, 2024 · pp. 1–6 Read article
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Detection and Classification of Diabetic Retinopathy Using Deep Learning Techniques
Abstract: This project delves into the evaluation of three prominent deep learning architectures Basic CNN, ResNet, and DenseNet for their efficacy in detecting diabetic retinopathy from retinal images. Utilizing a diverse dataset, the study employs standard deep learning frameworks to train and validate each model. The focus extends to exploring the potential benefits of transfer learning on a limited dataset. Evaluation metrics like specificity, sensitivity, and accuracy are employed for a …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 64–69 Read article
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Early Autism Diagnosis: Machine Learning Models and Their Effectiveness
Abstract: Diagnosis is of utmost importance for timely intervention and support. However, traditional diagnosis methods, which are based on subjective assessment, are delayed. This project explores the role that machine learning techniques might play in enhancing the accuracy and effectiveness of ASD detection. Several state-of-the-art classification algorithms were benchmarked using a dataset from Kaggle. Logistic Regression, XG Boost, Random Forest, Decision Tree, and Gradient Boosting were taken into consideration. Other performance …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 Read article
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A Framework for Privacy-preserving AI Models in Cloud Computing: Challenges and Solutions
Abstract: The growing adoption of cloud computing for deploying artificial intelligence (AI) models has led to significant advancements in sectors such as healthcare, finance, and e-commerce. However, the integration of AI with cloud computing raises critical privacy concerns, particularly when handling sensitive data. This paper presents a comprehensive framework for implementing privacy-preserving AI models in cloud environments, addressing the unique challenges, and proposing effective solutions. The suggested framework employs advanced privacy-preserving …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 3, 2024 · pp. 1–12 Read article
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Comparison and Analysis of Facial Emotion Detection Using Various Deep Learning Neural Networks
Abstract: Facial emotion recognition employs Convolutional Neural Networks (CNNs), Residual Networks (ResNet), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNNs) to automatically identify various emotions, including disgust, anger, fear, happiness, sadness, surprise, and neutrality. This study utilizes transfer learning along with data preprocessing techniques such as rotation, flipping, brightness adjustment, and enhancement methods. Traditional machine learning models achieve an accuracy range of 45 to 50%. In contrast, our proposed …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 37–42 Read article
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Enhancing Facial Recognition: Assessing CNNs for Detecting Image Manipulation
Abstract: Deepfake technology, powered by highly advanced deep learning models, has raised significant concerns regarding media manipulation, identity theft, and the spread of online disinformation. Due to the increasing sophistication of deepfake content, traditional forensic methods often fail to detect such artificially generated images with high accuracy. Consequently, deep learning-based approaches have become essential in combating this challenge. This study compares six prominent deep learning architectures: VGG16, ResNet50, MobileNetV2, InceptionV3, EfficientNetB0, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 27–36 Read article