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375 articles for “classification machine learning”
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Leafguard: Smart Plant Health Detection
Abstract: Machine learning techniques, including traditional (shallow) ML, deep learning (DL), and augmented learning (AL), are being increasingly utilized for leaf disease classification. These methods involve feature extraction, data augmentation, and transfer learning to enhance model effectiveness and reduce the need for labeled data. The success of machine learning approaches in this domain hinges on the quality and quantity of data available. LeafGuard is a cutting-edge device with intelligent sensing systems …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 32–39 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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Machine Learning Driven Mobile Price Prediction Using Feature Selection and Parameter Optimization
Abstract: Machine learning calculations are utilized in many fields like money, training, industry, medication, and online business. Machine learning calculations show execution contrasts relying upon the dataset and handling steps. Picking the right calculation, preprocessing and post-handling techniques have incredible significance in accomplishing great outcomes. The Random Forest classifier, K-nearest neighbor classifier, and support vector machine methods are evaluated to forecast mobile phone price categories. The “prediction” dataset which is taken …
Published in Current Trends in Information Technology · Vol. 14, Issue 3, 2024 · pp. 18–25 Read article
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Finding Risk Factors in Thyroid and Cardiovascular System Using Naive Bayesian (NB) Machine Learning Technique
Abstract: Thyroid malady could be a common condition. A TSH test is carried out to check for the capacities of your thyroid organ. Illnesses of thyroid can be caused by conditions which cause thyroid organ over or under function. The test can moreover assist you recognize thyroid conditions some time recently indications happen. It is either as well (hyperthyroidism) or under-active (hypothyroidism), you might know. In case untreated, thyroid clutter may …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 8, Issue 1, 2020 · pp. 1–5 Read article
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Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 45–54 Read article
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Examining the Crowd in Real-time with Deep Learning
Abstract: In this research, a model is proposed that can be used to estimate crowd density in a specific region and to establish social distances in accordance with predetermined rules. This is accomplished utilizing a multi-source model-based approach. In a small public gathering where hand counting is impossible, this technique conducts a survey. To do this, input video frames are extracted, each frame is processed, and then passed to the model …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 1, 2023 · pp. 41–45 Read article
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Advancements in Intrusion Detection: Tackling Imbalanced Network Traffic with Machine Learning and Deep Learning Techniques
Abstract: Malicious cyberattacks can frequently hide enormous amounts of typical data in unbalanced network traffic. It is very stealthy and obfuscating in cyberspace, which makes it challenging for Network Intrusion Detection Systems (NIDS) to guarantee the precision and promptness of detection. This essay investigates. Machine learning and deep learning are utilized for intrusion detection in imbalanced network traffic. It offers a novel method for addressing the problem of class imbalance termed …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 18–24 Read article
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Identifying and Implementing a Machine Learning Model Suitable for Processing Visually Evoked Potential
Abstract: A Brain-Computer Interface (BCI) is a system that translates brain activity patterns into computer commands, bypassing physical movement. Electroencephalography (EEG) is commonly used to acquire signals in BCI research. Visual evoked potentials (VEPs) are brain responses in the visual cortex to visual stimuli. Recent studies show that exposing individuals to flickering at a consistent frequency generates EEG signals synchronized with the stimulation. Efficient extraction of VEP signals begins with preprocessing …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 1–8 Read article
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Music Recommendation System Using Big Data and Machine Learning
Abstract: In comparison to earlier times, there is now a wealth of music available thanks to the development of music streaming services that can be used from mobile devices. Classifying digital music can be timeconsuming and cause fatigue. Obtaining one’s favorite music from the huge music selection has become a problem. The creation of a music recommendation system that can search through music libraries and make song recommendations to users based …
Published in Journal of Open Source Developments · Vol. 9, Issue 3, 2022 · pp. 18–24 Read article
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A Review of Code Defect Likelihood Using ML Methods
Abstract: AbstractSoftware begets defects and defects are inevitable in software design and coding. This paper focused on five machine learning analysis models (MLAM); Support Vector Machine, Random Forest, K-Nearest Neighbor, Classification and Regression Tree, and Linear Discriminant Analysis. These models are trained to detect software defects using the software defect dataset from Promise data repository. The collected dataset is preprocessed to reduce the amount of redundant features using a dimension reduction …
Published in Journal of Open Source Developments · Vol. 7, Issue 3, 2020 · pp. 1–6 Read article
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To Diagnose the Broken Tooth Fault, Signal Processing Techniques and Machine Learning Technique are Applied
Abstract: The objective of this research is to study the diagnosis of broken tooth fault of spur gear using vibration signals with signal processing and machine learning techniques. In this study, experiment has been performed and analyse on the broken tooth fault and healthy spur gear conditions. This paper describes two approaches to signal processing techniques from acquired vibration signals, which are time-domain and frequency domain, involving statistical characteristics of vibration …
Published in Trends in Mechanical Engineering & Technology · Vol. 13, Issue 3, 2023 · pp. 45–53 Read article
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A Review Paper on Comparison of Algorithms for Supervised Machine Learning
Abstract: AbstractSupervised learning attempts to create calculations that help deliver general speculations, which is able to do forecasts about future occasions. The objective of supervised learning is to manufacture a model of the segregation of class names in terms of predictor components. The classifier produced accordingly is then used to allocate class marks to the testing data where the estimations of the indicator elements are known, yet the estimation of the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 4, Issue 3, 2017 · pp. 11–16 Read article
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Convolution Neural Network Model for Intrusion Detection in Network
Abstract: The evolution of the internet has made protecting information a necessity. Network intrusion and prevention plays an integral role in network-based security. The Intrusion technologies primarily used in today’s world deploy various machine learning algorithms and train models based on them resulting in effectively low detection rates. A technical advancement from machine learning, Deep Learning employs complex mechanisms to extract features from samples. As observed that conventional intrusion detection systems …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 8, Issue 1, 2021 · pp. 7–13 Read article
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Sentiment Analysis Using Emojis
Abstract: Sentiment analysis is a fast-growing research part in NLP (Natural Language Processing). It is fully focused on categorizing customer’s opinion about a particular product, blogs or comments etc. Public opinion has a significant impact on people's desire to contact with businesses, as well as overall brand perception. According to a Podium research, 93% of buyers believe online reviews affect their shopping decisions. Users may not give you another chance after …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 1, 2022 · pp. 43–46 Read article
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Signal Feature Extraction and Machine Learning Techniques for Human Activity Recognition
Abstract: Human Activity Recognition (HAR) has emerged as a critical field of study with diverse applications in healthcare, fitness tracking, smart homes, and human-computer interaction. The aim of this research is to create an efficient HAR system through advanced techniques characterized by signal feature extraction and machine learning algorithms. The MEMS sensors are used appropriately during data mining to extract time-domain, frequency-domain, and statistical features, which are subsequently passed to the …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 1, 2025 · pp. 24–41 Read article
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Machine Learning Approaches for Phishing Detection: A Comparative Study
Abstract: These days, everyone has an internet addiction. All of us have used the internet for banking, booking, recharging, and buying. Phishing is a type of website threat that exists online. On the original website, phishing is an attempt to illegally obtain information such as login ID, password and credit card information. In this research, we proposed an efficient phishing detection system based on machine learning. Overall, the experimental findings demonstrated …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 1, 2024 · pp. 35–45 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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Smart Agriculture in India: Advancements in Image Processing for Automated Plant Disease Detection and Crop Analysis
Abstract: The adoption of image processing technologies in agriculture is emerging as a revolutionary method for tackling persistent challenges in the farming industry. These techniques are increasingly used for different tasks such as detecting plant diseases, assessing crop health, and predicting yields, especially in the framework of smart agriculture systems. This study paints a detailed picture of the latest progress in image processing techniques applied to automated disease detection and detailed …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 13–19 Read article
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Online Handwritten Kannada Character Recognition and Translation to English
Abstract: Abstract—The objective of the paper is to develop a technique that can efficiently recognize hand-written characters of Kannada language and then translate the same to English language. Online recognition refers to the processing of the characters as and when it is written on digitizer and recognizes the class which it belongs to. The iball 5540U Pen Tablet is used to collect the handwritten character samples and to build the database. …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 6, Issue 2, 2019 · pp. 37–53 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