Search Results - (( java implementation learning algorithm ) OR ( automatic identification learning algorithm ))
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The efficacy of deep learning algorithm in classifying chilli plant growth stages
Published 2021“…In this paper, the efficiency of deep learning algorithms in classifying the growth stage of chili plants is studied. …”
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Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm
Published 2008“…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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Deep learning approaches, platforms, datasets for behavior based recognition: a survey
Published 2025“…We attempt to classify current research work to report the ongoing trends in human behavior recognition using deep learning algorithms. This paper reviews various aspects, like the ones associated with machine learning and deep learning models, human activity recognition (HAR), deep learning frameworks/tools, abnormal behavior datasets, and a variety of other current trends in the field of automatic learning. …”
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Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
Published 2016“…First proposed algorithm is the combination of momentum algorithm with adaptive learning rate (ALR) algorithm, and second proposed algorithm is the combination of momentum algorithm with automatic learning rate selection (ALRS) algorithm. …”
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Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification
Published 2020“…In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. …”
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Deep learning approaches, platforms, datasets for behavior based recognition: a survey
Published 2025“…We attempt to classify current research work to report the ongoing trends in human behavior recognition using deep learning algorithms. This paper reviews various aspects, like the ones associated with machine learning and deep learning models, human activity recognition (HAR), deep learning frameworks/tools, abnormal behavior datasets, and a variety of other current trends in the field of automatic learning. …”
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Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data
Published 2019“…The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. …”
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An Educational Tool Aimed at Learning Metaheuristics
Published 2020“…Implemented with Java, this tool provides a friendly GUI for setting the parameters and display the result from where the learner can see how the selected algorithm converges for a particular problem solution. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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Multi-sensor fusion and deep learning framework for automatic human activity detection and health monitoring using motion sensor data / Henry Friday Nweke
Published 2019“…Third, to propose orientation invariant based deep spare autoencoder methods for automatic complex activity identification to minimize orientation inconsistencies and learn adequate data patterns. …”
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Dyslexia handwriting detection using Convolutional Neural Network (CNN) algorithm / Sofea Najihah Mohd Zaki
Published 2024“…Convolutional Neural Network (CNN) algorithm was chosen as one of the possible solutions after a thorough analysis of many algorithms for dyslexic handwriting identification. …”
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Age detection from face using Convolutional Neural Network (CNN) / Hanin Hanisah Usok @ Yusoff
Published 2024“…In this project, we want to create an age identification system that uses Convolutional Neural Network (CNN) algorithms to estimate people's ages fast and accurately from facial images. …”
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A Genetic-CBR Approach for Cross-Document Relationship Identification
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A review of handcrafted computer vision and deep learning approaches for food recognition
Published 2020“…Hence, there is demand for novel tools able to provide an automatic, personalised, and accurate dietary assessment through food recognition algorithms. …”
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Advancing machine learning for identifying cardiovascular disease via granular computing
Published 2024“…Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for CVD identification. …”
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Speaker identification through feature fusion based deep learning / Rashid Jahangir
Published 2021“…To address this problem, this study proposes a novel fusion of MFCC and time-based features (MFCCT), which combines the effectiveness of MFCC and time-domain features to improve the accuracy of text-independent speaker identification (SI) systems. On the other hand, recent advancements in deep learning have gained the attention of researchers working in the field of automatic speaker identification (SI). …”
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An annotated image dataset of pests on different coloured sticky traps acquired with different imaging devices
Published 2024“…When investigating the automatic identification and counting of pests on sticky traps using computer vision and machine learning, two aspects can strongly influence the performance of the model – the colour of the sticky trap and the device used to capture the images of the pests on the sticky trap. …”
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