106 hasil untuk machine learning
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Multi-stage Alzheimer's disease diagnosis using Diffusion Tensor Imaging and Cost-Sensitive Machine Learning
…healthy controls (HC). The proposed method, which incorporates various machine learning models, Bayesian hyperparameter optimization, 10-fold cross-validation, and cost-sensitive learning, achieved a high test accuracy of 90.4%. Feature ranking consistently identified Axial Diffusivity in the left…
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Comparative analysis of the main machine learning algorithms for the automatic recognition of fake news
…speed of spreading fake news. Whatever the goal, artificial intelligence, machine learning, deep fakes, and voice biometrics are powerful tools to develop fake news, and threat actors use them often. Therefore, machine learning has become a countermeasure, an instrument to combat the fake news…
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Blockchain and Machine Learning in Talent Acquisition: A Review on Credential Verification and Recruitment Optimization
…This review critically examines the integration of Blockchain and Machine Learning (ML) as transformative tools in addressing long-standing challenges such as credential fraud, skills mismatch, unconscious bias, and lack of process transparency. Blockchain provides a decentralized, immutable ledger…
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Implementation of a novel technique for DNA sequence analysis using Machine Learning, Deep Learning and hybrid frameworks
…and interpreting DNA strands. In recent years, algorithms based on machine learning have been effectively employed to identify and classify viruses, which is crucial for preventing outbreaks such as COVID-19. Feature extraction aids in identifying the impact of viruses and designing drugs. The…
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Determinants of Uneven Progress in Sustainable Development in the EU: Predictive Approaches Based on Machine Learning
…Union member states. Using a comprehensive panel dataset and advanced machine learning techniques, the study demonstrates that distributional indicators such as income inequality and poverty thresholds have stronger and more consistent predictive power than traditional macroeconomic measures…
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The Informal Economy as a Risk and an Opportunity for Sustainable Development: A Machine Learning-Based Approach
…indicators into SDG predictive models. To achieve this, we employed machine learning methods – such as Random Forest and XGBoost – and feature selection techniques (Chi-squared and Mutual Information), thereby introducing a novel element to studies on the informal economy. The results highlighted…
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The LETBP feature descriptor based fish species classification using Kepler optimization with Extreme Learning Machine
…directions. The extracted unique feature values are given to the Extreme Learning Machine (ELM) for the final classification. The ELM network randomly selects the bias and weights and in order to overcome this issue an optimization technique called Kepler Optimization Algorithm (KOA) is adopted…
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The analysis of students opinion about the teachers skills using supervised learning
…The module uses natural language processing techniques, and supervised machine learning for selecting the answers to be analyzed in a certain context.
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Credit Card Fraud Detection using Deep Learning Techniques
The objective of this paper is to identify credit card fraud and this topic can be solved with the help of advanced machine learning and deep learning techniques. Due to the fact that credit card fraud is a serious worldwide problem, we have chosen to create a model for detecting im-poster scams by…
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Deep Learning pentru descrierea automată a imaginilor în limbaj natural - Image Captioning
…spectacular developments in the field of deep neural networks and Deep Learning in recent years have led to absolutely remarkable progress also in the field of IC, the quality of the generated descriptive texts being substantially improved. Convolutional Neural Networks (CNN) have been naturally…
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Micrographia-based parkinson’s disease detection using Deep Learning
…(Micrographia), can be used for PD diagnosis in patients. Deep Learning (DL) approaches, a subfield of machine learning research represent a useful tool for unsupervised feature learning because they employ a succession of layers, each of which is responsible for extracting different sorts of data…
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Students Engagement Detection in Online Learning During Covid-19 Pandemic Using R Programming Language
…researchers tried to develop a prototype using R programming language and machine learning tools that can detect and recognize students’ face easily that might help teachers to take attendance without any hassle. Researchers took help of Artificial Intelligence as well as used Machine Learning…
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NEW TECHNOLOGIES AND FOREIGN LANGUAGE LEARNING: TRANSFORMING LANGUAGE EDUCATION
This article explores the transformative impact of new technologies on foreign language learning and its implications for language education. With the rise of globalization, proficiency in multiple languages has become increasingly valuable. The advent of online language learning platforms, virtual…
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Deep Reinforcement Learning. Studiu de caz: Deep Q-Network
…optimal behaviors in different contexts, perfectible in time. Among the machine learning methods of AI, reinforcement learning (RL) by repetitive interactions with the environment while targeting a purpose plays a particularly important role, besides supervised and unsupervised learning. However…
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Predicting Alzheimer’s Disease Using Deep Learning Artificial Intelligence Together with a Pre-Trained VGG19 and Inception_v3 Models
This paper presents two experiments in which, using artificial intelligence (specifically Deep Learning with convolutional neural networks), we were able to predict Alzheimer's disease based on MRI images. In order to have better results and to minimize the computational effort in the laboratory…
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Architecture of a Broadcast and Media Production Learning Center Integrating Cloud-Based Ingest and Automated News Classification
This paper presents a reference architecture for a learning center designed to support engineering education in television broadcast and media production. The proposed environment replicates a small-scale but operationally complete television broadcast facility and is intended to give students…
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COGNITIVE SYSTEMS. REDEFINING THE COOPERATION BETWEEN MAN AND SYSTEM
Cognitive systems appeared as a response to the real challenges brought by the Big Data phenomenon. It was found that the solutions for solving difficult problems caused by this phenomenon could be brought by using artificial intelligence tools. In this context a convergence between Big Data and…
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Digital transformation of marketing
The right key to an effective marketing activity is not the technique used, it is not the large amount of data but the immediate control of the useful information, knowing the preferences of the customers, the actions of the competition, which are constantly changing. In the information age, this…
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ARTIFICIAL INTELLIGENCE IN GASTROINTESTINAL ENDOSCOPY: ADVANCES, CHALLENGES, AND CLINICAL APPLICATIONS
…PubMed and EMBASE using the following terms: “artificial intelligence,” “machine learning,” and “gastrointestinal endoscopy.” We included systematic reviews, meta-analyses, clinical trials, consensus statements, and narrative reviews. Studies were selected and reviewed according to PRISMA…
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ARTIFICIAL INTELLIGENCE IN SURGERY: CURRENT TRENDS AND FUTURE
…Spinal surgery benefits from AI-driven planning and navigation: deep learning models outperform traditional methods in preoperative deformity prediction and segmentation, while robotics and computer vision improve instrument’s placement. In gastrointestinal surgery, AI systems enhance…