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41
Image-Based Air Quality Estimation Using Convolutional Neural Network Optimized by Genetic Algorithms: A Multi-Dataset Approach
Published 2025“…This paper proposes a new approach using convolutional neural networks with genetic algorithms for estimating air quality directly from images. …”
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42
Improvement Algorithm for Limited Space Scheduling
Published 2001“…The model characterizes resource space requirements over time and establishes a time-space relationship for each activity in the schedule, based on alternative resource levels. An example illustrates the presented algorithm that generates a feasible space schedule.…”
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43
Predicting Plasma Vitamin C Using Machine Learning
Published 2022“…<p dir="ltr">Precision Nutrition makes use of personal information about individuals to produce nutritional recommendations that have more utility than general population level recommendations. …”
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44
Decision-level Gait Fusion for Human Identification at a Distance
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45
Empirical comparison of regression test selection algorithms
Published 2001“…These criteria are: number of selected test cases, execution time, precision, inclusiveness, preprocessing requirements, type of maintenance, level of testing, and type of approach. The empirical results show that the five algorithms can be used for different requirements of regression testing. …”
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46
A genetic algorithm for testable data path synthesis
Published 2017“…The approach is formulated as an allocation problem and solved using an efficient genetic algorithm that generates cost-effective testable designs. …”
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47
A neural networks algorithm for data path synthesis
Published 2003“…This paper presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. …”
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48
Formal synthesis of VLSI layouts from algorithmic specifications
Published 2020“…In this paper we present a formal approach for high level synthesis. This formal high level syntesis system uses recursive algorithms to model the behaviour to be synthesized. …”
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49
A comparative study of five regression testing algorithms
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50
Spectrum Sensing Algorithms for Cooperative Cognitive Radio Networks
Published 2010Get full text
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51
Adaptive Chip-Level Channel Estimation for IMT-DS System: DL and UL
Published 2005“…In this paper, chip-level adaptive channel estimation has been explored by using LMS algorithm for wideband CDMA channel estimation. …”
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52
A simplified sliding‐mode control method for multi‐level transformerless DVR
Published 2022“…<p dir="ltr">Here, a finite-control-set sliding-mode control (FCS-SMC) method is proposed for single-phase three-level T-type inverter-based transformerless dynamic voltage restorers (TDVRs). …”
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53
Digital-Twin-Based Diagnosis and Tolerant Control of T-Type Three-Level Rectifiers
Published 2023“…<p dir="ltr">This article proposes a digital twin (DT)-based diagnosis and fault-tolerant control for T-type three-level rectifiers. To develop the DT, a dense deep neural network (DNN) machine learning approach is used. …”
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54
Exploring Semi-Supervised Learning Algorithms for Camera Trap Images
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55
Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
Published 2016Get full text
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56
Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach
Published 2006“…The algorithm is tested using 200 randomly generated 2-variable 4-valued functions. …”
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57
Decision-level fusion for single-view gait recognition with various carrying and clothing conditions
Published 2017“…Gait samples are fed into the MPCA and MPCALDA algorithms using a novel tensor-based form of the gait images. …”
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58
A Framework for Predictive Modeling in Sustainable Projects
Published 2012Get full text
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59
Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction
Published 2025“…The algorithm results show that the internal validation performance of RF, XGBoost, and MLP is strong, with AUC scores of 0.90, 0.90, and 0.87, respectively. …”
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60
The role of Reinforcement Learning in software testing
Published 2023“…</p><h3>Results</h3><p dir="ltr">This study highlights different software testing types to which RL has been applied, commonly used RL algorithms and architecture for learning, challenges faced, advantages and disadvantages of using RL, and the performance comparison of RL-based models against other techniques.…”