Search Results - (( using sparse based algorithm ) OR ( evolution optimization bat algorithm ))
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Multi-Swarm bat algorithm
Published 2023“…In this study a new Bat Algorithm (BA) based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA) is proposed to address the problem of premature convergence phenomenon. …”
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An improved public key cryptography based on the elliptic curve
Published 2002“…To speed up the underlying finite field operations, several new algorithms are introduced namely: selecting random sparse elements algorithm, finding sparse base points, sparse multiplication over polynomial basis, and sparse multiplication over normal basis. …”
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An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding
Published 2020“…Moreover, sparse coding has been commonly used in recent years for the purposes of retrieving and identifying images. …”
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Evaluation of sparsifying algorithms for speech signals
Published 2012“…Sparse representations of signals have been used in many areas of signal and image processing. …”
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Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff
Published 2017“…Basically, the QEEA is based on the Time Domain (TD) and Frequency Domain (FD) scheduling where it is dependent on the QoS requirements to allocate resources. The proposed algorithm is compared against other scheduling algorithms, namely, the Channel and QoS Aware (CQA), Priority Set Scheduler (PSS), Proportional Fair (PF), Maximum Throughput (MT) and Blind Average Throughput (BAT). …”
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Compressed Sensing Implementations For Sparse Channel Estimation In OFDM Systems
Published 2018“…Hence, a new fusion framework namely, Collaborative Framework of Algorithms (CoFA) is proposed, to pursue accurate recovery of the sparse signals from few linear measurements. …”
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Rain streaks removal using total variation and sparse coding based on case based reasoning approach
Published 2024journal::journal article -
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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
Published 2022“…Then, the selected features are given as input to the DBN classifier which is trained using the Taylor-based bird swarm algorithm (Taylor-BSA). …”
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Blind Source Separation Using Two-Dimensional Nonnegative Matrix Factorization In Biomedical Field
Published 2018“…Theoretically,β and α is parameters that used to vary the NMF2D algorithm in order to yield high SDR value. …”
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Visual Simultaneous Localization and Mapping Using Direct-Based Method for Unmanned Aerial Vehicle (UAV)
Published 2023“…The Direct Sparse Odometry (DSO) technique is a new form of visual odometry that makes use of a direct and sparse structure to achieve precision. …”
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Prime-based method for interactive mining of frequent patterns
Published 2010“…Since rerunning the mining algorithms from scratch can be very time consuming, researchers have introduced interactive mining to find proper patterns by using the current mining model with various minsup. …”
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Partitional clustering algorithms for highly similar and sparseness y-short tandem repeat data / Ali Seman
Published 2013“…In some cases, they are quite distant and sparseness. This uniqueness of Y-STR data has become problematic in partitioning the data using the existing partitional clustering algorithms. …”
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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. This study is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. …”
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Speech compression using compressive sensing on a multicore system
Published 2011“…The performance of overall algorithms will be evaluated based on the processing time and speech quality. …”
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Hardware development of autonomous mobile robot based on actuating lidar
Published 2022“…From here, the extracted values are implied on k-NN, Decision Tree and CNN training algorithm. The final result shows promising potential with 91% prediction when implemented on the Decision Tree algorithm based on our proposed system of a single actuating LiDAR sensor.…”
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