Liver Lesion Segmentation Using Deep Learning Models

Rehman, Aasia O.; Butt, Muheet Ahmed; Zaman, Majid

doi:10.56578/ataiml010108

Cited by 14 publications

(15 citation statements)

References 20 publications

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“…Thereby, feature clustering algorithms can be used effectively for feature-matching techniques. Many clustering algorithms have been used recently for speaker recognition, including Vector Quantization (VQ), K-means Clustering, Support Vector Machine (SVM) [29], Neural Network (NN) [30,31], Hidden Markov Modeling (HMM), and Dynamic Time Warping (DTW) [32]. In this paper, the VQ and K-means tactics have been used to identify the clusters.…”

Section: Mfcc Features Clusteringmentioning

confidence: 99%

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Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

Al‐Jarrah¹,

Ahmad²,

Jarrah³

et al. 2022

RIA

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The Speaker identification process is not a new trend; however, for the Arabic Holy Quran recitation, there are still quite improvements that can make this process more accurate and reliable. This paper collected the input data from 14 native Arabic reciters, consisting of “Surah Al-Kawthar” speech signals from the Holy Quran. Moreover, this paper discusses the accuracy rates for 8 and 16 features. Indeed, a modified Vector Quantization (VQ) technique will be presented, in addition to realistically matching the centroids of the various codebooks and measuring systems’ effectiveness. Note that the VQ technique will be utilized to generate the codebooks by clustering these features into a finite number of centroids. The proposed system’s software was built and executed using MATLAB®. The proposed system’s total accuracy rate was 97.92% and 98.51% for 8 and 16 centroids codebooks, respectively. However, this study discussed two validation tactics to ensure that the outcomes are reliable and can be reproduced. Hence, the K-mean clustering algorithm has been used to validate the obtained results and discuss the outcomes of this study. Finally, it has been found that the improved VQ method gives a better result than the K-means method.

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Section: Mfcc Features Clusteringmentioning

confidence: 99%

“…Clustering technique performance could be measured using clustering error criteria, the distance of every data point in a cluster from its centroid, which should be as lowest as possible [31]. Several algorithms were used to compute the clustering error criterion.…”

Section: Figure 4 Simple Vq Diagrammentioning

confidence: 99%

Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

Al‐Jarrah¹,

Ahmad²,

Jarrah³

et al. 2022

RIA

View full text Add to dashboard Cite

show abstract

“…The proposed system provides a good balance between the sum rate performance and hardware complexity of the system using a partially connected sub-array structure. Recently deep learning based algorithms are efficiently used for many signal processing applications and has given tremendous improvement in the results [31,32]. In future the performance of hybrid beamforming can be enhanced using deep learning techniques.…”

Section: Discussionmentioning

confidence: 99%

An Effective Kalman Based Hybrid Beamforming for Millimeter Wave Massive MIMO System by Using 2D Overlapped Partially Connected Sub-Array Structure

Londhe¹,

Hendre²

2022

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In 5G mobile communication networks, millimeter-wave (mmWave) technology plays a key role. To deal with the problem of path loss that happens in the mmWave band, competent beamforming employing a large antenna array is required. Fully digital beamforming approaches currently require separate radio frequency chains (RF chains) for each antenna, which adds to the computational complexity and hardware expense. In a multi-user environment, fully digital beamforming suffers from a larger antenna array layout, whereas analog beamforming solutions are prone to numerous performance concerns. Hybrid beamforming, on the other hand, offers a promising solution for multi-user mmWave communication. This work proposes a two-dimensional overlapping partially connected (2D-OPC) sub-array structure to reduce the antenna structure's hardware complexity and cost. The suggested technique comprises several sub-arrays that are overlapped in 2D space in the form of a Uniform Planar Array (UPA). The suggested scheme's performance is assessed using Kalman-based hybrid beamforming, which exhibits a considerable increase in spectral efficiency when compared to existing hybrid beamforming techniques. The suggested technique, which uses Kalman based hybrid beamforming and 2D-OPC provides 3.14% and 4.96% improvement over the MMSE hybrid beamforming and ZF pre-coding technique respectively.

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“…Image segmentation is the initial process of digital processing [28]. The digital image obtained is then preprocessed using the aforementioned procedure.…”

Section: Methodsmentioning

confidence: 99%

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Setiawan¹,

Hadiyanto²,

Widodo³

2022

RIA

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Shrimp is a marine culture found globally due to the ability of its yields to boost a country's economy. It is imperative to monitor its size to determine the condition of the shrimp underwater with complex noise using a non-invasive method. Therefore, this study aims to develop a new method for measuring the body weight of shrimp using morphometric features based on underwater image analysis and a machine learning approach. The method used consists of several steps, data collection using an underwater camera, image analysis using image grayscale, image binary, edge detection, region of interest detection, shrimp image morphometric features extraction, camera calibration using Triangle Similarity (TS), and Correction Factor (CF), calculation of morphometric features value, create machine learning model, training data, and testing data for estimation of underwater shrimp body weight. After testing the model, get the best accuracy value is RMSE = 0.05, MAE = 0.04, and R2 = 0.96 from the MLR method. In conclusion, the results showed that the hybrid method TS-CF-MLR is the best method for measuring underwater shrimp body weight estimation with the lowest error rate and highest coefficient of determination.

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Liver Lesion Segmentation Using Deep Learning Models

Cited by 14 publications

References 20 publications

Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

An Effective Kalman Based Hybrid Beamforming for Millimeter Wave Massive MIMO System by Using 2D Overlapped Partially Connected Sub-Array Structure

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

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