UHD Journal of Science and Technology

An Image Analysis for Designing an Optimal Stirrer in Metal Matrix Composites Manufacturing

2024-10-25T00:08:33+00:00

The global market for aluminum-based composites, widely used in manufacturing and construction, is expected to grow significantly. However, enhancing the cost-to-performance ratio is essential to improving their commercial viability. Efficient mixing plays a critical role in many industrial and chemical applications. Stir casting is the leading method for producing aluminum alloy matrix composites, but achieving a uniform particle distribution remains a significant challenge. In this study, the optimal stirrer design was identified using image processing techniques to analyze the distribution of ceramic grains. The stirrer that achieved the most uniform grain distribution was selected, eliminating the need for destructive testing. The mechanical properties of the final products validated the accuracy of the image analysis results.

Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features

2024-10-25T00:11:58+00:00

Nephrolithiasis is a scientific term that refers to kidney stones and means the formation of crystal concretions in the kidney. It is considered a widespread situation that affects millions of people worldwide. Those stones can cause serious discomfort to infected people, especially when they traverse the urinary system, although, the big stones may need a surgical intervention. Various systems are already in use to address kidney stones, including ultrasound imaging for detection, extracorporeal shock wave lithotripsy (ESWL) for non-invasive stone fragmentation, and ureteroscopy for surgical removal, showcasing the advances in medical technology for managing this condition. This study presents an approach for detecting stones in the affected kidney. A public dataset has been employed in this work, containing (2370) images of healthy and affected kidneys. The dataset was utilized to train the proposed approach for the aim of stone detection. To achieve high detection accuracy, we implemented two key phases before classification. The preprocessing phase enhances image quality by reducing noise using a median filter and improving contrast through contrast stretching and tone enhancement. The segmentation phase follows, accurately identifying the kidney’s edges and regions of interest for effective feature extraction. The Local Binary Pattern (LBP) technique, combined with the support vector machine (SVM) algorithm serves as the primary components of the proposed model. The feature extraction comes into action through the LBP technique as a preparation step for the SVM classifier to complete the stone detection process. The approach introduced in this paper has the potential to enhance detection accuracy and efficiency. Furthermore, it could be used as an early detection tool to identify potential cases, thereby helping to prevent complications and adverse outcomes. This method aims to improve on the traditional manual process employed by radiologists, which could be described as time and effort consumption rather than the exposure of the interpretations. The obtained results were compared with the most relevant approaches in the field of kidney stone detection, demonstrating the model’s effectiveness in achieving the desired goal with a diagnostic accuracy of 96.37% for kidney stones.

Exploring Post-Quantum Cryptography: Evaluating Algorithm Resilience against Global Quantum Threats

2024-11-30T18:39:12+00:00

Cryptographic algorithms perform a vital part in protecting information in general and safeguarding digital platforms. Nevertheless, improvements in quantum computing pose important concerns to traditional cryptographic approaches, demanding the development of quantum-resistant explanations. This study offers an inclusive investigation of post-quantum cryptographic algorithms, assessing their flexibility, competence, and practicality in justifying quantum risks. Through an equivalent approach, the research identifies optimistic applicants for upcoming cryptographic standards. Moreover, the study highlights the international essential for embracing these algorithms to ensure secure communication and data protection in the quantum era. These conclusions aim to notify the progress of strong cryptographic systems that address the appearing objections of quantum technologies.

The Clinical Neurological Manifestations of Patients Diagnosed with Carpal Tunnel Syndrome

2024-08-25T14:05:19+00:00

Background: Carpal tunnel syndrome (CTS) is a condition, in which the median nerve becomes pressed or squeezed at the wrist. This causes pain and numbness in the fingers. Therefore, a neurological study is crucial to assess the condition.Objectives: The objective of this study was to assess the neurological manifestations of CTS and their association with demographic and clinical features from October 2022 to March 2023. Materials and Methods: A quantitative study was carried out over the period of 5 months by prospectively selecting and enrolling 100 CTS patients with a confirmed diagnosis. The CTS assessment questionnaire was modified and patients consented to the study before the data collection. Results: Adults aged 35–44 were the dominant group and the disease was found in females 10 times more than males. The least assigned symptoms were tingling and numbness in the little finger (4%) and neck pain 22%. All the patients with CTS presented with severe levels of CTS. Statistically significant associations were found between occupations, duration of the disease, affected side, other chronic diseases, and the prevalence of the symptoms at P ≤ 0.05. Self-management to sub-side pain and numbness had crucial impact on reducing the symptoms (P ≤ 0.05). Conclusion: The prevalence of the neurological symptoms varied depending on the sociodemographic and clinical features. Self-management had a significant positive impact on reducing some of the neurological symptoms, such as pain in the wrist at night and tingling and numbness in the morning.

An Effective Computer-aided diagnosis Technique for Alzheimer’s Disease Classification using U-net-based Deep Learning

2025-01-13T14:54:02+00:00

The diagnosis of Alzheimer’s disease (AD), a common neurodegenerative disease that impairs thinking and memory abilities in older adults and ultimately results in cognitive impairment and dementia, is made possible in large part by computer-aided diagnosis (CAD). The idea has been to use either machine learning models or deep learning models to develop classification techniques for this disease. CAD techniques and mechanisms have emerged to help and facilitate early detection of this disease as a fundamental step in its treatment plan. As part of our approach, we proposed a model that included the following two pre-processing steps: Contrast Limited Adaptive Histogram Equalization (CLAHE) was utilized to enhance image contrast, especially in low-contrast areas. Normalization was then incorporated to ensure reliable training and faster convergence. A Gray-level co-occurrence matrix technique was used to extract seven texture features from the images following pre-processing: contrast, homogeneity, energy, correlation, variance, dissimilarity, and entropy. After that, these characteristics were added to the model output before the last classification layer. The best hybrid framework out of the five models we examined in this paper was utilized to build a convolutional neural network that can be used to identify AD characteristics from magnetic resonance images. As discussed in Section IV of this article, the U-Net model was selected because of its superior performance. The experimental results demonstrate that this technique showed great accuracy in segmentation and classification for each of the five AD Neuroimaging Initiative categories when a specific diagnosis was made. These results are as follows: Overall, the five classes’ final average scores for the four measures were as follows: 94.46% for Accuracy, 94.32% for Precision, 94.49% for Recall, and 94.41% for F1-score.

A review: Multi-Objective Algorithm for Community Detection in Complex Social Networks

2025-01-12T16:04:05+00:00

Recently, research on multi-objective optimization algorithms for community detection in complex networks has grown considerably. Community detection based on multi-objective algorithms (MOAs) in complex social networks is a fundamental scheduler, and it supports knowing the dynamics of a society, finding influential groups, and improving information dissemination. The traditional methodologies often cannot cope with the features that real-world network usually present, related to optimizing various and sometimes conflicting objectives. This paper provides an overview of some recent works on MOAs for community detection in complex social networks. This paper will explore the balance of the reached objectives, such as modularity, community size, and edge density. Which are analyzed by 15 different approaches in order to choose from works published during the period 2019–2024. These strengths and limitations of various MOAs are reviewed with a comparative analysis to provide insights into both the effectiveness and computational efficiency of these methods. The present trends and future research are discussed that underline the need for the development of solutions to be more adaptive and scalable in coping with the gradually increasing complexity of social networks.