Sana'a University Journal of Applied Sciences and Technology

العدد الحالي

مجلة جامعة صنعاء للعلوم التطبيقية والتكنولوجيا

مجلد 1 عدد 2 (2023)

2023-04-30

8 مقال

المجلد الأول - العدد الثاني في مجلة جامعة صنعاء للعلوم التطبيقية والتكنولوجيا

المقالات

8 مقالات

Investigating the Impact of Utilizing the K-Nearest Neighbor and Levenshtein Distance Algorithms for Arabic Sentiment Analysis on Mobile Applications

Ahmed A. Al-Shalabi، Salah Alhagree، Ghaleb Al-Gaphari، Fahd Alqasemi (المؤلف)

2023-04-30

PDF بريد إلكتروني

DOI: 10.59628/jast.v1i2.164

الملخص

The field of sentiment analysis of Arabic scripts remains a major challenge due to the features, characteristics, and complexity of the Arabic language. Few studies on Arabic Sentiment Analysis (ASA) have been conducted to the best of our knowledge when compared to English or other Latin languages. In addition, for ASA, very few studies have been conducted regarding the comments of the users (customers) of the apps available on the Google Play Store within various mobile application reviews. Most of the current studies have been conducted on datasets collected from Twitter user comments. In this paper, we propose a new approach to the analysis of sentiment in Arabic script based on the comments dataset of users of some mobile applications available on the Google Play Store. The proposed approach involves improving algorithms such as the Levenshtein distance (LD) algorithm for data preprocessing and then combining it with the K-Nearest Neighbor (K-NN) algorithm. Through the proposed approach, the results of the experiment were shown, investigating the impact of utilizing the K-NN and LD algorithms for ASA on mobile applications effectively. The experiments were carried out. The K-NN with LD algorithm has gained a better level of evaluation compared to the K-NN algorithm. K-NN with LD algorithm has achieved the highest accuracy, recall, precision, and F-score, which were 83.11% in accuracy, 66.30% in recall, 85.10% in precision, and 74.53% in f-score evaluation measure when =3.

الكلمات المفتاحية

Arabic Sentiment Analysis K-Nearest Neighbor Levenshtein Distance Google Play

The Effect of Tartaric Acid Concentration on the Structural, Morphological and Optical Properties of CuO-Fe2O3-MgO Nanocomposite

Hisham Alnahari، A.H. Al-Hammadi، Annas Al-Sharabi، Adnan Alnehia (المؤلف)

2023-04-30

PDF بريد إلكتروني

DOI: 10.59628/jast.v1i2.189

Microtextures on Quartz Grain Surfaces in the Beach Sediments of Southern Hodeidah, Red Sea Coast, Yemen

Ahmed Ali Al-Aydrus، Saeed Omar Wasel، Mohammed Abdullah Al-Wosabi (المؤلف)

2023-04-30

PDF بريد إلكتروني

DOI: 10.59628/jast.v1i2.205

A Hybrid Model for Using Cloud Computing and Blockchain Technologies to Protect Hospital Records

Ebtisam Ali Abdullah Alselwi، Abdulmajed Ahmed Al-Khulaidi، Anwar Al-Shamiri (المؤلف)

2023-04-30

PDF بريد إلكتروني

DOI: 10.59628/jast.v1i2.220

Fabrication and Study of the Effect of Mn-Substituted Ba-Zn Nanoferrites on the Enrichment of Structural Properties

Azmi A. M. Othman، A.H. Al-Hammadi، Sadiq. H. Khoreem (المؤلف)

2023-04-30

PDF بريد إلكتروني

DOI: 10.59628/jast.v1i2.281

الملخص

In this study, we investigated the effect of Mn doping on the structural properties of barium-zinc ferrites (BZF-NPs) synthesized using the traditional ceramic method. X-ray diffraction (XRD) was used to analyze the structural characteristics of the BZF-NPs, including the structural phases, crystallite size, and lattice parameters. In addition, various physical properties, such as bulk density, X-ray density, porosity, and specific surface area, were investigated. The XRD analysis of the samples revealed that the lattice parameters of the hexagonal M-type structure of the BZF-NPs decreased as the concentration of Mn+2 ions increased. In addition, the average crystallite size of the samples decreased from 52 to 42 nm with increasing Mn+2 concentration. The c/a parameter ratio indicates the formation of a magneto-plumbite structure. Moreover, we found that both the X-ray and bulk densities increased with increasing Mn+2 concentration. This behavior can be attributed to the difference in ionic radii between the donor Mn+2 ions and the host Fe+3 and Zn+2 ions. However, it was observed that the porosity increased with increasing Mn+2 concentration, in contrast to the bulk density behavior. Furthermore, it was observed that as the crystallite size increased, the surface area decreased. This trend can be attributed to the fact that larger crystallites have fewer surface atoms. Finally, it was noted that the bond lengths for both tetrahedral and octahedral structures decreased with increasing Mn2+ ion concentration. Overall, these results provide insights into the impact of Mn+2 ion doping on the structural and physical properties of BZF-NPs and highlight the potential applications of these materials in various fields.

الكلمات المفتاحية

Mn-substituted Ba-Zn ferrites lattice parameters structural properties Specific surface area Porosity

Mechanical Properties of the (81Sn–xCu–(19-x) In) % Alloys with x=1, 2, 3, 4, and 5

A.H. Al-Hammadi، Ibrahim G. H Loqman ، R.M.H. AL-Hdhrami (المؤلف)

2023-04-30

PDF بريد إلكتروني

DOI: 10.59628/jast.v1i2.339

الملخص

Research on Material Science has a long tradition, and lead has been used widely in many applications. Because of their low melting temperatures, low cost, and good wettability, tin -lead alloys have been preferred as solder alloys in industrial production, particularly in the electronics sector. However, due to the negative effects of lead on the environment and human health, studies on lead-free solder alloys have been increasing, and the subject is still important today. Furthermore, environmental protection agencies (RoHS and WEEE) have prohibited the use of lead-containing solder alloys in the manufacture of electronic and microelectronic products. In this work, the effect of adding Cu to alloys in five compositions of (81Sn-xCu-(19-x)In) % with x=1, 2, 3, 4, and 5 on the mechanical properties has been studied. The five samples were prepared from high purity (99%) elements (Sn –Cu –In ) using the melting technique. The creep of the ternary alloys was examined at different temperatures (25, 50, and 80 ) under three different loads (2.5, 6.3, and 11.4 MPa). It was found that the creep rate of the ternary alloys was increasing with the increases in Cu –content in all samples at different temperatures and loads. The mean values of the stress exponent were found to be in the range of (0.1-2.5) as well as the activation energies vaied in the range of (0.48-5.33) eV for all alloys under different loads (2.5, 6.3, and 11.4 MPa).

الكلمات المفتاحية

Sn- Cu -In alloys Particle size Mechanical properties Cu addition

Web Caching Hybrid Algorithm by Semantic Similarity

Abdualmajed Ahmed G. Al-Khulaidi، Mohammed Abdulbaqi Al-Sayani (المؤلف)

2023-04-30

PDF بريد إلكتروني

DOI: 10.59628/jast.v1i2.328

الملخص

Recently, most companies have been conducting their business through the cloud network, so increasing the speed of data access via the Internet has become very important. Therefore, the importance of cache algorithms lies in increasing the speed of data access. As we know, there are cache substitution algorithms that work well with the cache when applied to the processor cache, but they hardly work when applied to caching for web purposes. The reason for this discrepancy is that it is not intended to increase the complexity of this type of storage. Considering the challenges of cache usage in terms of the large discrepancy in file size and the heterogeneous model of user access to data in a web environment, especially with pages with dynamic content, there is a real need to develop 'web cache' algorithms. In this paper, a hybrid algorithm for web caching using semantic similarity is developed. The GDFS algorithm was developed using NGD (Normalized Google Distance) to determine the semantic similarity between cache objects, which resulted in better performance in comparison to other algorithms. The results showed that the web cache hybrid algorithm using semantic similarity increased the hit rate compared to the basic algorithms by up to 80.10%. The proposed hybrid algorithm was able to overcome the problem of the low byte hit rate in the GDFS algorithm. The improvement in the byte hit rate reached 65%. This indicates an increase in the byte hit rate. The results showed that the web cache hybrid algorithm using semantic similarity reduced the page load time using distance measured from Google compared to the page load time using other algorithms that do not use semantic similarity and do not use cache. The results showed that the web cache hybrid algorithm using semantic similarity reduced the page load time from 4.17 seconds using GDFS-Line to 2.16 seconds using GDFS-NGD.

الكلمات المفتاحية

NGD GDFS Hybrid Cache

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Ayman Alsabry، Malek Algabri، Amin Mohamed Ahsan (المؤلف)

2023-04-30

PDF بريد إلكتروني

DOI: 10.59628/jast.v1i2.361

الملخص

Breast cancer (BC) is a major health concern worldwide. It is a complex and multifactorial disease, and identifying its risk factors is crucial for early detection and effective treatment. This review article provides an overview of the literature on breast cancer risk factors, data sources, and machine learning algorithms for prediction. The paper discusses the various risk factors associated with breast cancer, including age, family history, lifestyle choices, and environmental factors. Additionally, the article explores the different data sources used in breast cancer research, including clinical data, genomic data, and lifestyle data. The paper then reviews the different machine-learning algorithms used for breast cancer prediction, including supervised and unsupervised learning. The performance of ML algorithms in predicting BC risk using different data sources was assessed. This review provides valuable insights into the current state of research on BC risk factors and prediction using ML algorithms and data sources, and the findings will be useful for healthcare professionals and researchers working in the field of BC.

الكلمات المفتاحية

Breast Cancer Risk Factors Breast Cancer Data Source Machine learning (ML) Support Vector Machines (SVM) Atificial Neural Networks (ANN) Decision Trees (DT) Random Forests (RF) Clustering Dimensionality Reduction