مجلة جامعة صنعاء للعلوم التطبيقية والتكنولوجيا

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.

In the present study, the sol-gel method has been utilized to synthesize tri-phase CuO-Fe₂O₃-MgOnanocomposites (NCs) by employing tartaric acid (TA) as an organic fuel. The obtained NCs were characterized using XRD, SEM, FTIR, and UV-Vis. The XRD pattern asserted the formation of NCs with MgO (cubic), CuO (monoclinic), and Fe₂O₃ (hexagonal) crystals. The crystallite size (D_av) was calculated by Scherrer's formula, whereas the optical properties were assessed using UV-Vis spectra. The optical band gap values of the synthesized materials displayed variations related to tartaric acid concentration due to the quantum confinement of tri-metal oxide NCs. The SEM described the morphological properties and FTIR confirmed the formation of interfaces among the metal oxides CuO, Fe₂O₃ and MgO in the NCs. The enhanced optical properties of these tri-metal oxide NCs make them a good candidate for optoelectronic applications.

Surface features of quartz grains on the Al-Haymah and Moushij beaches of the coastal area in the southern Red Sea of Yemen were analyzed by a scanning electron microscope (SEM) to deduce the provenance and depositional environment of sediments. The microtextures on quartz grains were classified into mechanical, chemical, and mechanical/chemical origins. The quartz grains from the two beach areas were rounded to subrounded in shape. Rounded quartz grains with a combination of bulbous and broken edges indicated a recycled provenance. Mechanical features such as V-shaped marks and straight and curved scratches revealed the combination of fluvial and high-energy subaqueous beach environments, whereas aeolian grains are mainly marked by crescentic percussion marks, elongated depressions, and upturned plates. Chemical features such as solution pits, silica globules, trapped diatoms, and adhering particles in quartz grains indicated a diagenetic environment and silica re-precipitation in a coastal environment. In general, the microtextures identified on quartz grains reveal that sediments are influenced by wind, fluvial, and subaqueous marine environments and are derived from nearby and distal sources.

Cloud computing technology is one of the hottest trends in information technology (IT) today. It is an important alternative to ensure high data processing and exchange. Thus, this technology can be used in the health sector to exchange patient medical records and health-related information. However, there are fears of the leakage of confidential patient data. This research studied two main contexts, the technological and organizational contexts, to find out the concerns about the use of cloud computing technology in the health sector. The results indicated the desire of hospitals to use cloud computing because of its enormous benefits in the health sector, with concern about data security with cloud computing. For this reason, this paper is proposed. In addition, a model  for protecting and storing the patient's medical record using two technologies: cloud computing and blockchain. The model guarantees confidentiality, high protection, and privacy of patient information, and provides all beneficiaries of cloud computing with all the needed information when wanted.

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.

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).

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.

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.