Bilgisayar Mühendisliği Bölümü Makale Koleksiyonu
Bu koleksiyon için kalıcı URI
Güncel Gönderiler
Öğe Coupled Multimodal Emotional Feature Analysis Based on Broad-Deep Fusion Networks in Human-Robot Interaction(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Chen, Luefeng; Li, Min; Wu, Min; Pedrycz, Witold; Hirota, KaoruA coupled multimodal emotional feature analysis (CMEFA) method based on broad-deep fusion networks, which divide multimodal emotion recognition into two layers, is proposed. First, facial emotional features and gesture emotional features are extracted using the broad and deep learning fusion network (BDFN). Considering that the bi-modal emotion is not completely independent of each other, canonical correlation analysis (CCA) is used to analyze and extract the correlation between the emotion features, and a coupling network is established for emotion recognition of the extracted bi-modal features. Both simulation and application experiments are completed. According to the simulation experiments completed on the bimodal face and body gesture database (FABO), the recognition rate of the proposed method has increased by 1.15% compared to that of the support vector machine recursive feature elimination (SVMRFE) (without considering the unbalanced contribution of features). Moreover, by using the proposed method, the multimodal recognition rate is 21.22%, 2.65%, 1.61%, 1.54%, and 0.20% higher than those of the fuzzy deep neural network with sparse autoencoder (FDNNSA), ResNet-101 + GFK, C3D + MCB + DBN, the hierarchical classification fusion strategy (HCFS), and cross-channel convolutional neural network (CCCNN), respectively. In addition, preliminary application experiments are carried out on our developed emotional social robot system, where emotional robot recognizes the emotions of eight volunteers based on their facial expressions and body gestures.Öğe Efficient cloud data center: An adaptive framework for dynamic Virtual Machine Consolidation(Academic Press, 2024) Rozehkhani, S.M.; Mahan, F.; Pedrycz, W.Cloud computing is a thriving and ever-expanding sector in the industry world. This growth has sparked increased interest from organizations seeking to harness its potential. However, the sheer volume of services and offerings in this field has resulted in a noticeable surge in related data. With the rapid evolution and growing demand, cloud computing resource management faces a fresh set of challenges. Resource limitations, such as high maintenance costs, elevated Energy Consumption (EC), and adherence to Service Level Agreements (SLA), are critical concerns for both the cloud computing industry and its user organizations. In this context, taking a proactive approach to resource management and Virtual Machine Consolidation (VMC) has become imperative. The logical management of resources and the consolidation of Virtual Machines (VMs) in a manner that aligns with the requirements and demands of service providers and users have garnered widespread attention. The goal of this proposed paper is to focus on addressing the VMC problem within a unified framework, divided into two main phases. The first phase deals with host workload detection and prediction, while the subsequent phase tackles the selection and allocation of appropriate VMs. In our proposed method, for the first time, we use a Granular Computing (GRC) model, which is an efficient, scalable, and human-centric computational approach. This model exhibits behaviors similar to intelligent human decision-making, as it can simultaneously consider all factors and criteria involved in the problems. We evaluated our proposed method through simulations using CloudSim on various types of workloads. Experimental results demonstrate that our proposed algorithm outperforms other algorithms in all measurement metrics. © 2024 Elsevier LtdÖğe Trust exploration- and leadership incubation- based opinion dynamics model for social network group decision-making: A quantum theory perspective(Elsevier B.V., 2024) Wang, P.; Liu, P.; Li, Y.; Teng, F.; Pedrycz, W.Social network (SN) holds significant sway over the consciousness and behavior of involved individuals, serving as an evolutionary medium for opinion dynamics models. Consensus is a fundamental concept in group decision-making (GDM). How to effectively explore the opinion evolution during the consensus-reaching process through SN is of paramount significance for decision-making science. Therefore, a dual-mechanism containing trust exploration and leadership incubation is developed for modeling opinion dynamics, creating a favorable condition for achieving consensus. First, a novel mechanism for analyzing the completeness of SN is proposed, encompassing a trust propagation process that considers trust discount and stability, as well as a trust aggregation method grounded by quantum theory. Second, a trust screening rule is discussed to retain the valid indirect trust relationships (TRs), and then a leadership incubation mechanism is developed to promote the effective achievement of consensus opinions in group decision-making. Finally, a numerical study is presented to elucidate the superiority and rationality of the proposed methods, and some simulation experiments and comparative analyses demonstrating the effectiveness and advantages of which are covered. © 2024Öğe KERMA, projected range, mass stopping power and gamma-ray shielding properties of antimony and tellurium reinforced iron phosphate glasses(Elsevier Ltd, 2024) Alan, H.Y.; Yilmaz, A.; Susam, L.A.; Ozturk, G.; Kilic, G.; Ilik, E.; Oktik S.In this study, the radiation shielding effectiveness of iron phosphate glass (Fe2O3–P2O5) doped with antimony (Sb) and tellurium (Te) is assessed in detail using advanced computational methods. The projected range, mass stopping power, and KERMA (Kinetic Energy Released per unit MAss) for fourteen different iron phosphate glass samples are calculated through the PAGEX and SRIM software. Mass attenuation coefficients, linear attenuation coefficients, mean free path, half value layers, tenth value layers, and effective atomic number are determined in 0.015–15 MeV energy range. The research reveals that doping iron phosphate glass with Sb2O3 significantly enhances its shielding capabilities when compared to the inclusion of TeO2. Another important aspect is, the IPGSb50 sample exhibited the highest KERMA values, indicating its exceptional capacity for energy absorption from ionizing radiation. Additionally, the IPGSb50 sample exhibited the lowest projected range for alpha particles, also this sample demonstrated a similar prowess in limiting the penetration of proton particles. Our findings indicate that the incorporation of Sb2O3 and TeO2 into iron phosphate glass matrices results in a noticeable improvement in gamma radiation shielding effectiveness. These doped glasses could serve as potent alternatives to traditional lead-based shielding materials, offering a safer and potentially more effective barrier against a variety of radiation types. © 2024 Elsevier LtdÖğe An assessment of microstructure, dentinal tubule occlusion and X-ray attenuation properties of Nd:YAG laser-enhanced titanium-doped phosphate glass and nano-hydroxyapatite pastes(Springer Science and Business Media Deutschland GmbH, 2024) Neel, E.A.A.; Aldamanhoury, H.M.; Hossain, K.M.Z.; Alawadhi, H.; ALMisned, G.; Tekin, H.O.This research evaluated the dentinal tubule occlusion capabilities of titanium dioxide-doped phosphate glass (TDPG) paste, with and without adjunctive Nd:YAG laser treatment in comparison to nanohydroxyapatite (n-HA) that also contains calcium phosphate. In total, forty etched dentin (ED) discs were allocated into eight groups: untreated ED (G1), laser-treated ED (G2), TDPG paste-applied ED (G3), TDPG paste followed by laser (G4), TDPG microspheres paste-applied ED (G5), microspheres paste followed by laser (G6), n-HA paste-applied ED (G7), and n-HA paste followed by laser (G8). The interventions were assessed using scanning electron microscopy (SEM) for morphological changes, counting opened dentinal tubules and elemental analysis. Additionally, the study incorporated an evaluation of radiological properties, specifically the materials' mass attenuation coefficients, effective atomic numbers, and exposure buildup factors, to ascertain their compatibility with X-ray imaging modalities. Findings indicated that paste application alone created a layer of precipitated crystals, effectively occluding dentinal tubules. Subsequent laser treatment enhanced occlusion by reducing opened dentinal tubules by approximately 50%, created a dense layer of altered TDPG or n-HA crystals with modified phosphorus and calcium composition. The inclusion of radiological assessment suggested that these materials, particularly when combined with laser, have potential not only for treating dentin hypersensitivity but also compatible with radiographic diagnostic processes. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.Öğe Advancing Medical Recommendations With Federated Learning on Decentralized Data: A Roadmap for Implementation(Institute of Electrical and Electronics Engineers Inc., 2023) Kumari, R.; Sah, D.K.; Gupta, S.; Cengiz, K.; Ivkovic, N.This proposal presents a road-map for implementing federated learning for personalized medical recommendations on decentralized data. Federated learning is a privacy-preserving technique allowing multiple parties to train machine learning models collaboratively without sharing their data. Our proposed framework incorporates differential privacy techniques to protect patient privacy. We discuss several evaluation metrics, including KL divergence, fairness, confidence intervals, top-N hit rate, sensitivity analysis, and novelty to evaluate the performance of the federated learning system.These metrics collectively serve as a robust toolbox for assessing the performance of the federated learning system. The proposed framework and evaluation metrics can provide valuable insights into the system’s effectiveness and guide the selection of optimal hyperparameters and model architectures. Our framework incorporates differential privacy methods to safeguard patient information effectively. IEEEÖğe An extensive benchmark analysis of advanced ceramic-concretes towards strategic material selection for nuclear applications and waste management(Elsevier Ltd, 2024) AlMisned, G.; Susoy, G.; Sen, Baykal, D.; Kilic, G.; Tekin, H.O.Ceramic concretes, with their exceptional durability and ability to incorporate a high percentage of heavy metal oxides, are of critical importance for nuclear radiation facilities, offering superior radiation attenuation characteristics essential for long-term safety and protection. This study presents a detailed evaluation of the gamma-ray shielding properties of various concrete composites, including Standard Concrete and Heavy Concretes (HC series), with densities ranging from 1.94 g/cm3 to 4.54 g/cm3. Utilizing computational methods, we analyzed several gamma-ray and neutron shielding parameters such as mass attenuation coefficients, linear attenuation coefficients, half and tenth value layers, mean free paths, exposure build-up factors, effective atomic number (Zeff), effective electron density (Neff), fast neutron effective removal cross-section (?R), and photon transmission factors (TFs). Our research reveals that the shielding efficacy of concrete is intrinsically linked to its density and elemental composition, with higher densities and the incorporation of heavy elements leading to enhanced attenuation capabilities. Among the concretes studied, Limonite with Steel Punch LS-a, which contains 74.53% Fe in its structure, exhibited the lowest transmission factors (TFs) across all tested thicknesses and energy levels (0.662, 1.1732, and 1.3325 MeV), indicating its superior photon attenuation potential. It can be concluded that the concrete samples with a higher Fe (iron) content in their structure demonstrate clear superiority in gamma-ray attenuation properties. © 2024 Elsevier Ltd and Techna Group S.r.l.Öğe Constructing order-2 information granules of linguistic expressions with the aid of the principle of justifiable granularity(Elsevier B.V., 2024) Huang, T.; Pedrycz, W.; Zhang, Q.; Tang, X.; Yang, S.To capture collective opinions/evaluations in a collection of individual linguistic expressions, this study proposes an approach to construct order-2 information granules by extending the numerical data-based principles of justifiable granularity to a linguistic data-based one. First, the two key criteria of the principle of justifiable granularity, namely coverage and specificity, are formally defined in the context of order-2 information granules. Second, three order-2 information granules construction models by maximizing the product of coverage and specificity are developed for coping with one-dimensional direct linguistic expressions, linguistic preference relations, and multi-dimensional direct linguistic expressions, respectively. Third, considering that the developed order-2 information granules construction models exhibit a non-linear uncertain objective function and equality constraints, the constrained multi-swarm PSO without velocity is improved with the use of the ? constrained handling technique for effectively solving the models. Case studies on the considered three types of linguistic expressions show the applicability of the proposed models and ensuing algorithm. The superiority of the improved algorithm in handling the proposed models is demonstrated by comparison with the original one. The effectiveness of the proposed models in terms of abnormal corrective ability and balance of coverage and specificity is verified by comparing with a family of Top-n methods. The originality of this study lies in the construction of an operational entity, namely order-2 information granule, that reflects the group opinion without specifying the formalism of individual linguistic expressions, which provides an effective and efficient way to aggregate ubiquitous linguistic information for subsequent computation, reasoning and decision-making. © 2024 Elsevier B.V.Öğe Semi-supervised community detection method based on generative adversarial networks(King Saud bin Abdulaziz University, 2024) Liu, X.; Zhang, M.; Liu, Y.; Liu, C.; Li, C.; Wang, W.; Zhang X.Community detection in complex networks often suffers from insufficient data and limited utilization of prior knowledge. In this paper we propose “Semi-supervised Generative Adversarial Network” (GANSE), a novel algorithm that integrates Generative Adversarial Networks (GANs) and semi-supervised learning to address these challenges. This method addresses the issues above through a multi-step process. Initially, the network is rewired using vertex similarity metrics, thereby enhancing its structural integrity. Subsequently, a novel generative adversarial network model is designed, and our model facilitates the reconstruction of the network, thereby yielding partitions. Which form the basis for identifying core communities. Additionally, the local clustering coefficient is incorporated as a reward signal and injected into the node selection process. Moreover, isolated nodes are reallocated, ultimately culminating in the derivation of the final community structure. Experimental results on four large real-life datasets demonstrate the clear superiority of the proposed algorithm in terms of F1 and Jaccard metrics when compared to existing algorithms. Notably, our GANSE method outperforms the traditional algorithms in networks with “missing data”. Thus showing its robustness and effectiveness in real-world incomplete datasets. Our findings highlight the potential of GANs and semi-supervised learning for enhancing community detection accuracy in complex networks. © 2024 The Author(s)Öğe Investigation of borate glasses reinforced with iron (III) oxide(Elsevier B.V., 2024) Alfryyan, N.; Alsaif, N.A.M.; Al-Ghamdi, H.; Issa, S.A.M.; Zakaly, H.M.H.; El-Hamalawy, A.A.; Sadeq M.S.Five samples of borate glasses reinforced with various ratios of Fe3+ ions with chemical formula (60-X)B2O3–10ZnO–10CdO–5Li2O–15Na2O-XFe2O3: (0.0 (Fe-0.0) ? X ? 1 (Fe-1.0) mol%) were prepared using the melt-quenching technique. The structure, physical, optical characteristics, and ?-ray attenuation properties of the prepared Fe-X glasses have been investigated. The amorphous state of Fe-X glasses confirmed by XRD observations. Density (?glass) of the Fe-X glasses increased from 3.21 g cm?3 to 3.91 g cm?3. Molar volume (Vm) gradually reduced from 18.87 cm3/mol to 15.35 cm3/mol with increasing Fe2O3 content in the glass network from 0.0 to 1.0 mol%. The color of Fe-X glasses changed from yellow to brown according to the absorption bands located at around 385–412 nm and 407–429 nm within the visible spectrum. The indirect optical band gap (Eg) decreased from 3.37 eV to 2.78 eV, while the linear refractive index (no) increased from 2.31 to 2.45. Mass- (MAC) and linear- (LAC) attenuation coefficients increased as Fe3+ ions increased in the glass network. The Fe-1.0 sample with highest Fe2O3 content possessed the lowest values of half-value layer (HVL) and mean free path (MFP) shielding parameters. Results revealed that the suggested glass compositions can be applied in optical and radiation protection fields. © 2024 Elsevier B.V.Öğe Synthesis and characterization of Bi2O3–B2O3–SiO2–MgO glasses: Structural, optical, and shielding properties(Elsevier B.V., 2024) Alsafi, K.; Aloraini, D.A.; Issa, S.A.M.; Zakaly, H.M.H.; Assem, E.E.; Shaaban, K.S.The systematic investigation of the impact of Bi2O3 on the structural, spectroscopic, and radiation features of 15SiO2 - 75B2O3 - (10-x) MgO - xBi2O3, x= (0 ? x ? 10) glasses were studied. The increase in glass density as Bi2O3 content increase is due to the incorporation of Bi2O3 into the glass network. XRD patterns indicate an amorphous, glassy nature of the glasses. The optical absorption spectra suggest that as the content of Bi2O3 increases, the optical band gap (Eopt.) widens and the Urbach energy (Eu) decreases. It has been observed that the opposite trend between the (Eopt.) and (Eu). The radiation shielding characteristics of MgBi glasses have been evaluated through the assessment of several fundamental radiation attenuation variables. As Bi2O3 content increases would enhance properties like (HVL), (MFP), and (Zeq), especially at lower energy ranges. Adding Bi2O3 to the glassy system has a positive impact on enhancing its neutron attenuation ability. The MgBi-10 glass has the best shielding performance against fast neutrons. The MgBi-10 glass sample shows promising properties for optical and radiation shielding purposes. © 2024 Elsevier B.V.Öğe Enhancing radiation shielding transmission factors and mechanical Robustness of borosilicate glasses through Bi2O3 modification: A comprehensive study(Elsevier Ltd, 2024) Almousa, N.; Issa, S.A.M.; Tekin, H.O.; Rammah, Y.S.; Mostafa, A.M.A.; Sen, Baykal, D.; Alshammari K.The mechanical behavior and gamma radiation attenuation features of borosilicate glasses with chemical compositions 16ZnO–8BaO-5.5SiO2-0.5Sb2O3-(70-x)B2O3/xBi2O3 are extensively investigated. Makishima-Mackenzie principle, Monte Carlo code, and Phy-X/PSD software are utilized in terms of determining these properties. Our results showed that the total packing density (Vt) decreased from 0.634851 to 0.571458, while the total dissociation energy increased from 26.612 (kJ/cm3) to 29.652 (kJ/cm3) for S1-glass (with 10 mol% of Bi2O3) and S5-glass (with 30 mol% Bi2O3). All elastic moduli are enhanced by increasing the Bi2O3 additive in the investigated glasses. Poisson's ratio was decreased from 0.281226 for S1-glass to 0.256957 for S5-glass. In terms of gamma-ray shielding parameters; linear (?) and mass attenuation (?m) coefficients for the rich glass sample with B2O3 (S5) possess the highest values among all investigated (S1–S5) samples. The glass sample S5 is reported with the lowest values of tenth (TVL) and half (HVL) value layers among all studied glasses. In addition, the exposure (EBF) and energy absorption (EABF) bulidup factors were decreased with increasing the amount of Bi2O3 reinforcement for mean free path values from 0.5 to 40 mfp. The lowest possible levels of attenuation (minimum transmission) were measured at a thickness of 3 cm for all of the glass samples. © 2024 Elsevier LtdÖğe Securing Cloud-based Healthcare Applications with a Quantum-resistant Authentication and Key Agreement Framework(Elsevier B.V., 2024) Bahache, A.N.; Chikouche, N.; Akleylek, S.A biosensor is a method for transmitting various physical phenomena, such as body temperature, electrocardiogram (ECG), pulse, blood pressure, electroencephalogram (EEG), and respiratory rate. This transmission occurs through the utilization of a Wireless Body Area Network (WBAN) when remotely diagnosing patients via Internet-of-Medical-Things (IoMT). However, the transmission of sensitive data from IoMT through WBAN via an insecure channel exposes it to various threats, necessitating the implementation of robust measures to guarantee security against potential adversaries. To address the security concerns associated with patient monitoring in healthcare systems and achieve the necessary security and privacy requirements during communication, a robust authentication framework is indispensable. Hence, it introduces an agile and robust post-quantum authentication framework for cloud-based healthcare applications, effectively mitigating the vulnerabilities identified in the recent literature. This framework is designed to protect against quantum attacks using the Kyber. A formal security verification of the proposed protocol is presented using AVISPA, as well as informally. Additionally, a comparison with the previous works is made regarding both performance and security. The comparison results conclusively show that our proposed framework is better regarding both measures. © 2024 Elsevier B.V.Öğe Towards a collective opinion generation approach with multiple objectives for evaluating rail transit station accessibility in urban areas(Elsevier B.V., 2024) Chen, Z.-S.; Wang, Y.; Chen, Y.-H.; Mardani, A.; Pedrycz, W.; Martínez, L.Urban rail transit can alleviate traffic congestion if sufficient ridership is achieved. The accessibility to rail transit stations largely determines public willingness to utilize urban rail transit. The current studies on the accessibility to rail transit stations tend to concentrate solely on individual rail transit lines or stations, neglecting the impact of the external macro-environment. Consequently, the outcomes of these measurements cannot accurately reflect the overall accessibility level of the region, nor do they furnish decision-makers with references for devising plans for rail transit development. This study introduces a novel approach for evaluating the accessibility to urban rail transit stations from a macro-level standpoint, utilizing expert knowledge. The present study conducts an analysis of the impact of political, economic, social, and technological factors on the accessibility to rail transit stations in urban areas. Subsequently, a comprehensive evaluation indicator system is developed based on the aforementioned analysis. Then, experts are summoned to provide their subjective evaluations for every indicator, which are depicted as probability distribution functions. The collective evaluation is derived through the aggregation of individual evaluations utilizing a bi-objective optimization approach that factors in both the collective fairness utility and the confidence level. Finally, the quantile average method is employed to consolidate the collective assessment outcomes of individual indicators, thereby deriving the level of accessibility to rail transit stations in urban areas. We design a small-scale application experiment and attempt to evaluate the accessibility to rail transit stations in Wuchang District, Wuhan with the proposed approach in order to demonstrate the feasibility of the approach. © 2024 The Author(s)Öğe Hybrid deep learning and metaheuristic model based stroke diagnosis system using electroencephalogram (EEG)(Elsevier, 2023) Sawan, Aktham; Awad, Mohammed; Qasrawi, Radwan; Sowan, MohammadOver the last few decades, there has been a significant increase in the average lifespan. Consequently, the number of elderly people suffering from strokes has also risen. As a result, strokes and their treatments have become crucial subjects of research, particularly for the application of machine learning. One of the primary factors in stroke treatment is the speed of response. Currently, both computed tomography (CT) and magnetic resonance imaging (MRI) are used to diagnose strokes. However, CT takes eight hours before an accurate diagnosis can be made, and MRI is expensive and not available in all hospitals. Therefore, there is a growing need for novel approaches to identifying strokes based on electroencephalogram (EEG) signals. In this paper, a hybrid model of deep learning and metaheuristic was developed in the offline stage to classify strokes. Since EEG data is a time series with frequencies, a hybrid model was deemed appropriate. This hybrid model combined a Convolutional Neural Network (CNN) with bidirectional Gated Recurrent Unit (BiGRU). The performance of this model surpassed that of other comparable models. Given the paramount importance of speed and accuracy in this work, the harmony search (HS) algorithm, which is specialized in handling frequencies, was used for feature selection. HS outperformed all similar algorithms when applied to the CNN-BiGRU hybrid model. Additionally, for the optimization of continuous hyperparameters, the multiverse optimization (MVO) algorithm was employed, which proved to be the most effective when compared to another similar algorithm for validation purposes. The new model, CNN-BiGRU-HS-MVO, was applied to analyze the data collected from Al Bashir Hospital using the MUSE-2 portable device, resulting in an impressive prediction accuracy of 99.991%. Moreover, it demonstrated an 11.08% improvement over the results from the paper titled “Predicting stroke severity with a 3-min recording from the Muse portable EEG study”. Furthermore, a decision support system was built on the cloud computing environment based on the hybrid model. This system allows for the diagnosis of patients anytime and from anywhere within minutes, with the authorized person receiving the diagnosis results through SMS notification.Öğe Prevalence and risk factors associated with dysglycemia among overweight and obese Palestinian children in the Hebron governorate(F1000Research, 2023) Al-Halawa, Diala Abu; Polo, Stephanny Vicuna; Qasrawi, RadwanBackground: The prevalence of dysglycemia among adolescents and younger children has been rising, yet health professionals are still unaware of the significance of this problem. According to the Palestinian Ministry of Health (MOH) records, most diabetic children under the age of 20 in Palestine are classified as type I; nonetheless, very limited data are available for policymakers to frame cost-effective screening programs. This study aims to determine the prevalence of dysglycemia in a sample of obese and overweight Palestinian children, identify risk factors associated with dysglycemia, and examine risk factors variance by gender. Methods: A cross-sectional sample of observed obese and overweight children was selected from public schools in the Hebron governorate. Informed consent, physical examination, anthropometric, and laboratory tests (Blood Glucose Level (BGL) and fasting BGL ) were performed on a sample of 511 students (44.6% boys and 55.4% girls) aged 13–18-years (13-15 years =46.2% and 16-18 years =53.8%). Results: The prevalence of confirmed overweight and obese cases was 73.2%, and dysglycemia prevalence among the confirmed cases was 3.7% (5.3% boys and 2.5% girls). The BMI classifications of the prediabetic children indicated that 42.1% were overweight and 31.1% were obese. Furthermore, 6.7% reported hypertension (both systolic and diastolic hypertension). Conclusions: The results of this study provide valuable information about the rising problem of dysglycemia among Palestinian children and underlines the need for rapid screening programs and protocols for early detection and classification of the disease, leading to initiation of early prevention and treatment plans.Öğe Antibiotic resistance knowledge, attitudes, and practices among pharmacists : a cross-sectional study in West Bank, Palestine(Hindawi, 2023) Al-Halawa, Diala Abu; Seir, Rania Abu; Qasrawi, RadwanAntibiotic resistance is an increasing problem worldwide. Dispensing antibiotics without prescription is a major contributing factor to antibiotic resistance. Pharmacists as healthcare providers are, in many studies, considered responsible for this practice. -is study aims to explore Palestinian pharmacists’ knowledge, attitudes, and practices concerning antibiotic resistance. A descriptive cross-sectional survey was conducted in 2021–2022. A random sample of 152 pharmacists was selected from the West Bank. Data were collected using a self-administered questionnaire that includes :ve sections: demographic characteristics, knowledge, attitudes, practices, and potential interventions. Results indicated that 60% of pharmacists dispense antibiotics without a prescription. A signifcant association between pharmacies’ locality and antibiotic knowledge, attitudes, and practices was found. Pharmacists’ knowledge-related responses indicated that 92.1% of the pharmacists agreed that inappropriate use of antibiotics can lead to in effective treatment and 86.2% disagreed that patients can stop taking antibiotics upon symptoms’ improvement. Only 17.1% disagreed that antibiotics should always be used to treat upper respiratory tract infections. Over two thirds considered that they are aware of the regulations about antibiotic dispensing and acknowledged that antibiotics are classified as prescription drugs. Furthermore, 71.7% and 53.3% agreed that they have good knowledge of the pharmacological aspects of antibiotics and antibiotic resistance. Concerning attitudes, 75.6% agreed that antibiotic resistance is an important and serious public health issue facing the world, and 52% thought that antibiotic dispensing without a prescription is a common practice in the West Bank. Our findings indicate that pharmacists’ locality and practices related to antibiotic dispensing without prescription are associated with the increase in antibiotics misuse and bacterial resistance. -ere is a need to design education and training programs and implement legislation in Palestine to decrease antibiotic resistance.Öğe Status and correlates of food and nutrition literacy among parents-adolescents’ dyads: findings from 10 Arab countries(Frontiers Media, 2023) Hoteit, Maha; Mansour, Rania; Mohsen, Hala; Bookari, Khlood; Hammouh, Fadwa; Allehdan, Sabika; AlKazemi, Dalal; Al Sabbah, Haleama; Benkirane, Hasnae; Kamel, Iman; Qasrawi, Radwan; Tayyem, ReemaBackground: Food literacy is capturing the attention worldwide and gaining traction in the Arab countries. Strengthening food and nutrition literacy among Arab teenagers are important promising empowering tools which can protect them from malnutrition. This study aims to assess the nutrition literacy status of adolescents with the food literacy of their parents in 10 Arab countries. Methods: This cross-sectional study involving a convenient sample of 5,401 adolescent-parent dyads (adolescents: mean age?±?SD: 15.9?±?3.0, females: 46.8%; parents: mean age?±?SD: 45.0?±?9.1, mothers: 67.8%) was launched between 29 April and 6 June 2022 in 10 Arab nations. The Adolescent Nutrition Literacy Scale (ANLS) and the Short Food Literacy Questionnaire (SFLQ) were used to meet the study aims. Results: More than one-quarter (28%) of adolescents had poor nutrition literacy, with 60% of their parents being food illiterate. The top three countries with nutritionally” less literate” adolescents were Qatar (44%), Lebanon (37.4%), and Saudi Arabia (34.9%). Adolescents’ age, gender, education level, primary caregivers, employment status, and the inclusion of nutrition education in the schools’ curriculum predicted the nutrition literacy levels of Arab adolescents. Besides, parental weight status, health status, parent’s food literacy level, and the number of children per household were significant determinants too. Adolescents studying at a university and having parents with adequate food literacy had the highest odds of being nutritionally literate (OR?=?4.5, CI?=?1.8–11.5, p?=?0.001, OR?=?1.8, CI?= 1.6–2.1, p?Öğe Perspectives and practices of dietitians with regards to social/mass media use during the transitions from face-to-face to telenutrition in the time of COVID-19: A cross-sectional survey in 10 Arab countries(Frontiers, 2023) Bookari, Khlood; Arrish, Jamila M.; Alkhalaf, Majid H.; Alharbi, Mudi; Zaher, Sara M.; Alotaibi, Hawazin; Tayyem, Reema; Al-Awwad, Narmeen; Qasrawi, Radwan; Allehdan, Sabika; Al Sabbah, Haleama; AlMajed, Sana; Al Hinai, Eiman; Kamel, Iman; El Ati, Jalila; Harb, Ziad; Hoteit, MahaDuring the COVID-19 pandemic, most healthcare professionals switched from face-to-face clinical encounters to telehealth. This study sought to investigate the dietitians’ perceptions and practices toward the use of social/mass media platforms amid the transition from face-to-face to telenutrition in the time of COVID-19. This cross-sectional study involving a convenient sample of 2,542 dietitians (mean age?=?31.7?±?9.5; females: 88.2%) was launched in 10 Arab countries between November 2020 and January 2021. Data were collected using an online self-administrated questionnaire. Study findings showed that dietitians’ reliance on telenutrition increased by 11% during the pandemic, p?=?0.001. Furthermore, 63.0% of them reported adopting telenutrition to cover consultation activities. Instagram was the platform that was most frequently used by 51.7% of dietitians. Dietitians shouldered new difficulties in dispelling nutrition myths during the pandemic (58.2% reported doing so vs. 51.4% pre-pandemic, p?Öğe Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID?19 pandemic(BioMed Central, 2023) Qasrawi, Radwan; Hoteit, Maha; Tayyem, Reema; Bookari, Khlood; Al Sabbah, Haleama; Kamel, Iman; Dashti, Somaia; Allehdan, Sabika; Bawadi, Hiba; Waly, Mostafa; Ibrahim, Mohammed O.; The Regional Corona Cooking Survey Group; Polo, Stephanny Vicuna; Al?Halawa, Diala AbuBackground A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%, 13.7%, 13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally.
