Showing papers in "International Journal of Service Science, Management, Engineering, and Technology in 2023"

An Effective Deep Learning Model to Discriminate Coronavirus Disease From Typical Pneumonia

Abstract: Current technological advances are paving the way for technologies based on deep learning to be utilized in the majority of life fields. The effectiveness of these technologies has led them to be utilized in the medical field to classify and detect different diseases. Recently, the pandemic of coronavirus disease (COVID-19) has imposed considerable press on the health infrastructures all over the world. The reliable and early diagnosis of COVID-19-infected patients is crucial to limit and prevent its outbreak. COVID-19 diagnosis is feasible by utilizing reverse transcript-polymerase chain reaction testing; however, diagnosis utilizing chest x-ray radiography is deemed safe, reliable, and precise in various cases.

Evaluation of Agricultural Policies and Programmes for Sustainable Future Farming Intensification in Nigeria

Abstract: Nigeria is enthusiastic with strong ambition and immense potential, which has been manifested in various agricultural frameworks and policies. Most of the agricultural frameworks and policies are meant by the government to attain food self-sufficiency and rapid economic development. Several studies have explored historical trends of government interventions and liberalization in the agricultural sector. However, the objective of the government has not been attained as a result of trajectory in efficient implementations of agricultural policies in the country. This paper therefore primarily aims at evaluating the agricultural policies and programmes for sustainability of future farming in Nigeria with specific focus on various determinants of sustainability of the agricultural sector, which will consequently bring food self-sufficiency, create employment, and bring about rapid economic growth in the country. The methodology used is qualitative content analysis of secondary data. The first part of the findings demonstrated that there is need to fill the gap in the existing agricultural frameworks and policies towards making future farming sustainable in the country. The second part of the result indicated that sustainability of agricultural sector for future farming intensification focused on the improvement of some major aspects such as viable implementation of agricultural policy, productive land for agricultural productivity, and government inputs to the farmers in reviving the agricultural sector. It is also noted that the interventions of the government through the building of shopping malls and establishment of national markets for marketability of various products such as groundnut, palm oil, rubber, cocoa, among others, can be instrumental in achieving sustainable future farming food domestic food production to attain food self-sufficiency and rapid economic development. In conclusion, the study showed that the implementation of agricultural policy is meaningful, and it can foster the sustainability of farming specifically in adequately utilizing unused land across the country. It is therefore suggested that the government needs to improve the input subsidies and to establish various national markets for selling different by-products and many others in order to achieve sustainability of the agricultural sector for future farming in the country.

Novel Architecture for Image Classification Based on Rough Set

Abstract: The Computed Tomography (CT) scan images classification problem is one of the most challenging problems in recent years. Different medical treatments have been developed based on the correctness of CT scan images classification. In this work, a novel deep learning architecture is proposed to correctly diagnose COVID-19 patients using CT scan images. In fact, a new classifier based on rough set theory is suggested. Extensive experiments showed that the novel deep learning architecture provides a significant improvement over well-known classifier. The new classifier produces 95% efficiency and a very low error rate on different metrics. The suggested deep learning architecture coupled with novel tolerance outperforms the other standard classification approaches for the detection of COVID-19 using CT-Scan images.

Supplier Selection in Family Small and Medium Enterprises: Modelling the Priority Attributes With an Integrated Entropy-MARCOS (E-MARCOS) Method

Abstract: Current literature on family businesses and small and medium enterprises (SMEs) offers some disintegrated insights in view of managing supply chains, particularly the supplier selection agenda. The presence of distinct characteristics inherent to both enterprises calls for a new line of inquiry regarding supplier selection for family SMEs. This work advances the literature by (1) identifying a set of supplier selection attributes best encapsulating the interests of family SMEs, and (2) evaluating these attributes to guide relevant decision-making. With a case study in the food industry and previous lists of supplier selection attributes, ten attributes were considered relevant to family SMEs. Applying the hybrid entropy-MARCOS method yields the priority attributes in decreasing order: on-time delivery, total service quality, product quality, productivity, attitude, response to customer requests, problem-solving capacity, payment terms, price, and flexibility. An analysis with other comparable methods suggests high consistency of these findings. Theoretical insights were discussed.

Exploring the Differences in Work Value Among Generations in the UAE

Abstract: This study aims to measure the work values of Generation X, Generation Y, and Generation Z and address the generational differences based on their work values. An appropriate sample was used where respondents were employees working, across the United Arab Emirates, in different industries in the private and public sectors. The data were collected from 130 employees based on a 45-item work value inventory (WVI). Fisher's least significant difference (LSD) statistical test was used to answer the research question based on multivariate tests across all three generations. Findings have suggested that Generation X placed more emphasis on work values as compared to Generation Z and Generation Y and were differentiated based on surroundings, altruism, and way of life work values.

Subspace Data-Driven Control for Linear Parameter Varying Systems

Abstract: In this research, a unique subspace data driven control for linear parameter changing system with scheduling parameters is presented. This control paves the way for investigating the nonlinear system based on the results regarding the linear system that are already known. Only the data matrix is utilized to represent the output prediction value in the future various time instants, while the input-output observation data matrix is used to identify Markov parameters in the form of state space forms. The cost function in data-driven control is then adjusted using the output prediction value. The optimal control input value of this quadratic cost function is solved using a parallel distribution technique, and the algorithm's iterative convergence is thoroughly examined. Finally, the DC motor, whose mass distribution factor is considered to be one linear parameter varying system, is controlled using the suggested subspace data driven control approach.

Design of a Closed-Loop Error-in-Variable System Controller and Its Application in Quadrotor UAV

Abstract: In view of the fact that the output is only disturbed by error in most of the current system studies, this article proposes a closed-loop variable system model with error (both input and output signals are disturbed by noise) and designs the controller of the system. In this study, minimum variance controller and self-correcting minimum variance controller are designed using minimum variance control. Then an example is given to evaluate the performance of the designed controller using minimum variance performance evaluation. Finally, the closed-loop variable error system is combined with the quadrotor UAV (unmanned aerial vehicle), and the position controller in the position control loop of the quadrotor UAV is designed. The experimental results show that the controller has good performance and can well meet the design needs.

Using Smart Applications in Delivering Government Services in the UAE

Abstract: This study addresses the public adoption of and satisfaction with UAE government e-services, emphasizing the quality of digital platforms, mainly smart applications. This quantitative research uses the technology acceptance, e-service quality, and web trust models. An online survey designed via Survey Monkey was distributed to Emirati citizens in Sharjah, Dubai, and Abu Dhabi municipalities via government Facebook and Instagram accounts in December 2022. The sample included the Ministries of Human Resources, Education, Interior, and Foreign Affairs, which provide daily individual and commercial services to citizens through digital applications. Three hundred respondents completed the form. The findings revealed that participants appreciate and trust the electronic services provided via smart applications, particularly their ease of use and quality of information. Factors motivating their use include information disclosure strategy and interactivity. The UAE's increasing use of smart digital applications necessitates training, user-friendly designs, and integration with rather than replacement of traditional direct methods.