Ali M. Aseere's research while affiliated with King Khalid University and other places

At present global software development (GSD) is gaining considerable attention in software industry. The management of global software projects presents substantial complexity owing to several inherent challenges of GSD. The project management practices used to execute in-house development projects are inadequate to address the unique challenges posed by global software projects, making their management a formidable task. Software organizations rely on traditional project management practices with the aim of managing global software projects, often resulting in impairments or failures. This paper explores the critical success factors (CSFs) in project management for global projects by developing a framework for effective project management within the context of GSD. The study focuses on identifying and prioritizing CSFs in software project management within a GSD setting utilizing Multi Criteria Decision Making (MCDM) analysis methods. Therefore, present research provides an extensive literature review of CSFs in software project management within GSD. Additionally, the research applies the combinatorial approach to assess the various dimensions and CSFs of software project management in GSD. The proposed approach aids in measuring and comparing the effect of several dimensions and CSFs of software project management in GSD. Five dimensions and twenty factors have been determined through literature review and further evaluated for prioritization using the combinatorial approach. The identified dimensions and factors will be valuable in devising strategies to effectively manage global software projects.

Localization of multiple targets is a challenging task due to immense complexity regarding data fusion received at the sensors. In this context, we propose an algorithm to solve the problem for an unknown number of emitters without prior knowledge to address the data fusion problem. The proposed technique combines the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurement data fusion which further uses the maximum likelihood of the measurements received at each sensor of the surveillance region. The measurement grids of the sensors are used to perform data association. The simulation results show that the proposed algorithm outperforms the multipass grid search and further effectively eliminated the ghost targets created due to the fusion of measurements received at each sensor. Moreover, the proposed algorithm reduces the computational complexity compared to other existing algorithms as it does not use repeated steps for convergence or any biological evolutions. Furthermore, the experimental testing of the proposed technique was executed successfully for tracking multiple targets in different scenarios passively.

Digital technology advancement and the Internet of Things (IoT) are playing a major role to take a big leap towards achieving Industry 4.0. Cloud-based data management and big data analytics have given rise to adopt Cloud Enterprise Resource Planning (CERP). The CERP has become a significant tool for the success of the information management system (IMS) which is ultimately responsible for the success of any organization. The selection of CERP depends on many critical success factors (CSFs) that must be considered while evaluating and selecting a CERP. In this work, identified CSFs of CERP are modeled using a multi-criteria decision-making (MCDM) approach. The Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) based modelling have been carried out to derive the ranking of the CSFs responsible for the CERP. The group decision-making (GDM) based AHP has also been adopted to build the decision-making model. The paper models 5 dimensions and 20 sub-criteria factors to provide the prioritized rank of dimensions and sub-criteria factors. The AHP and FAHP models identify the ranking of the 5 dimensions as Organizational Behavior, Cloud ERP Essentials, Technological Advancement, Innovational Ideas, and Environmental Impact.

Brain Hemorrhage is the eruption of the brain arteries due to high blood pressure or blood clotting that could be a cause of traumatic injury or death. It is the medical emergency in which a doctor also need years of experience to immediately diagnose the region of the internal bleeding before starting the treatment. In this study, the deep learning models Convolutional Neural Network (CNN), hybrid models CNN+LSTM and CNN+GRU are proposed for the Brain Hemorrhage classification. The 200 head CT scan images dataset is used to boost the accuracy rate and computational power of the deep learning models. The major aim of this study is to use the abstraction power of deep learning on a set of fewer images because in most crucial cases extensive datasets are not available on the spot. The image augmentation and imbalancing the dataset methods are adopted with CNN model to design a unique architecture and named as Brain Hemorrhage Classification based on Neural Network (BHCNet). The performance of the proposed approach are analyzed in terms of accuracy, precision, sensitivity, specificity and F1-score. Further, the experimental results are evaluated by comparative analyses of the balanced and imbalanced dataset with CNN, CNN+LSTM and CNN+GRU models. The promising results are achieved with CNN by imbalancing the dataset and gain highest accuracy that outperforms the hybrid CNN+LSTM and CNN+GRU models. The results reveals the effectiveness of the proposed model for accurate prediction to save the life of the patient in the meantime and fast employment in the real life scenario.

The purpose of smart city is to enhance the optimal utilization of scarce resources and improve the resident’s quality of live. The smart cities employed Internet of Things (IoT) to create a sustainable urban life. The IoT devices such as sensors, actuators, and smartphones in the smart cities generate data. The data generated from the smart cities are subjected to analytics to gain insight and discover new knowledge for improving the efficiency and effectiveness of the smart cities. Recently, the application of deep learning in smart cities has gained a tremendous attention from the research community. However, despite raise in popularity and achievements made by deep learning in solving problems in smart cities, no survey has been dedicated mainly on the application of deep learning in smart cities to show recent progress and direction for future development. To bridge this gap, this paper proposes to conduct a dedicated survey on the applications of deep learning in smart cities. In this paper, recent progress, new taxonomies, challenges and opportunities for future research opportunities on the application of deep learning in smart cities have been unveiled. The paper can provide opportunities for experts in the research community to propose a novel approach for developing the research area. On the other hand, new researchers interested in the research area can use the paper as an entry point.

In the higher education sector, there is a growing trend to offer academic information to users through websites. Contemporarily, the users (i.e., students/teachers, parents, and administrative staff) greatly rely on these websites to perform various academic tasks, including admission, access to learning management systems (LMS), and links to other relevant resources. These users vary from each other in terms of their technological competence, objectives, and frequency of use. Therefore, academic websites should be designed considering different dimensions, so that everybody can be accommodated. Knowing the different dimensions with respect to the usability of academic websites is a multi-criteria decision-making (MCDM) problem. The fuzzy analytic hierarchy process (FAHP) approach has been considered to be a significant method to deal with the uncertainty that is involved in subjective judgment. Although a wide range of usability factors for academic websites have already been identified, most of them are based on the judgment of experts who have never used these websites. This study identified important factors through a detailed literature review, classified them, and prioritized the most critical among them through the FAHP methodology, involving relevant users to propose a usability evaluation framework for academic websites. To validate the proposed framework, five websites of renowned higher educational institutes (HEIs) were evaluated and ranked according to the usability criteria. As the proposed framework was created methodically, the authors believe that it would be helpful for detecting real usability issues that currently exist in academic websites.

The escalating growth in digital technology is setting the stage for changes in university education, as E-learning brings students and faculties outside the contained classroom environment. While mobile learning is considered an emerging technology, there is comprehensive literature on mobile learning and its applications. However, there has been relatively little research on mobile learning recognition and readiness compared to mobile learning studies and implementations. The advent of mobile learning (M-learning) provides additional flexibility in terms of time and location. M-learning lacks an established place in university education. The influence of its critical success factors (CSFs) on the university education system must be analyzed and understood. In the present study, decision-makers establish four dimensions which are further classified into 13 CSFs to evaluate and rank them. It is imperative to judge the most important CSFs and rank them according to their importance. To this end, multi-criteria decision-making (MCDM), like the fuzzy analytic hierarchy process (FAHP), is an important tool to establish the influence of each CSF. It identifies the four dimensions of M-learning by evaluation in a crisp and fuzzy environment. Global and local weights have been employed for ranking in a decision-making process to enable universities to choose the best adoption factor for mobile learning. The result establishes the influence of CSFs in M-learning success, in decreasing order, as the technological dimension (TD), individual/user dimension (ID), pedagogical dimension (PD), and social dimension (SD). A greater understanding of the mobile learning implementation process can allow researchers and decision-makers to collaborate to incorporate effective mobile learning strategies.

Energy is considered the most costly and scarce resource, and its demand is increasing day-by-day. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30-40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in the residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 04 multi-storied buildings situated in Seoul, South Korea has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the MAE, MAPE, and RMSE have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better as compared to simple data and normalized data.