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The advancement of teaching and learning across the majority of courses is made possible by information and communication technology (ICT), and Islamic studies is no exception. Education has been among the many industries that information and communication technology (ICT) has revolutionized. Islamic education has embraced ICT to improve teaching and learning processes since it is based on the Quran's teachings and the traditions of the Prophet Muhammad (peace be upon him). This article aims to address the role of digital technology, or ICT, in Islamic teaching and learning, as well as ICT applications for spreading Islamic education, which are becoming increasingly significant over time. Overall, this review offers insightful information about the state of ICT integration in Islamic education today and shows the upcoming directions for additional study and advancement in this area.

Abstract Pomology, also known as fruticulture, is a significant contributor to the economies of many nations worldwide. While vertical farming methods are not well-suited for fruit cultivation, substrate-based cultivation is commonly practiced. Vertical farming methods use no soil for cultivation of the plants, and the cultivation is done in vertically stacked layers. Therefore, smaller herbs are best suited for such cultivation, whereas, the majority of the fruit trees are big and woody. Therefore, vertical farming methods are not well suited for fruit trees. However, to maximize fruit production, smarter substrate cultivation methods are needed. Utilizing remote sensing techniques, such as Internet of Things (IoT) devices, agriculture sensors, and cloud computing, allows for precision agriculture and smart farming in autonomous systems. Nevertheless, a lack of understanding of fruit nutrient requirements, growing conditions, and soil health conditions can result in reduced fruit production. To address these challenges, this paper proposes an intelligent model based on machine learning that recommends the best fruit to grow based on prevailing soil and climatic conditions. The system is trained on a dataset that includes details on eleven different fruits, such as Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. The model takes into account the soil type and nutrient contents to recommend the most suitable fruit to grow in the prevailing climate. To enhance the model's efficiency, two novel techniques, Gradient-based Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), have been incorporated. The results show that the proposed system has achieved 99% accuracy in recommending the right fruit based on the given environmental conditions. As a result, this system has the potential to significantly improve the profitability of the pomology industry and boost national economies.

Gastrointestinal (GI) cancer is leading general tumour in the Gastrointestinal tract, which is fourth significant reason of tumour death in men and women. The common cure for GI cancer is radiation treatment, which contains directing a high-energy X-ray beam onto the tumor while avoiding healthy organs. To provide high dosages of X-rays, a system needs for accurately segmenting the GI tract organs. The study presents a UMobileNetV2 model for semantic segmentation of small and large intestine and stomach in MRI images of the GI tract. The model uses MobileNetV2 as an encoder in the contraction path and UNet layers as a decoder in the expansion path. The UW-Madison database, which contains MRI scans from 85 patients and 38,496 images, is used for evaluation. This automated technology has the capability to enhance the pace of cancer therapy by aiding the radio oncologist in the process of segmenting the organs of the GI tract. The UMobileNetV2 model is compared to three transfer learning models: Xception, ResNet 101, and NASNet mobile, which are used as encoders in UNet architecture. The model is analyzed using three distinct optimizers, i.e., Adam, RMS, and SGD. The UMobileNetV2 model with the combination of Adam optimizer outperforms all other transfer learning models. It obtains a dice coefficient of 0.8984, an IoU of 0.8697, and a validation loss of 0.1310, proving its ability to reliably segment the stomach and intestines in MRI images of gastrointestinal cancer patients.

Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model’s capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model’s superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches.

Playing a game is an essential factor that leads towards human development and then learning. Usually, it can be seen that children can spend hours in playing different non computer based games like hide and seek, board games, such as chess, Ludo, checkers and keep them engaged in their plays. These games not only keep them busy but also improvise their creative thinking and learning ability. When children play, it can be witnessed that they also learn while play. In this way, play can be related to learning, enhancing children’s cognitive and emotional development through social and cultural context. Digital Game Based Learning (DGBL) can also be useful as a supportive choice for traditional class lectures. The intent of DGBL is to incorporating new methods to existing instructional way and at also simultaneously, empower learners to gain skills and capabilities that may be beneficial in future endeavors. This paper explores the existing frameworks and models that are being used for the development of games for the classroom and identifies the 13 components that can be used for the development of history based games.

Brain tumor diagnosis at an early stage can improve the chances of successful treatment and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures, such as magnetic resonance imaging (MRI), can be used to diagnose brain tumors. Deep learning, a type of artificial intelligence, can analyze MRI images in a matter of seconds, reducing the time it takes for diagnosis and potentially improving patient outcomes. Furthermore, an ensemble model can help increase the accuracy of classification by combining the strengths of multiple models and compensating for their individual weaknesses. Therefore, in this research, a weighted average ensemble deep learning model is proposed for the classification of brain tumors. For the weighted ensemble classification model, three different feature spaces are taken from the transfer learning VGG19 model, Convolution Neural Network (CNN) model without augmentation, and CNN model with augmentation. These three feature spaces are ensembled with the best combination of weights, i.e., weight1, weight2, and weight3 by using grid search. The dataset used for simulation is taken from The Cancer Genome Atlas (TCGA), having a lower-grade glioma collection with 3929 MRI images of 110 patients. The ensemble model helps reduce overfitting by combining multiple models that have learned different aspects of the data. The proposed ensemble model outperforms the three individual models for detecting brain tumors in terms of accuracy, precision, and F1-score. Therefore, the proposed model can act as a second opinion tool for radiologists to diagnose the tumor from MRI images of the brain.

This paper presents a systematic review of relevant primary studies on the use of augmented reality (AR) to improve various skills of children and adolescents diagnosed with autism spectrum disorder (ASD) from years 2005 to 2018 inclusive in eight bibliographic databases. This systematic review attempts to address eleven specific research questions related to the learing skills, participants, AR technology, research design, data collection methods, settings, evaluation parameters, intervention outcomes, generalization, and maintenance. The social communication skill was the highly targeted skill, and individuals with ASD were part of all the studies. Computer, smartphone, and smartglass are more frequently used technologies. The commonly used research design was pre-test and post-test. Almost all the studies used observation as a data collection method, and classroom environment or controlled research environment were used as a setting of evaluation. Most of the evaluation parameters were human-assisted. The results of the studies show that AR benefited children with ASD in learning skills. The generalization test was conducted in one study only, but the results were not reported. The results of maintenance tests conducted in five studies during a short-term period following the withdrawal of intervention were positive. Although the effect of using AR towards the learning of individuals was positive, given the wide variety of skills targeted in the studies, and the heterogeneity of the participants, a summative conclusion regarding the effectiveness of AR for teaching or learning of skills related to ASD based on the existing literature is not possible. The review also proposes the research taxonomy for ASD. Future research addressing the effectiveness of AR among more participants, different technologies supporting AR for the intervention, generalization, and maintenance of learning skills, and the evaluation in the inslusive classroom environment and other settings is warranted.

The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes’ growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (°F), illumination (lx), and CO2 (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO2); when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system’s overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maximum of 15-18wh.

This ICT tools and technologies have become essential elements for the successful functioning of organizations. Consequent to that push, Pakistan government has provided ICT infrastructure including labs, Internet services, computers for both staff and students, access to the digital library, and video conference rooms especially from the year 2002 in educational institutes. Yet, Pakistan has not improved it ranking among world standards for ICT readiness as well as higher education management. Therefore, we assessed and measured ICT usage with the goal to know the adoption of ICT in terms of implementation, acceptance, and management in universities. Specifically, we focused on universities in Sindh province of Pakistan. In this research, we used the qualitative approach and conducted face to face interviews. Our target population included experienced administrative staff at important administrative posts and teaching staff who were familiar with ICT infrastructure in higher education institutions. Through this research, it was also revealed that, there ICT infrastructure is available in higher education institutions, yet, it lacks proper upgradation and that the human resource needs trainings. Furthermore, the analysis of data resulted in seven main themes that include adoption of ICT, utilization of ICT, ICT Policy of universities, higher authorities, attitude towards ICT, motivating factors, and challenges towards ICT success. It depicts the complete picture of ICT adoption and management practices in public sector universities in Sindh, Pakistan.

The adoption of Information Technology (IT) has remained a point of enthusiasm for the last couple of decades. The Technology Acceptance Models such as TAM are used to predict users’ behaviours. Studies on the ability to anticipate people's acceptance with regard to health information system (HIS) are lacking. This study focuses on the “Perceived Ease of USE (PEOU)” and the “Perceived Usefulness (PU)” of the HIS of healthcare professionals in Pakistan. The first step is to examine the level of acceptance and then the willingness to upgrade the HIS for the betterment of the healthcare services in Pakistan among the stakeholders. Consequently, this study used model and proposed that the HIS acceptance is determined by 5 main constructs, namely, “behavioural intention”, “attitude, perceived usefulness”, “perceived ease of use” and “technology self-efficacy”. In this model, “Technology Self-Efficacy (TSE)” had positive and significant effect on “perceived usefulness (PU)” and “Perceived ease of use (PEOU)”. “PEOU” has both positive and significant effects on “perceived usefulness (PU)” and attitude. “PU” had positive and significant effect on attitude and “behavioural intention”. “Attitude “had positive and significant effect on “behavioural intention”. A questionnaire was designed based on the conceptual model.A seven-factor Likert scale became used during the questionnaire. A survey was then performed after pre-testing the statistics collecting device. valid responses have been coded and analysed by means of the usage of the Statistical package for the Social Sciences (SPSS) model 21. each descriptive and inferential analyses had been done. The developed version become tested on a pattern of 200 health care specialists of Pakistan.

An efficient resource allocation scheme plays a vital role in scheduling applications on high-performance computing resources in order to achieve desired level of service. The major part of the existing literature on resource allocation is covered by the real-time services having timing constraints as primary parameter. Resource allocation schemes for the real-time services have been designed with various architectures (static, dynamic, centralized, or distributed) and quality of service criteria (cost efficiency, completion time minimization, energy efficiency, and memory optimization). In this analysis, numerous resource allocation schemes for real-time services in various high-performance computing (distributed and non-distributed) domains have been studied and compared on the basis of common parameters such as application type, operational environment, optimization goal, architecture, system size, resource type, optimality, simulation tool, comparison technique, and input data. The basic aim of this study is to provide a consolidated platform to the researchers working on scheduling and allocating high-performance computing resources to the real-time services. This work comprehensively discusses, integrates, analysis, and categorizes all resource allocation schemes for real-time services into five high-performance computing classes: grid, cloud, edge, fog, and multicore computing systems. The workflow representations of the studied schemes help the readers in understanding basic working and architectures of these mechanisms in order to investigate further research gaps.

This paper has assessed the implementation and acceptance of information and communication technology (ICT) at public sector universities in Pakistan. This research was conducted in three major public-sector universities based in one province of Pakistan. A total of 550 questionnaires were distributed and 325 were returned. In addition, three interviews were conducted from the administrative personnel of universities. The research was conducted to assess ICT implementation at universities and its acceptance by academicians. The base for this research was an integrated framework developed for this research called academicians acceptance and use of ICT (AAU-ICT). This framework was developed using; Unified theory of acceptance and use of technology (UTAUT) with an additional four constructs Culture, external incentives, perceived needs and job relevance. In order to make the results strong, a mixed method approach was adopted by incorporating both quantitative and qualitative methods. The results demonstrate that ICT is being implemented at universities, yet it has issues of equipment availability and proper environment, while academicians’ acceptance of ICT is more contingent on to their field of teaching than the need of ICT in profession.

Within the space of question answering (QA) systems, the most critical module to improve overall performance is question analysis processing. Extracting the lexical semantic of a Natural Language (NL) question presents challenges at syntactic and semantic levels for most QA systems. This is due to the difference between the words posed by a user and the terms presently stored in the knowledge bases. Many studies have achieved encouraging results in lexical semantic resolution on the topic of word sense disambiguation (WSD), and several other works consider these challenges in the context of QA applications. Additionally, few scholars have examined the role of WSD in returning potential answers corresponding to particular questions. However, natural language processing (NLP) is still facing several challenges to determine the precise meaning of various ambiguities. Therefore, the motivation of this work is to propose a novel knowledge-based sense disambiguation (KSD) method for resolving the problem of lexical ambiguity associated with questions posed in QA systems. The major contribution is the proposed innovative method, which incorporates multiple knowledge sources. This includes the question’s metadata (date/GPS), context knowledge, and domain ontology into a shallow NLP. The proposed KSD method is developed into a unique tool for a mobile QA application that aims to determine the intended meaning of questions expressed by pilgrims. The experimental results reveal that our method obtained comparable and better accuracy performance than the baselines in the context of the pilgrimage domain.

A collection of information, alongwith related services managed and stored in the digital formats for accessing over a digital network is called digital library. The study aims to calculate the acceptance of digital library program initiated by higher education commission of Pakistan. Higher education commission is monitoring body that overlooks the higher education institutions and provides them with required facilities, equipment’s and services. One of its biggest services is Digital library program, an outstanding national level digital library in Pakistan. For the sake of encourage the research culture and improve the digital infrastructure nation-wide, digital library is introduced. The study is based on a reviewing relevant literature on the subject to evaluate successes of digital library project and assessing its acceptance in perspective of users particularly research students of various universities and to provide theoretical framework towards acceptance of digital library by research students.

Everything in this world is encapsulated by space and time fence. Our daily life activities are utterly linked and related with other objects in vicinity. Therefore, a strong relationship exist with our current location, time (including past, present and future) and event through with we are moving as an object also affect our activities in life. Ontology development and its integration with database are vital for the true understanding of the complex systems involving both spatial and temporal dimensions. In this paper we propose a conceptual framework for building spatio-temporal database model based on ontological approach. We have used relational data model for modelling spatio-temporal data content and present our methodology with spatio-temporal ontological accepts and its transformation into spatio-temporal database model. We illustrate the implementation of our conceptual model through a case study related to cultivated land parcel used for agriculture to exhibit the spatio-temporal behaviour of agricultural land and related entities. Moreover, it provides a generic approach for designing spatiotemporal databases based on ontology. The proposed model is capable to understand the ontological and somehow epistemological commitments and to build spatio-temporal ontology and transform it into a spatio-temporal data model. Finally, we highlight the existing and future research challenges.

Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management.

For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain.

This paper investigates the use of a simplified set of Arabic phonemes in an Arabic Speech Recognition system applied to Holy Quran. The CMU Sphinx 4 was used to train and evaluate a language model for the Hafs narration of the Holy Quran. The building of the language model was done using a simplified list of Arabic phonemes instead of the mainly used Romanized set in order to simplify the process of generating the language model. The experiments resulted in very low Word Error Rate (WER) reaching 1.5% while using a very small set of audio files during the training phase when using all the audio data for both the training and the testing phases. However, when using 90% and 80% of the training data, the WER obtained was respectively 50.0% and 55.7%.

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

Universities around the world especially those in developing countries are faced with the problem of delivering the level of information and communications technology (ICT) needed to facilitate teaching, learning, research, and development activities ideal in a typical university, which is needed to meet educational needs in-line with advancement in technology and the growing dependence on IT. This is mainly due to the high cost involved in providing and maintaining the needed hardware and software. A technology such as cloud computing that delivers on demand provisioning of IT resources on a pay per use basis can be used to address this problem. Cloud computing promises better delivery of IT services as well as availability whenever and wherever needed at reduced costs with users paying only as much as they consume through the services of cloud service providers. The cloud technology reduces complexity while increasing speed and quality of IT services provided; however, despite these benefits the challenges that come with its adoption have left many sectors especially the higher education skeptical in committing to this technology. This article identifies the reasons for the slow rate of adoption of cloud computing at university level, discusses the challenges faced and proposes a cloud computing adoption model that contains strategic guidelines to overcome the major challenges identified and a roadmap for the successful adoption of cloud computing by universities. The model was tested in one of the universities and found to be both useful and appropriate for adopting cloud computing at university level.

Speech compression has been an area of interest for many researchers, and their objectives remained to reduce the bit rates, also to maintain the speech quality. Adaptive Differential Pulse Code Modulation (ADPCM) was first such scheme that evolved out of PCM and in low bit rate source coding techniques; Code Excited Linear Prediction (CELP) was adopted as standard in the Department of Defense (DoD), USA. In this research a novel approach is developed and compression up to 50% is achieved with negligible loss of quality. This coding technique is developed to use a PCM coded speech, initially, and then substituting 3 to 5 Least Significant Bits (LSB) of every byte at the decoding end from a fixed codebook, randomly.

Like most of the South-Asian languages Urdu and Sindhi are partial word order languages. Conventional syntax representation models like Context Free Grammars are not capable enough to cope with partial word order syntax. Linear Specification Language (LSL) is an extension of Context-Free Grammars (CFGs) which allows arbitrary partial order (free word order) on the right hand side of grammar rule. Partial word order in LSL is handled by using different types of linear precedence (LP) constraints. LSL by using LP constraints is capable enough to represent the syntax of partial word order sentence. Issues related to represent Urdu/Sindhi language sentences with their constituent parts in LSL are discussed. LSL versions for different types of Urdu and Sindhi sentences are presented.