Diabetes is a common chronic disease that needs early diagnosis and proper management to avoid severe complications. While current Artificial Intelligence (AI) tools generate predictive information, they often lack an integrated element for post-diagnosis support in order to fill in this critical gap in patient self-management. This research proposes and validates a hybrid System which aims to bridge this gap. The methodology is based on a novel, fused dataset (PIMA and Type 2 Diabetes) that was carefully preprocessed following a leakage-safe protocol in order to increase generalizability. The system architecture is a combination of two different critical components: Strong Bidirectional Long Short term Memory (BiLSTM) model for prediction and rule based engine for creating personalized lifestyle recommendations. In order to validate the efficacy of the BiLSTM model, seven traditional machine learning (ML) models and standard deep learning (DL) models have been comparatively tested, in which BiLSTM model has demonstrated a better generalization and prediction performance. Rigorous 10-fold cross validation was used to validate the system, which came up with an accuracy of 84.02%, precision of 87.89%, and recall of 80.50%. This research concludes that by successfully combining the high-performance predictive engine and real-time guidance module, it is possible to develop a holistic clinically relevant tool to close the loop between diagnosis and proactive self-management.

Aim/Purpose Early identification of at-risk students is a burning issue in higher education. Although conventional Machine Learning (ML) models have high predictive accuracy, they tend to be opaque black boxes and offer no diagnostic information. This paper aims to fill this diagnostic gap by developing an eXplainable AI (XAI)-based framework to convert technical risk scores into actionable prompts for students' self-regulation and reflection. Background Conventional academic assistance is reactive, meaning it is provided after failure has occurred. Although ML enables proactive identification, its lack of transparency prevents educators from offering specific support. This paper introduces a framework to improve student success by integrating explainability and fairness beyond classification, aligning technical AI performance with pedagogical objectives. Methodology Three ML models, Logistic Regression, Random Forest, and XGBoost, were used to analyze a refined sample of 278 student records. The methodology entailed a preprocessing pipeline of data. SHAP (Shapley Additive Explanations) was incorporated to support both global and local interpretability, whereas a formal Fairness Audit was performed to guarantee that risks were fairly detected across gender groups. Contribution The research introduced a novel human-focused framework that fills the gap between predictive analytics and pedagogical theory. It shows how XAI can help turn a technical risk score into a metacognitive prompt, encouraging data-driven conversations between educators and students and offering a clear roadmap for institutional interventions. Findings The analysis revealed that the Random Forest model achieved 92.9% accuracy and an AUC-ROC of 0.977. SHAP analysis found school absenteeism and midterm grades as the most important risk predictors. Individualized diagnostics (waterfall plots) in the system provided the necessary evidence through student self-reflection, and the audit of fairness ensured that the model supports gender groups equally. Recommendations for Practitioners Recommendations for Researchers Educational institutions must implement risk prediction systems based on XAI to go beyond mere warning systems. Practitioners should employ individual-level diagnostics to tailor mentoring and motivate students to reflect on their learning behaviors through evidence-based self-reflection. Further studies are expected to include longitudinal pilot studies that quantify the actual behavioral effects of XAI-based prompts on student outcomes. Researchers are also advised to test the framework using larger, multi-institutional datasets to increase its generalizability. Impact on Society Transparent and fair ML systems can improve student retention and graduation rates, leading to better resource allocation and a more inclusive educational environment. By focusing on student agency, these systems foster a more successful, self-aware workforce that benefits society in the long term. Future Research To establish the global applicability and ethical soundness of the XAI framework, future studies must explore real-time application of XAI to learning management systems and test the cross-cultural validity of behavioral predictors.

Classifying exam questions using Bloom’s Taxonomy is a critical task in education. However, performing this classification manually is often inefficient, prone to subjective errors and demands significant time from educators. To address this challenge, this study proposes and evaluates a robust system using state-of-the-art Deep Learning (DL) techniques. This system uses the pre-trained language model RoBERTa to understand the meaning of text and turn it into useful features. These features are then used by three different types of models to classify the text: a Gated Recurrent Unit (GRU), a Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN). To further enhance performance, a Weighted Ensemble model is implemented, which intelligently combines the predictions of the three models. The models were trained and evaluated using a publically available dataset. Experimental results show that all models demonstrate an outstanding efficacy and the weighted ensemble model outperforms all individual models. The ensemble model established a new standard of performance at 92.37% accuracy, macro F1-Score of 0.923 and an Area Under the Curve (AUC) of 0.992. Crucially, the marginal gain over the best single model was confirmed to be statistically significant via McNemar’s Test (P = 0.048). This paper presents a scalable system that will enable the automatic assessment of the objects in real-time with high accuracy that could ensure the objects in the assessment are not biased, would significantly reduce efforts put in by teachers and enhance the overall quality of educational practices.

Accurate effort estimation of user stories is a key challenge in agile software development due to both subjectivity and the complexity of natural language requirements. This paper proposes a hybrid Deep Learning (DL) model for data driven effort estimation using large scale textual data and advanced semantic modeling. One of the significant contributions is the development of a dataset of 6,956 user stories which was collected from many heterogeneous sources and then meticulously cleaned and refined to 4,079 high quality instances, by using a systematic preprocessing and expert validation. Following a Design Science Research (DSR) methodology, a hybrid model integrating a pre-trained Bidirectional Encoder Representations from Transformers (BERT) encoder with a Long Short-Term Memory (LSTM) is developed to capture both contextual semantics and sequential dependencies in user story description. The DL model is evaluated against multiple Machine Learning (ML) baselines using a robust multi-metric framework. Experimental findings show the superior performance of the proposed model with a Mean Absolute Error (MAE) = 0.6481, Root Mean Square Error (RMSE) = 1.4559 and  = 0.6581, which is a huge improvement over the conventional methods. To ensure practical relevance in discrete Scrum planning, the continuous model outputs were mapped to the standard Fibonacci sequence, achieving a classification accuracy of 72%. To guarantee the consistency of performance improvements, the statistical validation is done with the Wilcoxon Signed-Rank Test to show that the improvements are significant (p < 0.05). Moreover, the model is operationalized as web based decision support system to enable real time estimation in agile software development. The results reveal the efficacy of using large scale curated datasets alongside hybrid DL model to reduce estimation bias and boost predictive quality to deliver a scalable and strong solution for intelligent agile project management. Future work will focus on integrating eXplainable AI (XAI) techniques and validating the model across real world industrial datasets to further enhance transparency and generalizability.

The Scrum process model is widely adopted in the IT industry to improve project management and software development. However, there is an increasing need to enhance the structure and behavior of IT organizations within this framework. This research addresses this by proposing the integration of the Viable System Model (VSM) into the Scrum process. The VSM is a proven framework for analyzing and improving the viability of complex systems. Integrating VSM into Scrum provides a holistic view of an organization, considering its internal and external interactions, power dynamics, and overall viability. This integration aims to enhance organizational effectiveness and improve project outcomes. The research investigates how VSM can optimize Scrum in IT organizations by identifying and addressing challenges such as communication gaps, decision-making inefficiencies, and power struggles. By leveraging VSM insights, organizations can better align roles, responsibilities, and processes, leading to increased productivity, improved collaboration, and stronger project performance. The proposed approach is tested using actual software project data, and results show reduced delays, improved quality, and higher stakeholder satisfaction. In conclusion, this study advances the Scrum model by incorporating VSM to strengthen organizational structure and behavior, resulting in better project outcomes.

This paper proposes an expert system for detecting dengue using a fuzzy logic approach in Pakistan. A knowledge-based system represents an expert system, which is one of the most frequent types of Artificial Intelligence in Medicine (AIM), with medical knowledge of a clearly defined goal and the ability to reach the correct conclusion. In a proposed system, the knowledge of a particular issue is typically represented by a set of rules rather than individual variables. Through mosquito bites an infected mosquito transmits the dengue virus that functions as a pathogen exclusively in human bodies. Dengue fever is an infectious tropical disease. The risk of dying from dengue fever increases when the diagnosis is delayed, despite the fact that only a small fraction of people infected with the disease actually develop severe symptoms. Because of this, it is essential to diagnose dengue fever in its earliest stages. As a result, the main purpose of this research was to construct an expert system for the early detection of dengue disease utilizing the Fuzzy Inference System (FIS), a potent instrument for coping with imprecision and uncertainty. The system takes a patient’s physical symptoms as input and translates them into fuzzy membership functions for analysis. The system that was designed can be used to assist a patient in receiving an early diagnosis of dengue disease. The proposed system has been tested on real data sets and achieved a remarkable accuracy rate of 96%.

Chronic kidney disease (CKD) is a non-communicable condition that often leads to renal failure, which is when the kidneys are no longer able to filter waste and excess fluid from the body's circulation. Early detection of CKD can slow the progression of the illness and reduce the overall cost of treatment. In this work, a novel approach for identifying CKD using a fuzzy logic system is proposed. The fuzzy system includes eight input parameters such as weight, age, serum creatinine, diastolic blood pressure, systolic blood pressure, glomerular filtration rate, blood glucose, and smoking. The output variable describes a specific patient’s stage of chronic renal disease based on these input factors. The output will indicate the current stage of a patient’s kidney disease. This system can therefore assist specialists in determining the stage of chronic renal disease. The MATLAB software is used to create the fuzzy system. The proposed system is tested on a real data set of patients and the results obtained are promising.

Malaria continues to be a pressing public health issue in tropical regions, with its deleterious effects on human health well documented. While a minority of cases may be life threatening, delayed diagnosis can significantly worsen the severity of malaria. This paper presents the Recommender and Decision Support System for Malaria Management (RDSSMM), designed to assist researchers, physicians and healthcare professionals in malaria endemic areas. RDSSMM is made up of four main components: a knowledge base, fuzzification module, inference engine, and defuzzification module. Mayo Hospital Lahore, Pakistan was used to develop the fuzzy expert system based on experts’ opinions in the field of medicine. For ease of access, a mobile application has been included which allows patients to get primary diagnosis by keying in their physical signs that are then transformed into fuzzy membership functions. The data was collected from the outpatient department of Mayo Hospital Lahore as one way of evaluating RDSSMM’s effectiveness. Comparing its predictions to expert medical diagnoses and reports pointed out this system’s performance. Malaria risk was correctly predicted 80% of the time by RDSSMM. These findings show that RDSSMM can greatly improve malaria management in rural areas through provision of quick and accurate results, which would facilitate timely decisions regarding malaria testing and monitoring.

In today’s business environments, data-driven decision-making has played a crucial role in bringing real-time sustainability to organizational behavior. Furthermore, the Internet of Things (IoT) has been widely adopted recently, but studies investigating its impact on organizational dynamics and sustainability are limited. This study explores the IoT’s potential to improve the real-time decision-making and sustainability of organizations. A cross-sectional study was conducted on 250 respondents belonging to a diverse range of industries. Five variables were analyzed in the study—IoT implementation, real-time data analytics, decision-making, organizational behavior, and organizational performance. These variables were scored on a 5-point Likert-type scale, with responses ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The data were collected with the help of a structured questionnaire. The data were analyzed using descriptive statistics, Pearson correlations, and structural equation modeling (SEM) to test the relationships among the study variables. The findings indicate that enhanced organizational behavior (r = 0.1101), decision-making (r = 0.269), and real-time data analytics (r = 0.1888) are all strongly associated with the IoT. Applying structural equation modeling further reveals a direct connection between IoT adoption and company performance. Moreover, it is observed that the coefficients for organizational behavior (β = 0.0707, p < 0.01) and real-time data analytics (β = 0.0851, p < 0.001) are statistically significant. This study demonstrates how the IoT can bring real-time sustainability to organizational behavior by improving decision-making and business operations. Although decision-making is enhanced by the IoT, organizations still need to optimize their resource usage and reduce waste to enhance sustainability. This study bridges a substantial gap in the existing literature by carefully investigating the relationship between IoT technology, organizational behavior, and decision-making techniques. Our findings conclude that leveraging the IoT changes the nature of digital innovation and brings real-time sustainability to organizational behavior.

The current investigation examines the numerical performance of the fractional-order endemic disease model based on the direct spreading of cholera by applying the neuro-computing Bayesian regularization (BR) neural network process. The purpose is to present the numerical solutions of the fractional-order model, which provides more precise solutions as compared to the integer-order one. Real values based on the parameters can be obtained and one can achieve better results by utilizing these values. The mathematical form of the fractional direct spreading cholera disease is categorized as susceptible, infected, treatment, and recovered, which represents a nonlinear model. The construction of the dataset is performed through the implicit Runge–Kutta method, which is used to lessen the mean square error by taking 74% of the data for training, while 8% is used for both validation and testing. Twenty-two neurons and the log-sigmoid fitness function in the hidden layer are used in the stochastic neural network process. The optimization of BR is performed in order to solve the direct spreading cholera disease problem. The accuracy of the stochastic process is authenticated through the valuation of the outputs, whereas the negligible calculated absolute error values demonstrate the approach’s correctness. Furthermore, the statistical operator performance establishes the reliability of the proposed scheme.

Determining factors influencing the success of software projects has been the emphasis of extensive research for more than 40 years. However, the majority of research in this domain has focused on developed countries, with little attention paid to underdeveloped and developing countries. The primary objective of this article was to assess the effect of critical elements on the success of software projects in underdeveloped countries (like Pakistan), because enterprise environmental factors and staff working habits, as well as their experience and expertise level, all have an effect on a project's success. For this purpose, data were collected from 339 senior developers and project managers working in Pakistan Software Export Board (PSEB) registered software companies. Structural Equation Modelling (SEM) was used to analyze the constructs and to assess the relationship between factors affecting software success. The empirical results showed that improper planning, inadequate human resources, wrong estimation of time and cost significantly negatively impacted the success of software projects. This research has opened new doors to extend our work in the software community to ultimately succeed in software projects.

Cloud systems are tools and software for cloud computing that are deployed on the Internet or a cloud computing network, and users can use them at any time. After assessing and choosing cloud providers, however, customers confront the variety and difficulty of quality of service (QoS). To increase customer retention and engagement success rates, it is critical to research and develops an accurate and objective evaluation model. Cloud is the emerging environment for distributed services at various layers. Due to the benefits of this environment, globally cloud is being taken as a standard environment for individuals as well as for the corporate sector as it reduces capital expenditure and provides secure, accessible, and manageable services to all stakeholders but Cloud computing has security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Allocation of resources in the Cloud is difficult because resources provide numerous measures of quality of service. In this paper, the proposed resource allocation approach is based on attribute QoS Scoring that takes into account parameters the reputation of the asset, task completion time, task completion ratio, and resource loading. This article is focused on the cloud service's security, cloud reliability, and could performance. In this paper, the machine learning algorithm neuro-fuzzy has been used to address the cloud security issues to measure the parameter security and privacy, trust issues. The findings reveal that the ANFIS-dependent parameters are primarily designed to discern anomalies in cloud security and features output normally yields better results and guarantees data consistency and computational power.

Load balancing is a technique for identifying overloaded and under loaded nodes and balancing the load between them. To maximize various performance parameters in cloud computing, researchers suggested various load balancing approaches. To store and access data and services provided by the different service providers through the network over different regions, cloud computing is one of the latest technology systems for both end-users and service providers. The volume of data is increasing due to the pandemic and a significant increase in usage of the internet has also been experienced. Users of the cloud are looking for services that are intelligent, and, can balance the traffic load by service providers, resulting in seamless and uninterrupted services. Different types of algorithms and techniques are available that can manage the load balancing in the cloud services. In this paper, a newly proposed method for load balancing in cloud computing at the database level is introduced. The database cloud services are frequently employed by companies of all sizes, for application development and business process. Load balancing for distributed applications can be used to maintain an efficient task scheduling process that also meets the user requirements and improves resource utilization. Load balancing is the process of distributing the load on various nodes to ensure that no single node is overloaded. To avoid the nodes from being overloaded, the load balancer divides an equal amount of computing time to all nodes. The results of two different scenarios showed the cross-region traffic management and significant growth in revenue of restaurants by using load balancer decisions on application traffic gateways.

Cloud computing is an emerging domain that is capturing global users from all walks of life-the corporate sector, government sector, and social arena as well. Various cloud providers have offered multiple services and facilities to this audience and the number of providers is increasing very swiftly. This enormous pace is generating the requirement of a comprehensive ecosystem that shall provide a seamless and customized user environment not only to enhance the user experience but also to improve security, availability, accessibility, and latency. Emerging technology is providing robust solutions to many of our problems, the cloud platform is one of them. It is worth mentioning that these solutions are also amplifying the complexity and need of sustenance of these rapid solutions. As with cloud computing, new entrants as cloud service providers, resellers, tech-support, hardware manufacturers, and software developers appear on a daily basis. These actors playing their role in the growth and sustenance of the cloud ecosystem. Our objective is to use convergence for cloud services, software-defined networks, network function virtualization for infrastructure, cognition for pattern development, and knowledge repository. In order to gear up these processes, machine learning to induce intelligence to maintain ecosystem growth, to monitor performance, and to become able to make decisions for the sustenance of the ecosystem. Workloads may be programmed to "superficially" imitate most business applications and create large numbers using lightweight workload generators that merely stress the storage. In today's current IT environment, when many enterprises use the cloud to service some of their application demands, a different performance testing technique that assesses more than the storage is necessary. Compute and storage are merged into a single building block with HCI(Hyper-converged infrastructure), resulting in a huge pool of compute and storage resources when clustered with other building blocks. The novelty of this work to design and test cloud storage using the measurement of availability, downtime, and outage parameters. Results showed that the storage reliability in a hyper-converged system is above 92%.

With the ongoing revolution in the Internet of Things (IoT) and cloud computing has made the potential of every stack holder that is connected through the Internet, to exchange and transfer data. Various users perceive this connection and interaction with devices as very helpful and serviceable in their daily life. However, an improperly configured network system is a soft target to security threats, therefore there is a dire need for a security embedded framework for IoT and cloud communication models is the latest research area. In this paper, different IoT and cloud computing frameworks are discussed in detail and describes the importance of the daily life of people. The main focus is to design the Cloud-IoT integration that is used to implement IoT and Cloud Framework for M2M communication, also building a relationship between different devices to connect through a cloud and also find different security methods to secure those devices. Extensive papers finding and results showed different ways they have been introduced to manipulate M2M in the digital field of health care and the virtual world. While focusing on the methodology used in M2M it is also imperative to concentrate on security levels from different inside and outside attacks on IoT and cloud ecosystem. There is a need to create a strong and secure connection between all of our IoT devices with a cloud so that there should be a fixed and safe connection between cloud environments concerning M2M connection between all wired and wireless devices. Meanwhile in contemplation of security mode also conducive to maintain M2M connection between IoT devices and Cloud and in which areas these methodologies have been implemented.

Diabetes is one of the ever-increasing menace crippling millions of people worldwide. It is an independent risk factor for many cardiovascular diseases including medium and small vessels and results in heart attack, stroke, kidney failure, blindness, and lower-limb amputations. According to a World Health Organization (WHO) report estimated 1.6 million deaths were the direct result of diabetes. Nutrition plays a vital role in diabetes management alongside physical activity, drugs, and insulin. Weight management can help to avert or delay at pre-diabetic stages. This research work explains the features of the Nutrition Diet Expert System (NDES), which will preferably be used by the health care professionals (HCPs) for calculating per day calorie requirements of diabetic patients and recommend the best diet plan to control diabetes. The primary objective of this proposed model for diet plan is to help individuals attain healthy body weight and optimum check on diabetes by gaining control over blood pressure and lipid count. The ultimate focus of this research result is prevention of diabetes related complications using nutrition diet expert system. In this paper, proposed recommender system has come in handy in figuring out the diet plan by determining the individual dietary requirements at the level of micro and macro nutrients for expert system using fuzzy logic. The results are very promising indicating testing and assessment of the expert system worked well for the individual diet plan.

The past two decades witnessed a broad-increase in web technology and on-line gaming. Enhancing the broadband confinements is viewed as one of the most significant variables that prompted new gaming technology. The immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud servers. As internet capabilities are enhanced new ways of gaming are being used to improve the gaming experience. In cloud-based video gaming, game engines are hosted in cloud gaming data centers, and compressed gaming scenes are rendered to the players over the internet with updated controls. In such systems, the task of transferring games and video compression imposes huge computational complexity is required on cloud servers. The basic problems in cloud gaming in particular are high encoding time, latency, and low frame rates which require a new methodology for a better solution. To improve the bandwidth issue in cloud games, the compression of video sequences requires an alternative mechanism to improve gaming adaption without input delay. In this paper, the proposed improved methodology is used for automatic unnecessary scene detection, scene removing and bit rate reduction using an adaptive algorithm for object detection in a game scene. As a result, simulations showed without much impact on the players' quality experience, the selective object encoding method and object adaption technique decrease the network latency issue, reduce the game streaming bitrate at a remarkable scale on different games. The proposed algorithm was evaluated for three video game scenes. In this paper, achieved 14.6% decrease in encoding and 45.6% decrease in bit rate for the first video game scene.

Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity. Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs. In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English tweets. Word embedding is a notable arrangement of procedures that can remember words linguistics dependent on the spread theory which expresses that word is utilized and happens within the same settings tend to indicate comparable implications. Bag of words is an approach used in natural language processing to retrieve information and features from written documents. For the bag of words, machine learning techniques like naive bayes, decision tree, k-nearest neighbor, and support vector machine is used to enhance the accuracy. For word embedding the neural network technique is proposed by the combina-tion of recurrent neural network (RNN) with long-short term memory (LSTM) for sentimental analysis of tweets. Datasets of Urdu and English tweets are used for negative and positive classification tweets with machine learning techniques. The contribution of this paper involves the implementation of a hybrid approach that focused on a sentiment analyzer to overcome social network challenges and also provided the comparative analysis of different machine learning algorithms. The results indicate improvement while using the combination of RNN with the help of LSTM showed accuracy 87% on the Urdu dataset and 92% on the English dataset.

The goal of this paper is to deliberate issues pertaining to teamwork effectiveness among students in a university environment. Final year project is an important assessment to measure students’ teamwork skills in the university. Three main factors have been identified that contribute to teamwork effectiveness. The factors are namely interpersonal skills, interdependence and commitment to success. Interpersonal factors include social sensitivity and emotional engagement attributes. In this perspective, measures such as teamwork support, care, trust, honesty and respect towards team members are important key criteria. On the other hand, interdependence among others includes traits such as promoting each other to achieve common goals, bringing the best of each other and helping each other completing the task. Commitment to team success involves high obligation, high motivation, strong common goals and strong shared values and beliefs. In this paper, construct items for each factor are developed based on previous studies. The findings show that these factors are relevant toward the success of the student’s final year project. Lacking these skills may result in poor performance among team members and may lead to unfavorable outcome.

Recently, several computer applications provided operating mode through pointing fingers, waving hands, and with body movement instead of a mouse, keyboard, audio, or touch input such as sign language recognition, robot control, games, appliances control, and smart surveillance. With the increase of hand-pose-based applications, new challenges in this domain have also emerged. Support vector machines and neural networks have been extensively used in this domain using conventional RGB data, which are not very effective for adequate performance. Recently, depth data have become popular due to better understating of posture attributes. In this study, a multiple parallel stream 2D CNN (two-dimensional convolution neural network) model is proposed to recognize the hand postures. The proposed model comprises multiple steps and layers to detect hand poses from image maps obtained from depth data. The hyper parameters of the proposed model are tuned through experimental analysis. Three publicly available benchmark datasets: Kaggle, First Person, and Dexter, are used independently to train and test the proposed approach. The accuracy of the proposed method is 99.99%, 99.48%, and 98% using the Kaggle hand posture dataset, First Person hand posture dataset, and Dexter dataset, respectively. Further, the results obtained for F1 and AUC scores are also near-optimal. Comparative analysis with state-of-the-art shows that the proposed model outperforms the previous methods.