Heart disease remains a leading cause of death globally, underscoring the need for early detection to improve outcomes and reduce healthcare cost. This study proposes a machine learning-based approach to predict heart disease risk using a comprehensive dataset covering demographic, lifestyle, and clinical variables. The study addresses limitations in prior research by focusing on fairness, interpretability, and practical deployment for clinical utility. The methodological framework involved stratified data splits and careful oversampling to prevent leakage. The anonymised Kaggle/BRFSS dataset was partitioned into 70 % training, 15 % validation and 15 % test sets, and SMOTE oversampling was applied only on the training split to balance the minority (heart disease) class. Feature selection via mutual information highlighted HadAngina, BMI and AgeCategory. Hyper-parameter-tuned models-including Logistic Regression, Random Forest, XGBoost, K-nearest neighbour and multilayer perceptron-were trained using stratified 5-fold crossvalidation with fixed random seeds; XGBoost achieved the highest performance (accuracy 91.99%, ROC-AUC 0.9756). Fairness was assessed using demographic-parity difference, equal-opportunity (true-positive-rate) gap and calibration error across sex, age and the official race/ethnicity categories (White, Black, Asian, Hispanic and Other). Initial results revealed lower recall for younger and Black individuals. A simple re-weighting of the training data and group-specific threshold adjustments reduced the demographic-parity difference to 0.0875 with minimal impact on accuracy. SHAP values provided global and local explanations of feature contributions-crucial for building trust among clinicians and patients. The model was deployed via a Flask-based web app, providing real-time heart disease risk assessments with user-friendly interfaces and explanations for clinicians and the public. This study bridges AI and healthcare by delivering a fair, transparent, and accessible predictive tool. Future enhancements include adversarial debiasing, wearable data integration, and expanding to other cardiovascular conditions to improve diagnostic accuracy, reduce inequities, and increase trust in AIdriven healthcare.

This study examines how firm size shapes performance and risk-taking among FTSE 100 firms from 2018-2023, a period spanning Brexit and COVID-19 market turbulence. Using daily market data, performance is defined by next-day stock price direction, firm size by the logarithm of market capitalization, and risk-taking by rolling return volatility. A comparative supervised machine-learning framework evaluates Logistic Regression, Support Vector Machines, Random Forest, Extra Trees, Decision Trees, Gradient Boosting, and K-Nearest Neighbors on scaled features that include volatility, leverage, and capital intensity, with cross-validated out-of-sample tests. The main contribution is UK-specific evidence that a regularized linear classifier (Logistic Regression) achieves state-of-the-art accuracy while preserving interpretability for corporate finance decisions, outperforming more flexible non-linear models on noisy, high-frequency equity data. Logistic Regression attains the top performance (97 % accuracy; ROC-AUC 0.98), with SVM close behind; tree ensembles consistently rank volatility as the most influential predictor of performance. Empirically, larger firms exhibit more stable returns and lower unstructured risk, consistent with superior risk management capacity. Practically, the results provide a reproducible screening pipeline for FTSE 100 equities by risk profile and size, and theoretically they indicate that the dominant decision boundary in this feature space is near-linear.

The integration of modern authentication methods such as multifactor authentication (MFA), biometric verification, and password-less authentication in legacy systems presents significant challenges due to outdated infrastructure, compatibility issues, and security vulnerabilities. This study analyzes the difficulties associated with implementing these authentication techniques in legacy environments and proposes viable solutions. A combination of software-based adaptation layers, API gateways, and Zero Trust frameworks can help bridge the gap between traditional and modern security paradigms. The research also includes algorithmic approaches for retrofitting authentication mechanisms, security models for risk assessment, and implementation case studies. The results indicate that a hybrid approach combining incremental upgrades with identity federation mechanisms enhances authentication security while minimizing operational disruptions. The study concludes that legacy systems require a strategic balance between modernization efforts and system stability, ensuring long-term security and efficiency in authentication processes.

This study assesses four machine learning (ML) algorithms – Decision Tree (DT), Random Forest (RF), K-Nearest Neighbours (KNN) and Support Vector Machines (SVM) – for predicting customer churn in banking. It explores optimisation methods including SMOTE, hyperparameter tuning and feature importance to enhance model effectiveness. Results indicate that RF outperforms the others, showing significant metric improvements over previous studies.

This study investigates task allocation optimization in IT project management using cooperative game theory, focusing on the Shapley Value approach. Effective task allocation is critical in IT projects to balance technical proficiency and team cooperation. By modeling developers and tasks within a cooperative framework, this research contributes to the field by proposing a systematic method to quantify individual contributions and maximize team performance. Through a Python-based implementation, the study demonstrates the scalability of the approach and evaluates its effectiveness in various scenarios, focusing on fairness and efficiency.

Chest radiographs (CXRs) remain one of the most widely used imaging modalities for diagnosing cardiopulmonary diseases, yet increasing imaging demand and a shortage of radiologists continue to challenge diagnostic accuracy and efficiency. Deep learning approaches offer promising solutions, but their clinical adoption is hindered by limited interpretability. In this study, we compare a baseline convolutional neural network (CNN) augmented with Gradient-weighted Class Activation Mapping (Grad-CAM) to a multimodal framework that integrates large language models (LLMs) for explainable CXR interpretation. The baseline CNN, trained on the ChestX-ray8 dataset of 108,848 images across 14 thoracic pathologies, achieved a mean Area under Curve (AUC) of 0.891 and macro F1-score of 0.80, with Grad-CAM heatmaps providing visual explanations of model predictions. In contrast, the multimodal framework combined CNN-derived features with clinical narratives using cross-attention and fewshot prompting and was fine-tuned on 68,000 paired image-report samples. This approach improved diagnostic performance (AUC=0.915, F 1=0.89), generated human-readable reports, and localized anatomical features to enhance interpretability and clinical reasoning. Our findings demonstrate that multimodal AI substantially improves precision and transparency over CNN-only systems, offering a more reliable decision-support tool to assist radiologists, reduce diagnostic errors, and optimize workflow in high-volume clinical environments.

This article investigates methods for optimizing traffic light control parameters with the aim of improving urban traffic efficiency. Modern approaches to ensuring the sustainability of urban transport systems are discussed. The study concludes that simulation modeling serves as a leading methodology for analyzing transportation systems and identifying optimal solutions to diverse challenges, which ultimately enhances traffic flow characteristics and increases the resilience of the transport network. An optimized simulation model of a major city intersection has been developed, an optimization experiment carried out, and optimal values for traffic light signal phases have been determined.

This project develops a blockchain-based web application aimed at verifying pharmaceutical products, a critical step in combating counterfeit drugs. By leveraging Ethereum's Sepolia test network and the power of smart contracts, the system facilitates secure, transparent processes for registering, verifying, and tracking pharmaceutical items. The application combines a PHP-based backend with a JavaScript-powered frontend, seamlessly integrating tools like MetaMask for user authentication and Web3.js to enable blockchain communication. The study underscores the significant advantages blockchain offers over traditional verification methods, particularly in terms of data integrity, transparency, and security. As a result, it meets the CIA triad model's requirements for confidentiality and integrity in information security. The novel features of the project include the use of optimism roll-ups for scalability, two-factor authentication (2FA), and data encryption to address critical challenges often overlooked in similar proposals for blockchainbased authentication systems. The results of the project demonstrate a tangible improvement in supply chain transparency and fraud prevention within the pharmaceutical industry. This work lays a strong foundation for further exploration of decentralised applications, not only in pharmaceutical validation but also across other essential sectors.

This study presents the design and development of a real-time, machine learning-based human activity recognition (HAR) system. The research focused on building a Convolutional Neural Network (CNN) model integrated into a web-based application for real-time detection and classification of human activities. Four experimental setups were implemented: a baseline CNN, CNN with data augmentation, VGG16 with transfer learning and augmentation, and Inception ResNetV2 with augmentation. The models were trained and validated using a labeled human activity image dataset from Kaggle. Model performance was evaluated using accuracy, precision, recall, and computational cost. The models were implemented using TensorFlow and Keras in Python, and deployed via a Flask-based web application for real-time activity detection. Among all models, the Inception ResNetV2 with data augmentation achieved the best performance (accuracy$=0.762$, precision$=0.84$, recall$=0.69$), demonstrating strong generalization and suitability for real-time applications. The novelty of this work lies in integrating an optimized deep learning model with a web-based real-time processing framework, offering an efficient and scalable solution for activity recognition in domains such as healthcare and sports.

This paper presents a real-time assistive system designed to support visually impaired individuals through object detection, voice-guided navigation, and speech-based information retrieval. The system integrates the YOLOv5s deep learning model for object recognition and a speech interface for intuitive auditory feedback. The proposed model was implemented on a system powered by an Intel(R) Core (TM) i5-8250U CPU @ 1.60$\mathbf{G H z}$(up to 1.80 GHz) with 8 GB RAM and integrated Intel(R) UHD Graphics$\mathbf{6 2 0} \boldsymbol{(} \mathbf{1 2 8 ~ M B} \boldsymbol{)}$, utilizing the built-in system camera for real-time video input. Quantitative evaluation demonstrated an object detection accuracy of$89.6 \% \text{mAP}$, a navigation success rate of 96%, and a speech recognition accuracy of 93%. The system achieved an average latency of 0.72 s for object detection, 1.1s for navigation, and 0.68 s for voice retrieval, with a frame rate of 22-25 FPS on GPU hardware. Performance testing was conducted under varying lighting conditions, partial occlusions, and different noise levels$(40-70 ~\text{dB})$to evaluate environmental robustness. The results confirm that the system maintains stable operation in both indoor and outdoor environments, demonstrating practical applicability and real-time responsiveness for assistive navigation in dynamic and crowded settings.

Financial fraud, particularly in credit card transactions, remains a critical issue for financial institutions due to the growing complexity of digital payment systems and cyberattacks. This study investigates the effectiveness of five machine learning algorithms-Logistic Regression, Naive Bayes, Random Forest, Light Gradient Boosting (LightGBM), and Extreme Gradient Boosting (XGBoost) and these were selected for their complementary strengths in interpretability, scalability, and ability to handle nonlinear relationships. A deep learning Autoencoder was also used as an anomaly detection baseline for fairness of comparison. The dataset was highly imbalanced (1% fraud), addressed using SMOTE resampling and MinMaxScaler normalization. Expanded feature analysis, including descriptive statistics, correlation patterns, and feature importance, revealed that transaction amount and merchant category were the strongest predictors of fraud. Experimental results show that Random Forest outperformed all other models, achieving 99% accuracy, 0.99 recall, and a perfect ROC-AUC of$\mathbf{1. 0 0}$. These findings demonstrate that ensemble-based methods are both accurate and stable for real-world fraud detection. Practical implications regarding model interpretability, deployment, and fairness are also discussed. The findings of this research have practical implications for financial institutions looking to implement automated fraud detection systems, ultimately improving their ability to minimize financial losses and enhance the security of digital payment systems.

Brain tumors, a significant health concern due to their potential to disrupt critical brain functions, require accurate and timely detection for effective management. Traditional diagnostic methods, reliant on manual examination of MRI scans by radiologists, often face challenges related to time constraints and susceptibility to human error. This study investigates the application of deep learning techniques to automate the detection and classification of brain tumors from MRI scans. We evaluated various neural network architectures, including CNN, VGG16, VGG19, and ResNet-50, to determine their effectiveness in identifying and classifying brain tumors from MRI images. Our results demonstrate that the CNN model outperforms VGG16, VGG19, and ResNet-50 in terms of both accuracy and generalization, making it the most effective choice for automated tumor detection. To address practical clinical needs, we developed a user-friendly web application that integrates the CNN model, enabling real-time tumor detection and classification. This application allows healthcare professionals to upload MRI images and receive immediate tumor detection and classification results, facilitating quicker and more precise diagnostic processes. The integration of deep learning models into this web-based platform marks notable progress in the automated detection of brain tumors. By enabling realtime analysis, the application supports clinicians in making informed decisions and planning treatment strategies with enhanced accuracy.

The global film industry faces significant challenges in making content accessible across diverse linguistic and cultural contexts, particularly through dubbing, which involves translating and synchronizing audio for different languages. Traditional dubbing methods, while effective, are labour-intensive, costly, and often struggle with preserving cultural nuances and emotional depth. With advancements in artificial intelligence (AI), there is a promising opportunity to address these challenges and revolutionize the dubbing process. This research explores the application of AI-driven technologies to improve the efficiency and accuracy of dubbing English-language films into Urdu. The research investigates the current state of AI-driven dubbing technologies, identifies their limitations, and proposes methods to enhance their effectiveness. Key objectives include analysing AI technologies for translation and speech synthesis, developing a streamlined process for English-to-Urdu dubbing, and evaluating the cultural and emotional fidelity of AI-generated content. A comprehensive methodology involving data extraction, speech-to-text conversion, translation, text-to-speech synthesis, and audio-video integration is employed to create an automated dubbing system. The system’s performance is assessed based on accuracy, naturalness, and synchronization, revealing both successes and areas for improvement. The findings demonstrate that AI-driven dubbing can significantly enhance the efficiency and scalability of the dubbing process, although challenges related to accent variability, cultural sensitivity, and voice naturalness remain. The study concludes with recommendations for future research, including the need for improved transcription accuracy, advanced translation models, and real-time dubbing capabilities. This research contributes valuable insights into the potential of AI to transform film localization and other sectors by making content more accessible and culturally relevant to diverse audiences.

This paper introduces Agri Sage, a mobile application aimed at addressing critical challenges in modern agriculture. Agri Sage integrates real-time plant disease detection using machine learning, an e-commerce platform for agricultural products, and weather updates alongside government pricing and subsidy information. Leveraging TensorFlow Lite for image analysis, the app diagnoses plant diseases even under suboptimal image conditions. The e-commerce platform allows farmers to connect with buyers and manage bulk transactions, while real-time weather and government data help farmers make informed decisions. This holistic approach enhances farm management, boosts productivity, and makes technology accessible to farmers in diverse environments. Extensive testing of Agri Sage demonstrated its efficacy in real-world applications, particularly in improving crop health and market access.

With the continuous advancement of technology and the need to keep pace with the digital era, the implementation of robust automated job recommendation systems has become essential to address the limitations of traditional methods and manual processes. This dissertation focuses on developing an online job search website application that integrates an automatic recommendation, alert and notification system, facilitating a more efficient connection between employers and job applicants using ML. Employers will have the ability to post job openings, review applicant profiles, and select the most qualified candidates. The user profile will be utilized to recommend jobs to candidates through a Semantic-Based Search System, with the Cosine Similarity technique serving as the key factor for automated job recommendations and comparing alongside the behavior of Jaccard similarity and the Jaccard similarity with subset matching. In other to understand what the best scenario for use for each is. The development of this job portal application aims to address the challenges faced by companies in filling vacancies and by job seekers in finding suitable employment opportunities.

The development of drowsiness detection applications has become increasingly important within the automotive technology sector, success rates are far from optimal. Despite the potential life-saving impact of such applications, the possible role of dynamic risk management in the successful completion of drowsiness detection projects has not been directly explored in existing studies on the topic. Considering the theoretical, as well as practical significance of the issue, the research under consideration aimed to analyze the benefits, challenges, and best practices of dynamic risk management in projects like “Drive Guard” a drowsiness detection Android application, with emphasis on the development of a framework for its implementation in such applications.

This research aims to develop machine learning (ML) models for the early identification of diabetes, a chronic condition that presents considerable global health threats, including heart disease, kidney failure, and neuropathy. Conventional diagnostic techniques, such as fasting plasma glucose (FPG) and HbA1c tests, tend to be invasive and often recognize the illness only in its advanced stages. To tackle this issue, the study investigates a range of ML algorithms, including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, AdaBoost, Naive Bayes, and XGBoost. It also incorporates comprehensive data preprocessing methods like feature scaling and Synthetic Minority Over-sampling (SMOTE). The goal of these initiatives is to improve the models’ performance and interpretability to facilitate earlier diagnosis. Among the models assessed, K-Nearest Neighbors (KNN) and AdaBoost stood out, both achieving an accuracy of 0.7727, with XGBoost following closely at $\mathbf{0 . 7 6 6 2}$. After tuning, Support Vector Machines (SVM) showed a significant improvement, reaching an accuracy of 0.85, while KNN achieved an accuracy of 0.87 on the test set. Although Decision Trees and Naive Bayes performed less effectively, this research highlights the necessity of model interpretability in clinical settings. Techniques such as SHapley Additive exPlanations (SHAP) are utilized to elucidate predictions. The implementation of these models on a web-based platform using flask framework which allows for real-time assessments of diabetes risk and insights into critical risk factors, aiding healthcare providers and patients in making better-informed decisions.

With thousands of passengers travelling through airports on a daily basis during air travel, it is particularly important to ensure that the lives of passengers and crew are safe. In modern airport security screening, it is vital to ensure the efficient and accurate detection of threats in passenger baggage, which will have a bearing on whether or not the safety of company and individual property can be effectively safeguarded. In addition, failure to effectively improve the accuracy and efficiency of threat item detection during airport security screening may waste security resources and increase passenger inconvenience, leading to anxiety and dissatisfaction during screening and affecting their travelling experience. Furthermore, if the detection technology is not effective in screening threatening items, airports may need to increase the number of security personnel to manually check baggage, which can significantly increase operational costs. This research therefore utilises YOLOv5 object detection technology to improve the efficiency of airport security systems. YOLOv5's real-time capabilities can quickly identify potential threats by detecting prohibited items in baggage X-ray images. However, the black-box nature of deep learning models such as YOLO often prevents the transparency required for critical security applications. To address this issue, LIME (Local Interpretable Model- agnostic Explanations) was integrated into the workflow to provide interpretable visualisations that shed light on the decision-making process of YOLO models. By applying LIME, we generated visual interpretations that highlighted key areas of the image that influence model predictions, leading to a deeper understanding of model behaviour and increased trust in the system. The experimental results show that the accuracy of detecting Seal increased to 0.64, indicating a significant improvement in the model's ability to detect this category. The accuracy rate reaches ${1 . 0 0}$ at a confidence level of 0.936, implying that almost all predictions are correct when the model's prediction confidence level exceeds 0.936.The YOLO-LIME combined framework not only maintains high detection accuracy, but also significantly improves the interpretability of the model's decisions. This dual approach allows security personnel to better understand and validate model outputs, ultimately contributing to the reliability and transparency of airport security processes.

The rapid spread of fake content across digital platforms, including text, and images, poses significant challenges to the integrity of information. While recent advancements in multimodal fake detection have shown promise, existing models often focus on a single modality or lack scalability and transferability to unseen events. This study addresses these limitations by developing a novel fake detection model based on the CLIP and BLIP architectures, designed to be scalable across text and image modalities. The proposed architecture makes use of a BLIP model to extract image-text similarity and a CLIP model to extract image and text embeddings. The hybrid fusion technique was used to fuse the features. The proposed model was evaluated against existing bimodal approaches, demonstrating superior performance with an accuracy of 90.63%, precision of 92.04%, recall of 90.73%, and an F1-score of 91.38%. These results highlight the model’s effectiveness in accurately detecting fake content across diverse modalities while maintaining a balanced performance between precision and recall. This research contributes to the advancement of multimodal fake detection by providing a scalable and comprehensive approach, paving the way for future developments in combating misinformation across various digital mediums.

Competition in the e-commerce system due to its rapid development forces companies to search for solutions to improve efficiency. The most important issue in this case is logistics operations, which determine the satisfaction of customer requirements. The emergence of intelligent technologies allows you to rebuild the system of delivery of online orders, making it more flexible and convenient. The article considers one of the ways to solve this problem. Using the example of building a two-link delivery system from an online store, it is shown that the proposed system will not only speed up the delivery process, but also reduce the load on the city’s transport network both in terms of traffic and from an environmental point of view. Ultimately, the proposed delivery option will contribute to the implementation of the ESG concept. The proposed system uses simulation models that have proven themselves well for solving such problems. Various options for courier deliveries of the “last mile” are recommended

Transport logistics in modern conditions includes more and more intelligent elements that improve both physical objects and optimize processes, using technologies of communication means, the Internet of things, and data analysis,. One of the processes that is implemented in the logistics chain is the loading and unloading process. The implementation of this process is especially relevant when delivering goods to shopping centers located in cities. To optimize loading and unloading processes, simulation models are used that allow not only to reduce the waiting time of trucks in the queue for unloading, but also to increase the efficiency of the entire process, as well as reduce the impact on the environment, which is extremely important in cities, and especially megacities. The article provides a practical example of using such a model, an optimization experiment was conducted and the achieved efficiency is shown.

The United States is the second largest producer of apples in the world with an estimated $21 billion downstream revenue. Since agriculture in the USA is highly mechanized, it is critical that latest advancements in technology are always integrated to the agricultural sector to not only improve efficiency but also improve quality, quantity, and to ensure faster distribution. Crop disease hampers the overall agricultural productivity and for a temperature-controlled crop like apple trees, identification of diseases at beginning stage is of paramount importance. There are two ways to identify and rectify issues relating to apple tree diseases, firstly by engaging expert biologists and secondly via automated identification through image processing. The biggest challenges with identification of diseases via biologist are accuracy, time constraints in case of bigger farms and budgetary limits. This research proposes the use of Machine Learning (ML) technique to aid and assist in automated disease detection and identification, and hence, making it affordable. It proposes the use of an ensemble (via weighted average) over single models, thereby improving performance and robustness by utilizing augmentations (positional and colour) which were not present in earlier studies. The proposed work surely creates an impact on the current plant disease diagnosis field by making the classification mode accurate and robust since it reaches accuracy of ~95% for all the classes.

Alzheimer's disease (AD) is neurodegenerative brain illness. It is classified as a degenerative illness since it worsens with time. A multitude of risk factors contribute to the development of Alzheimer's disease, such as demographic information, test scores, and genetics. The paper presents the comparison of machine learning algorithms to identify the highest accuracy level in classification of Late Onset Alzheimer's disease. Dataset from the Alzheimer's Disease Neuroimaging Initiative has been requested to train and test the machine learning models. The dataset included 539 normal controls and 411 Alzheimer's Disease individuals. A main dataset includes variables that are often used in clinical practice to develop the machine learning algorithms. Another dataset was created that exclusively included subjects aged 65 and up in order to assess the accuracy of algorithms used to diagnose late-onset Alzheimer's disease. According to the benchmarked findings, Linear Discriminant Analysis performed the most efficiently, achieving accuracy and an F1-score of 1.

The recent era of technology, Industry 4.0 finds its way to the field of manufacturing based on advanced technology such as artificial intelligence, internet of things, augmented reality, robotics, and more. These technologies can truly lead a massive transformation in production and manufacturing processes. This study aims to understand the extent to which undergraduate students in the field computer science and information technology in Iraq are aware of the techniques and applications of Industry 4.0. For this purpose, we have reviewed the literature as well as have collected opinions of students using a paper-based survey at the college of computer science and IT, University of Anbar. This can help in determining the level of students’ awareness of Industry 4.0 and to identify limitations, if any, when it comes to their educational journey. Data has been analyzed and results shown no differences between males and females when it comes to Industry 4.0 applications understanding. Beside this, students from different departments shown somewhat different sense regarding their knowledge of Industry 4.0 techniques and applications, which may be a big indication of the urgent need of curriculum revision and update.

Customer clustering is an unsupervised machine-learning approach that groups diverse customers based on shared characteristics. This research focuses on improving customer cluster analysis in the retail sector through an exploration of machine learning techniques, specifically employing the k-means model and predictive algorithms. An Ensemble approach is proposed to gain deeper insights into customer behavior and predict the future actions of new customers within the same cluster assignment. The study evaluates multiple machine learning techniques, utilizing K-fold cross-validation for enhanced model performance, with key metrics including accuracy, precision, recall, and F1-score. Notably, the Extreme Gradient Boosting Classifier excels in Dataset One, while Random Forest outperforms in Dataset Two. The project aims to combine these top-performing ensemble classifiers using the Voting-Soft Classifier for customer classification. The proposed model achieves high precision scores for both datasets, with a particularly promising precision score of 94.57% and an F1-score of 93% for Dataset Two, demonstrating its effectiveness in customer classification.

The purpose of the article is to outline the core concepts of a subject intension, molding and development, being also important to the intensification of AI-enhanced university education. Cognitive development clearly suggests that it is inseparably linked with speech intellectual activity and because of it is reality reflection as if refracted in the light of linguistic meaning. The developed program reflects the logic of the teacher's activities in the formation of a student's personal qualities [participating in subject-subject relations; participating in a joint, discursive practice on the part of all participants, which leads to the active transformation of the students' “Image of the World”; contribution to the development of self-regulation, that is, the capacity for independent problem solving and self-directed activity]. This program is intended by the authors for estimating the accomplished results with the use of any textbook and course in any subject on condition of changing test materials for such a factor attribute as the quality of knowledge in the subject. This teaching methodology is more flexible and means developing a sensitivity to students' current abilities.

Due to growing environmental concerns in relation to global warming and pollutants, it’s become very critical to study about sources which are contributing towards it. Once such source is emissions from the industrial equipment’s like Gas Turbine (GT). GTs are used all over the world across various industries for power generation or mechanical drive operations. Some of these industries belongs to Energy, Utilities, Refineries, Chemical & Fertilizer sectors. In this study, efforts were be made to compare all commonly used ML regression models such as MLR, DTR, RFT, Adaboost Regressor, GBR, XGBoost Regressor using same dataset for emission prediction (CO & NOx). Model’s evaluation metrices were analyzed to predict the best model along with number of significant features. Evaluation metric included MAE, RAE, MAPE & RMSE. In addition, various features were analyzed for patterns using univariate & multivariate analysis graphical tools. This study can contribute towards new PEMS (Prediction Emission Monitoring System) designing using ML Regression Models which will help industrial companies in boosting their operation efficiencies, minimization of emissions (like CO & NOx) and making financial gains due to its cost effectiveness over traditional CEMS (Continuous Emission Monitoring System). Overall, these new PEMS will keep monitoring and optimizing industrial equipment’s operations for emissions control during their lifecycles.

Daily retail sales are impacted by a lot of external factors, holidays and special events are one such category. In general, retail sales are largely impacted by fluctuations in demand hence, it is common for a retailer to run out of stock for some items and overstock the other items and this happens due to the lack of understanding of the actual number of items in demand for a particular item at a particular time of the month. This research work examines the impact of holidays or special events on the sales of a wide category of items using predictive analytics. It is done by performing exploratory data analysis and pre-processing methods followed by feature engineering and information extraction to extract the optimal input parameters to be fed into the model. The dataset is time-series data however, advanced machine learning algorithms are also used along with time-series methods to see if time-series data works well with non-time-series algorithms. Different time-series methods along with gradient boosting and the Facebook prophet model are evaluated in this work, achieving 92.83 % forecast accuracy with the Facebook prophet model. The gradient boosting model performs well with a MAPE value of 22.25% and time-series Holt Winters' additive method provides a MAPE value of 12.84 %. Each of the algorithms provides a good score with this time-series data and an appropriate algorithm can be chosen as per the business need.

Phishing website detection is the task of classifying websites as phishing or legitimate based on URL parameters and certain behaviour of the site. In today's world, dependency on websites has become inevitable. With the increase in website users population and the rise of the internet, cyber-attacks have become a common thing. Attackers across the globe target innocent users to steal their personal classified information such as login credentials, credit or debit card information, which may lead to serious monetary and identity damage for the users. One of the main challenges with this problem is the constant change in phishing URLs. Due to this, there is a constant need to update the detection mechanism, which may be extinct in a short period of time. Most of the current phishing detection tools utilise the black box method, where phishing URLs are stored and queried for verification. This may not be an efficient way due to the constant change in the URLs. In this study, a machine learning based approach is proposed along with a feature selection method to select the right set of features that may contribute to higher detection accuracy. The proposed model is also aimed at being simple, faster, and interpretable. Efficiency, accuracy, and model execution time will be evaluated against the final model.

Plant diseases pose a significant threat to global food production and require early identification to ensure global food security and safeguard the economic interests of agriculture stakeholders. Practical implementation in agriculture requires fast, lightweight and accurate models for edge devices. This research presents a tomato leaf disease classification approach using a fast and lightweight pre-trained CNN architecture designed for efficiency. The method incorporates resampling techniques to address the data class imbalance, while pre-processing steps such as resizing, and augmentation enhance model performance. The transfer learning-based NASNetMobile model is trained and evaluated using the plant village dataset, containing nine disease classes and a healthy class. Performance assessment focuses on lightweight characteristics and classification accuracy of the model. Results demonstrate the effectiveness of the approach in balancing lightweight characteristics and accuracy, offering the potential for extending disease classification to other plant species. This research contributes to the development of low-end devices for easy disease identification, benefiting global food security and agricultural stakeholders’ economic well-being.

Airbnb is known to be a home-sharing and rental platform which provides facilities to homeowners or renters (referred to as hosts) to offer their houses otherwise known as listings on an online platform for guests booking. It is the responsibility of the hosts to set the expected price of their items independently. Although Airbnb along with a few sites provides many advices, we are yet to have any free or accurate system. This became difficult for the hosts to correctly come up with a price for their listed properties due to many different factors in the system. There are a few pricing models available in the market, however, these are not free. It is the responsibility of the host to enter the appropriate basic price for each night for a particular property. The other challenge is dynamic pricing based on holidays, seasons, and weather. The host can't keep the same price for all the dates as this impacts the business significantly. It is extremely critical to ensure appropriate prices are listed during this competitive time. This study compares the performance of numerous machine learning algorithms and methodologies in Airbnb price prediction to identify the most accurate one. Linear Regression, XGBoost, Random Forest, ANN and KNN are among the machine learning models experimented in this study. Different performance measures are used to validate the results.

This research proposes a computer vision-based solutions to identify whether a patient is covid19/normal/Pneumonia infected with comparable or better state-of-the-art accuracy. Proposed solution is based on deep learning technique CNN (Convolutional Neural networks) with multiple approaches to cover all open issues. First approach is based on CNN models based on pre-trained models; second approach is to create CNN model from scratch. Experimentation and evaluation of multiple approaches helps in covering all open points and gaps left unattended in related work performed to solve this problem. Based on the experimentation results of both the approaches and study of related work done by other researchers, Both the approaches are equally effective can be recommended for multi-class classification of lung disease.

Home Loan plays a pivotal role in today's age when one steps into purchasing their home. It has been witnessed that in many cases users are unable to pay the after taking the loan and thus the loan is slipped to NPA(Non-Performing Asset) from Standard Asset for the bank or any lending institution. The revenue generation is ceased. As the housing loan is taken against property the lenders have right to sell the property and close the dues, but the process is lengthy as judicial procedures are involved. In most cases, the property value is much less than the calculated loan amount (Principal + Interest). In this study we examined the several ML methods to identify the loan default before disbursing the loan to the applicant. This matter has been studied widely and used the predictive analytics to find out the relationship between attributes and the target variable. Predictive Analytics enables us to feed optimal set of features to the ML models. The study started with 122 attributes and ended up with around 30% of features as the ideal subset for housing loan default prediction. Then, five ML models were fit into the dataset and the champion model came up with roc score 0.94, Recall 0.90 and Precision 0.94. LIME and SHAP were applied on the champion model along with the dataset for global and local interpretability. The experimental procedure concluded that ML models along with predictive analytics can arrest the loan disbursal to the ineligible applicants and will also provide the insight of such prediction with the help of model interpretability.

Sign Language Recognition is a form of action recognition problem. The purpose of such a system is to automatically translate sign words from one language to another. While much work has been done in the SLR domain, it is a broad area of study and numerous areas still need research attention. The work that we present in this paper aims to investigate the suitability of deep learning approaches in recognizing and classifying words from video frames in different sign languages. We consider three sign languages, namely Indian Sign Language, American Sign Language, and Turkish Sign Language. Our methodology employs five different deep learning models with increasing complexities. They are a shallow four-layer Convolutional Neural Network, a basic VGG16 model, a VGG16 model with Attention Mechanism, a VGG16 model with Transformer Encoder and Gated Recurrent Units-based Decoder, and an Inflated 3D model with the same. We trained and tested the models to recognize and classify words from videos in three different sign language datasets. From our experiment, we found that the performance of the models relates quite closely to the model's complexity with the Inflated 3D model performing the best. Furthermore, we also found that all models find it more difficult to recognize words in the American Sign Language dataset than the others.

The article outlines the main advantages of using modeling to optimize parking space. The site of the urban road network of the 10th complex of the Profit Group residential area in the city of Naberezhnye Chelny was selected as the object of study. A simulation model of the open parking space of this site, as well as a closed parking lot, was built. During the computer experiment, an estimate of the time spent on parking, as well as the required volume of parking space, was obtained. The novelty of the proposed approach in the article is that using the developed simulation model it is also possible to assess the degree of environmental load on the territory.

The strategy of spatial development of the country for the period up to 2024, based on the infrastructure of a specific type of transport, provides for the connection of the territories of settlements with modern communications; phased reconstruction and modernization; personnel, technical and technological support for interaction and digital transformation of the Russian transport complex. The development of the Russian Arctic is a priority area, since it has a significant natural resource, socio-economic and transport potential, which must be maximized, taking into account all the features of this region. It is important to develop the northern territories in such a way that the transport infrastructure of the Arctic meets the requirements in the field of comfort and safety of the human environment. This can be achieved through the use of vehicles of increased environmental friendliness and energy efficiency. In the study we analyse the possibility of converting shift buses used to deliver personnel involved in the development of fields in the Arctic zone to gas motor fuel using simulation models.

When managing the transport system of an urbanized area, infrastructural changes cannot always solve transport problems. At the same time, organizational measures implemented within the framework of an intelligent transport system can be effective. To make operational and strategic decisions, it is necessary to form a base of typical emergency situations, having previously studied them on a simulation model. For this, we have chosen a micro-simulation method, which allows taking into account the stochastic nature of the traffic flow. As a result of a computer experiment, we have obtained estimates of changes in parameters (average time for a vehicle to cross an intersection in all directions, average speed) when emergencies of a given duration occur at a T-shaped intersection. The novelty of the proposed approach lies in the possibility of assessing the nature of the emergency situations' development for various values of influencing factors.

However, energy forecasting is still a challenging task due to the many unpredictable factors that can impact energy consumption and production, such as changes in weather patterns, economic conditions, and energy policies. Therefore, energy forecasts should be continuously updated and refined as new information becomes available. The purpose of this research is to present a high-level, machine learning-centric viewpoint on data-driven energy forecasting. Challenges and constraints of data-driven energy forecasting will be discussed, along with the many machine learning methods and methodologies that can be implemented. The paper compares the performance of various deep learning and time series analysis techniques such as LSTM, RNN, ARIMA and SARIMA in energy forecasting. This research can provide a more comprehensive understanding of the effectiveness of different models in energy forecasting, which can have significant implications for energy management, policymaking, and infrastructure development.

In today’s job market, finding a suitable job is a complex task that requires innovation to ease the complexity of finding the job. As technology shapes the business landscape, a deeper understanding of the Advance Natural Language Processing (NLP) and using it properly will elevate the solution of finding the match between candidates and employers. In this research, comparative approach between GPT-2 and T5 model has been done to find out the best model for the job matching chatbot. Also, in this chatbot multiple criteria decision making method has been used to find the best job related to the user’s requirement. The research novelty is a chatbot that works based on the Transformer-based Text-to-Text Transfer Transformer (T5) model and compare it to GPT-2 to address the challenges of finding the best job based on job seekers’ preferences and also compare both generated answers to realise the accuracy of each model in job matching chatbot. GPT-2 and T5 both are excel in natural language understanding tasks, enabling us to parse and map user queries to many job preferences, ensuring a comprehensive understanding of user skills, and also they can provide a high rate of accuracy and performance in the natural language tasks. The research’s contribution focuses on preference job matching chatbot application, which effectively bridges the gap between employers and job seekers. Using context-based meanings of specific words and new terms defined in the conversation, the model generates responses based on user input. A seamless connection between job seekers and potential employers is made possible by our approach to Human Resources technology, which serves as a more personalised, effective, and user-friendly job matching system. The model tokenises words and generates test cases based on them. By utilising NLP techniques, it will help to ensure that all scenarios are taken into account.

In this study, we explore deep learning models for single-character Chinese character recognition tasks, with a special focus on two architectures: VGG19 and EfficientNetV2. By improving recognition accuracy with limited computational resources and dataset size, this study aims to address real-world challenges.In the field of image recognition, deep learning has shown excellent ability, especially the importance of convolutional Neural Network (CNN). This study reviews the history of handwritten Chinese character recognition and explores the application of deep learning in various fields such as object detection, image classification, and semantic segmentation. The research methodology incorporates problem analysis, data collection, model construction, training, and result generation. PyTorch is used as the basic framework to implement the model, and strict data preprocessing is conducted to optimise the performance. The user interface and interaction design allow us to show the practical application of the model and encourage user-friendly participation. By applying VGG19 and EfficientNetV2 models in a single-character Chinese character recognition task, we reveal the impact of limited training data and computational constraints on accuracy and performance. We confirm that higher training cycles improve accuracy, but we also note diminishing returns. Meanwhile, the research highlights the exciting potential of deep learning in character recognition tasks and advocates its widespread application in practice. While overcoming computational and data limitations, our study reveals the intricate relationship between model training, accuracy improvement, and practical usability. The experimental results show that the average training result of the EfficientNetV2 model is 94.957322%, and the average training result of the VGG19 model is 95.756285%. This study provides dedicated support for the in-depth research and development of Chinese character recognition and its various application fields.

Stock price forecasting has always been a challenging task due to its high volatility and complexity. Recently, various machine learning models have been employed to improve the accuracy of stock price predictions. This research aims to enhance stock price forecasting by integrating supply chain factors into LSTM models and conducting a comparative performance analysis with other models such as ANN, RNN, and GRU. A comprehensive literature review was conducted to understand the history of machine learning in finance, the role of different models in stock market prediction, the impact of varied factors in stock market prediction, and the challenges and limitations of artificial intelligence in stock forecasting. The methodology involved problem analysis, defining the research purpose, and project design which includes data collection, model building and training, and performance evaluation and validation. The models were trained and assessed on a dataset that incorporated supply chain factors. The experimental results showed that the GRU model outperformed the other models in terms of R2 score, MAE, and MSE. The study contributes to the existing body of knowledge by providing empirical evidence on the importance of incorporating supply chain factors into predictive models and by comparing the performance of different models. The findings have practical implications for investors, analysts, and policymakers who rely on accurate stock price predictions for decision-making.

Alzheimer's disease (AD) is a type of brain disorder that is regarded as a degenerative disease because the corresponding symptoms aggravate with the time progression. Single nucleotide polymorphisms (SNPs) have been identified as relevant biomarkers for this condition. This study aims to identify SNPs biomarkers associated with the AD in order to perform a reliable classification of AD. In contrast to existing related works, we utilize deep transfer learning with varying experimental analysis for reliable classification of AD. For this purpose, the convolutional neural networks (CNN) are firstly trained over the genome-wide association studies (GWAS) dataset requested from the AD neuroimaging initiative. We then employ the deep transfer learning for further training of our CNN (as base model) over a different AD GWAS dataset, to extract the final set of features. The extracted features are then fed into Support Vector Machine for classification of AD. Detailed experiments are performed using multiple datasets and varying experimental configurations. The statistical outcomes indicate an accuracy of 89% which is a significant improvement when benchmarked with existing related works.

Alzheimer's disease is one of the brain disorders. It's also characterized as a degenerative disease because it becomes worse over time. Apolipoprotein E (APOE) is a genetic risk factor for Alzheimer's disease that has been linked to the disease in several genome-wide association studies (GWAS). Single nucleotide polymorphisms are the most common type of genetic variation among individuals (SNPs). SNPs have been identified as important biomarkers for this condition. SNPs aid in the study and detection of the disease in its early stages. We focus on employing a stacked Machine Learning (ML) model to categories Alzheimer's patients in this paper. The model was tested on all AD genetic data from phase 1 of the neuroimaging project (ADNI-1). The results showed that the stacked model outperformed other machine learning methods with an overall accuracy of 93.7 percent. The findings suggest that stacking approaches are effective in detecting Alzheimer's disease.

The clinical note documents and diagnosis of disease have always been a real issue in both developed and developing countries. Therefore, the electronic health record technology has emerged worldwide to improve the quality of clinical notes. Moreover, to simplify disease diagnosis, nowadays, doctors opt for the Artificial Intelligence (AI). Alongside this progress, the present study aims to create a web based electronic health record integrating the AI to diagnose Diabetes Type 2 disease. In doing so, MERN stack (Nodejs /react, Express JS and Mango DB) software were used to develop the health record. It is worth noting that Pima Indian diabetes data set was adopted to construct the logistic regression model for the AI part.

This paper presents an online crime analytics (Crime Geo Analytics Tool). The purpose is to facilitate the use of information technology to enable the general public to access data regarding crime in an geographical location, both online and interactively. Crime Geo Analytics Tool is a web-based system that informs stakeholders about criminal offence rates. To the best of our knowledge, there is no such online system in Pakistan, which helps people to get crime statistics, visualizations, and report on crimes. The system can also perform predictions and suggestions for users.

Artificial intelligence, and especially Artificial Neural Networks (ANN), has gain a monumental growth and interest of healthcare providers to improve medical care while reduce cost. Applications of ANN for classification and prediction are well-established on numerous aspects of real-world applications. One of these many aspects is to improve healthcare delivery through influencing healthcare provider decisions. This study provides a systematic review of the applications of ANN to medical applications. We have screened 87 articles from several academic databases with coverage our cross-disciplinary query to identify matches in the literature based on the combinations following keywords; Artificial Neural Networks, Medicine, Healthcare, and Applications. Our systematic review process involves searching evidence from different sections while focusing on eligibility, rationale, objectives, evaluation and limitations. Reviewed studies have targeted the use of different ANN used including multilayer perceptron, convolutional and recurrent neural networks, along with deep learning approaches. Most of these studies informed classification of diseases or decision-making process. The commonest ANN architecture in this review was found to be the multilayer perceptron within a feed forward learning approach. Interpreting ANN final models was found to be the main challenge with medical applications.

The use of technology and Artificial Intelligence is becoming much more apparent in our lives, especially, in the many working environments that there are; FinTech, Medicine, Education, and more recently, Legal Tech. The paper provides the background for the use of AI in Law, explores the experience of existing applications. Based on the requirements gathered during the interviews and the observations made from the case study the decision support system waa developed that is injected with intelligence through the inclusion of a framework, which facilitates the data science element and in turn assists legal professionals with decision making, risk management and carrying out cumbersome legal research tasks.

The article covers the methods of Deep Learning for education data mining (EDM) and learning analytics (LA). The authors have reviewed the researches on EDM and LA that have employed the classical approaches of artificial neural networks (ANNS). The authors come to conclusion that LA and EDM present insufficient volume of research on the application of a new and more profound technology of artificial neural networks - Deep Learning. The new methods are described and the research on LA and EDM that employs Deep Learning is studied.

Successful transformation of government public transactions to digital services depends on having a precisely thought national strategy which is informed by a context-aware information and communication technologies (ICTs). This study examines the level ICTs awareness of public sector employees and the extent to which they can support the digital transformation of government public transactions. An online questionnaire was adopted to gather data from employees of Iraq public sector employees. According to the results, employee participants were believed they have proper ICTs skills to support the digital shift of government services. The findings also indicate that employee participants were enthusiasts to the digital services, whereas they are less satisfied with the government national policy as well as the allocated financial resources to implement digital government principles.

Ground-level ozone concentration is one of the main concerns for air pollution, due to the negative impacts on human health, animals, foliage, climate and the whole ecosystem. The aim of this paper is to reduce the influential outliers by including weightages within robust method to avoid the bias of the model. The influential outliers from x-space (predictors) have been identified using leverage values. Furthermore, Cook's distance and standardized residual have been computed to clarify the influential outliers from both of x-space and y-direction. S-estimation and MM-estimation have been introduced as a new approach for reducing the influential outliers from x-space and both of y-direction and x-space respectively. The comparison between the robust method and the ordinary least square method shows that, the accuracy measures of the robust method have been improved by around 0.94% (D+1), 0.56% (D+2) and 1.85% (D+3) respectively.

Intracranial pressure (ICP) could be an indicator of a neurological disorder known as hydrocephalus, which is currently managed by shunting procedure. This paper investigates the current advances of shunting valves and provides an overview of ICP readings interpretation from a medical point of view with reference to Alder Hey hospital in Liverpool, UK. Moreover, this paper helps to express ICP readings using advanced data science approach and prepares for implementing intelligent approaches as an alternative pathway to improve the use of ICP within the current medical system. It is assumed that this paper would help specialists and non-specialists in an informative way to comprehend ICP readings. It also allows combining ICP reading with other parameters to derive a proper action with respect to patients with hydrocephalus.

This article describes the use of various Learning Analytics (LA) tools in the Learning Management System (LMS). Using the LA tools consists of four steps: collecting data and choosing the appropriate LA methods, choosing the LA tools, testing the LA tools, and evaluating the results. The main conclusions of this article are that LA tools are still too dependent on the data format and the LMS version, and that teachers find using this type of tool useful, but still difficult toutilize.

Data science is the process of liberating meaning from raw data using scientific methods and algorithms, and is becoming much more commonly used in healthcare with the emergence of personalised healthcare. Alzheimer's disease (AD) is a neurodegenerative disease that has no proven curative treatment, however a new treatment protocol, ReCODE, has been proposed to slow and reverse the progression of the disease. In this paper, an overview of AD is provided, followed by a description of the ReCODE protocol, including the new proposed methods and data to be used in prediction diagnosis and treatment. The ways in which data science can help with prediction and diagnosis are then reviewed, along with the data science techniques that can help with each treatment in the protocol. It is concluded that current data science techniques are useful in aiding the successful treatment of AD patients with he ReCODE protocol, and though there is much promise to the use of data science techniques to predict and diagnose AD, no such technique yet exists that can process all the necessary data. Future research should be conducted to develop such a data science technique. Further research should also be conducted to improve current data science techniques used to support the treatment of AD.

The article is an attempt to introduce bilingual educational programs using the example of California (the USA) in the context of protection of linguistic diversity and the ability to use the native or first language under conditions of globalization and the challenges of Industry 4.0 and Society 5.0. The analysis of the main documents regulating the introduction of multiculturalism and linguistic pluralism in the context of bilingual education introduced by the legislative initiative of California Department of Education are drawn. The paper also studies programs and initiatives implemented by educational organizations of California Association of Bilingual Education (CABE), Californians Together and others. Information educational web sides and educational Internet resources supporting bilingual education in the studied region are analyzed.

In the period of active globalization processes, which cover the sphere of mass media, the development of regional languages from the point of view of sociolinguistics has become a particularly topical issue. The object of linguists' research is the functional potential of regional languages, the ways of their preserving and promoting, as well as the ways of language policies of multinational states. The article deals with the issue of regional languages functioning in the field of mass media. The aim of the research is analysis of the quantitative parameters of regional languages, which will present the functional position of regional languages in the media space, as well as to study and predict the development of the functional potential of regional languages through the mass media. The paper presents the results of the survey carried out to understand the social positioning of the role of regional languages in mass media.

Hydrocephalus is currently managed using traditional mechanical shunts. A smart patient monitoring and shunting system is needed for both patient follow-up and drainage of the cerebrospinal fluid. eHealth is a current and necessary trend for better management of chronic-diseases such as hydrocephalus. This paper demonstrates the analysis of questionnaire data to test the user's acceptance of healthcare technology. The paper also presents a concept for a smart shunting system in-terms of the hardware required for such system to function. The valve mechanism is put under focus as it is the most crucial component of this system.

The process of cement manufacturing produces a huge amount of carbon dioxide (CO2). The utilization of alternative waste materials from various industrial processes as a partial substitution to cement is encouraged due to environmental and specific technical requirements. This strategy will have the potential to reduce cost of cement, conserve energy, and reduce waste volumes. Therefore, the aim of this research is to investigate effect of the replacement of cement with modified fly ash (MFA) and ground granulated blast furnace slag (GGBS) to reach 80% total replacement on mechanical and durability performance of cement mortar. Normal consistency, the initial and final setting times, compressive strength and electrical resistivity of all the ternary mixtures were determined and compared with the control binder. Compressive strength and electrical resistivity were tested at various curing ages of 3, 7, 14, and 28 days. Test results revealed that the normal consistency of the ternary mixtures increased with increasing the GGBS and MFA content, while the initial and final setting time decreased compared to that of control mixture. The results also showed that the compressive strength of all the ternary blends mortars were lower at early and later ages in comparison with control mortar. The reductions in the compressive strengths of the ternary mixtures T40, T60 and T80 compared to the control mixture were approximately 16%, 29% and 37%, respectively at 28 days. The surface electrical resistivity of ternary blends mixtures was higher than the control mixture at all curing ages. The use of GGBS and MFA in the production of cement mortar and concrete can significantly help in reducing the CO2 emissions of the cement industry and reduce the overall cost of cement.

Industry 4.0 is a term frequently used to describe the new upcoming industry era. Higher education institutions aim to prepare students to fulfill the future industry needs. Advancement of the digital technology has paved the way for evolution of education and technology. Evolution of education has proven its conservative nature and a high level of resistance to changes and transformation. The gap between the industry's needs and competencies offered generally by education is revealing the increasing need to find new educational models to face the future. The aim of this study was to identify the main issues faced by both universities and students in preparing future workforce. From December 2018 to April 2019, a regional qualitative study was undertaken in Liverpool, United Kingdom (UK). Interviews were conducted with faculty members and undergraduate students, and the results were analyzed using the open coding method. Four main issues had been identified which are the characteristics of future workforce, students' readiness to work, expectations on different roles played at the tertiary education level and awareness of the latest trends. In conclusion, this technology era requires the employers, academic practitioners and students to work together in order to face the upcoming challenges and fast changing technologies. We suggest that an interactive system should be provided as a platform for these three different parties to play their roles.

Chronic Kidney Disease is a serious lifelong condition that induced by either kidney pathology or reduced kidney functions. Early prediction and proper treatments can possibly stop, or slow the progression of this chronic disease to end-stage, where dialysis or kidney transplantation is the only way to save patient's life. In this study, we examine the ability of several machine-learning methods for early prediction of Chronic Kidney Disease. This matter has been studied widely; however, we are supporting our methodology by the use of predictive analytics, in which we examine the relationship in between data parameters as well as with the target class attribute. Predictive analytics enables us to introduce the optimal subset of parameters to feed machine learning to build a set of predictive models. This study starts with 24 parameters in addition to the class attribute, and ends up by 30 % of them as ideal sub set to predict Chronic Kidney Disease. A total of 4 machine learning based classifiers have been evaluated within a supervised learning setting, achieving highest performance outcomes of AUC 0.995, sensitivity 0.9897, and specificity 1. The experimental procedure concludes that advances in machine learning, with assist of predictive analytics, represent a promising setting by which to recognize intelligent solutions, which in turn prove the ability of predication in the kidney disease domain and beyond.

The initial monitoring of patients with headache is an essential part of ongoing patient safety. Usually, patients are asked to fill in traditional paper-based diaries or outcome measures (e.g., HIT-6 and MIDAS) on a regular basis to measure the impact of headache on a patient's life. However, within publicly funded health care systems such as the UK's National Health Service (NHS), long term monitoring in neurology clinics appears not to be possible for all patients with chronic headache due to the continued decline in funding over the past decade. Nowadays, there is scope to improve patient monitoring and safety in the headache clinic by employing mobile health (mhealth) technologies. The M-health application represents an intelligent solution and holds potential to allow specialists to monitor a larger number of patients than would be possible within the current service model. Mobile applications could replace traditional paper-based diaries and outcome measures and provide several advantages including improved monitoring of historical responses to therapies, improved recording of side effects and can be adapted to improve communication between patients and clinicians. We therefore developed a mobile application-based system to allow remote monitoring of patients with chronic headache.

The rate at which people miss hospital appointments has decreased but remains a big concern for health care professionals as well as funding agencies. This research paper used an open data obtained from the NHS database to determine the factors that may lead to missed appointments and create a model that can be used to predict the likelihood of a patient missing an appointment. Logistic regression models and bivariate analysis were used to determine whether there was a meaningful relationship/association between "did not attend" and forgetfulness, gender, apathy, and transportation. An extensive literature review was conducted to narrow down the reasons that might lead to missed appointments. In conclusion, the research showed there was a significant difference between gender, type of clinic and apathy in organizations.

The contextual compendium analysis presented in this paper focuses on the Industry 4.0 and healthcare services innovation that relate to it. The appraisal discerns the specific components of Industry 4.0 and their related innovations or contribution in the healthcare industry. The first component, Cyber-physical systems, has led to Medical Cyber-physical systems applied in different circumstance to improve the efficiency of service provision. The second component, Internet of Things, has brought with it expanded networks, biosensors, smart pharmaceuticals, and other artificial organs. The final component has inspired the integrated of Natural Language Processing model as a calm-system operating in the background to complete a host of the process that improves diagnoses among other service provision and assistance functions. Additionally, the paper discusses Cognitive Computing, mHealth, and eHealth as emerging medical fields that can benefit from Industry 4.0.

Education With the inclusion and integration of internet and digital learning Education 2. 0 brought tools in the different context of education. The use of social networking concepts such as chat rooms and the ever-growing student data have placed education on the brink of becoming one of the craters and users of Big Data. As such, this paper explores educational data mining techniques alongside some of the emerging learning analytics with the objective of gaining insight into some of the common learning behaviors among students. The task at hand embraces predictive analytics and it employs decision trees, neural networks, and Naïve Bayes algorithms to classify and cluster student learning patterns that can explain academic performance. Predictive analytics has emerged as one of the tools furthering adaptive learning among other lifechanging novelties. Nonetheless, integration of big data in academia is in its infancy although the western hemisphere is making progress towards the integration. Such progress will increase the relevance of data mining in education and this paper envisages to be among the first ones to address the applicability of machine learning in improving education. Hence, the objective of this paper is to develop predictive models based on the decision tree, neural network, and Naïve Bayes algorithms.

This article considers how teachers and university administrators can use a significant amount of data stored in the information systems of institutions. Intelligent analysis of these learning processes is of great use in the higher education system. The use of learning analytics (LA) by a large number of higher educational institutions shows the interest and participation of universities in this matter. Learning analytics can tell a lot about the progress of students and the environment in which learning takes place. Intellectualization of educational analytics will help provide predictive models that can serve as a basis for quality assurance and quality improvement. This article gives an idea of the current level of LA development at the international level. The article also draws conclusions about the problems and limitations associated with learning analytics (LA). The existing experience has been studied, and the conclusion have been made about the existing limitations that prevent the wider use of LA.

The present level of the technology development gives various opportunities to implement methods never used or seen before. Different solutions emerge, and one of the most critical goals for the scientific society is to establish a strong link of the new technology with the methods used traditionally in particular areas. In this article we would like to discuss how two relatively new approaches to education process, Learning Analytics (LA) and Serious Games (SG), can interrelate between each other with reference to the classroom implementation. We will review the role of LA in the context of SG and vice versa and discuss possible and actual limitation that could confine the positive results of such interrelation.

the purpose of the article is to outline the core concept of a social-interactive learning. Electronic educational resource provides the opportunity of permanent on-line contact with a teacher that evidently contributes to the development of 'legitimate peripheral participation'. Attention is drawn to the fact that we would see a highly interactive classroom, where guided participation allows students to obtain the necessary language skills for a shorter period of time. Attempts are made to snoop the students, learning curve.

Current efforts move to improve healthcare services in Saudi Arabia (SA). Given the essential roles of these services within the country, ongoing effort to improve health informatics services in SA is required in order to ensure the sector is up-to-date. One area for improvement focuses on the referral system in place in Madinah city. This paper presents a solution to the above problem by proposing a Web-based healthcare information system link that can connect four healthcare centres in Madinah city. This will serve as a pilot project. This link could help healthcare providers to access patient information within their own system. Many healthcare applications have problems with interoperability. Hence, one of the aims of this research is to look at system integration of some of the existing healthcare information systems.

This article focuses on the functionality of the Tatar language in cyberspace. Tatar is a regional co-official language of the Republic of Tatarstan, second only to Russian in the term of the number of speakers in the Russian Federation. The aim of this work is to identify the strengths and weaknesses of Tatar on the Internet: a key challenge that Tatar is currently facing. Regional languages are becoming increasingly vulnerable in the context of the globalised world, making sociolinguistic analysis of their functional power, potential perspectives and threats extremely important. The paper provides an overview of the readiness of applied tools and software to enable the Tatar language to function in cyberspace. The research results can be useful for improving the language policy and language development processes in Tatarstan and Russia in general. Tatarstan's experience in supporting and developing its title language can benefit other multinational states and regions in managing language functionality.

The article provides a comparative analysis of the resources of distance learning of regional languages. It presents the overview of online learning websites for the Tatar, Bashkir, Udmurt and Chuvash languages - regional co-official languages of the Russian Federation. The main factors determining the level of representation of a particular language in the field of Internet technologies are identified. Results of the analysis allow to improve the set of measures within the regional language policy to improve the functionality of Russian regional languages.

The article presents the analysis of gamification software for the minority languages promotion. Gamification is a concept of using videogame aspects that invoke pupil's motivation and interest in the educational software. The aim of the paper is to provide an overview on the current situation with the modern software that can be used to teach regional or minority languages. Gamification software can be considered as a pioneering means ad hoc. The results of this analysis provide information that will allow to conclude, whether development of new type of gamification software for regional and minority languages promotion is necessary.

The risk assessment of any Network or Security systems has a high level of uncertainties because usually probability and statistics were used to evaluate the security of different cyber security systems. In this paper we will use Shannon entropy to represent the uncertainty of information used to calculate systems risk and entropy weight method since the weight of the object index is normally used and point to the significant components of the index. We evaluate the risk of security systems in terms of different security layers and protections. The information system is analysed by perimeter, network, host, application and data layers' protections. The capability of protections is measured by introducing the concept of protection effectiveness. We write the security evaluations algorithm to normalized the protection matrix and calculate the entropy and the entropy weight, then we will use the weight and paths to evaluate and calculate the total risk in the system and give the systems administrator a clear guidance on the vulnerable security entities. We try to develop a novel approach to evaluate the cyber security suitable for the majority of cyber systems by introducing the term of security entities.