Accurate prediction of calories burned during physical activities is crucial for various applications in health monitoring, fitness tracking, and personalized nutrition. Traditional methods often lack the precision needed for individualized estimates, which has increased interest in advanced machine learning approaches. This research introduces a deep learning model designed to predict calories burned with enhanced accuracy by capturing complex, non-linear relationships in the data. The model employs a multilayer perceptron neural network, Leaky ReLU activations, dropout regularization, and the Adam optimizer to improve generalizability and prevent overfitting. The evaluation of training and validation loss over epochs demonstrated the model's robustness and capacity to generalize effectively to novel data. The model's performance was evaluated using various metrics, achieving superior results with a remarkable Mean Absolute Error (MAE) of 0.27% and an accuracy of 99.73%, outperforming other models discussed in the literature. These findings indicate that deep learning offers significant potential for improving calorie prediction models, providing more reliable fitness and health management tools.

Detecting spam emails is essential to maintaining the security and integrity of email communication. Existing research has made significant progress in developing effective spam detection models, but challenges remain in improving classification performance and adaptability to evolving spamming techniques. In this study, we propose a novel spam detection model with a comprehensive feature engineering approach that combines term frequency-inverse document frequency (TF-IDF) vectorizer and word embedding features to optimize the feature space. Our contribution lies in integrating semantic-based word embeddings, leveraging pre-existing knowledge to capture the semantic meaning of words and enhance the representation of email texts. To identify the most suitable word embedding technique for our model, we evaluated GloVe, Word2Vec, and FastText. GloVe was selected for its better performance, which is the result of its pre-training on a large and diverse text corpus. Furthermore, the model was evaluated without word embeddings, which did not exhibit the same effectiveness level as our word embedding-based model. Additionally, we utilized the support vector machine as a classifier and hyperparameter tuning technique to identify our model’s most effective parameter values. The proposed model was tested on two datasets. The experimental results showed that our model outperformed the other models discussed in the literature, achieving an accuracy of 99.5% on the SpamAssassin dataset, and 99.28% on the Enron-Spam dataset.

After the establishment of the Semantic Web concept by Tim Berners-Lee in 2001, there has been a lot of work and study to understand the goals of the Semantic Web. According to initial perceptions and strategy declarations, the Semantic Web will perform as an addition to the present Web, which is largely understandable by humans. Semantic Web is also called Web 3.0, Which enables World Wide Web information, databases, and other structured resources to be presented in a consistent structure that other applications can use. The review paper begins by examining the nature of the Semantic Web and the difference between the traditional Web and the Semantic Web. Moreover, the paper includes a brief overview of the Semantic Web and its technologies and discusses seven of the domains that the Semantic Web has been used. In addition, the paper describes the position of the Semantic Web nowadays. As a result, a researcher who is not familiar with the Semantic Web should find this paper a beneficial tutorial.

The field of finance makes extensive use of real-time prediction of stock price tools, which are instruments that are put to use in the process of creating predictions. In this article, we attempt to predict the price of Bitcoin in a manner that is both accurate and reliable. Deep learning models, as opposed to more traditional methods, are used to manage enormous volumes of data and to generate predictions. The purpose of this research is to develop a method for predicting stock prices using the Hybrid Convolutional Recurrent Model (HCRM) architecture. This model architecture integrates the advantages of two separate deep learning models: The 1-Dimensional-Convolusional Neural Network (1D-CNN) and the Long-Short Term Memory (LSTM). The 1D-CNN is responsible for the feature extraction, while the LSTM is in charge of the temporal regression. The developed 1D-CNN-LSTM model has an outstanding performance in predicting stock values.

The management of the financial budget of universities is an extremely complex task due to having numerous different documents and calculation processes. In many developing regions and countries including the Kurdistan Region of Iraq, budget execution and accounting processes are manual. This had deleterious effects on the functioning of their expenditure and income management. This research represents the design and implementation of a responsive web-based system for controlling the financial budget of universities. This system can improve the recording and processing of financial transactions. Moreover, it traces all the stages of the transaction processing from budget releases, does auditing, and accounting of expenditures, incomes, deposits, and funds. Furthermore, it provides financial information on present and past performance. The system is a responsive web-based system, which adjusts the layout of the pages based on the screen size and orientation of the user's device. The system was implemented by using programming languages such as HTML, PHP, JavaScript, jQuery, AJAX, and MySQL. Finally, the provided system needed to be investigated from the performance point of view. Therefore, a questionnaire was used to determine the system’s usability by using the System Usability Scale (SUS) tool. The results revealed that a score of 86.250% of satisfaction has been achieved.

The management of employees' salaries is an extremely complex task and time-consuming job due to having a large volume of payroll data and calculations. In many developing regions and countries including the Kurdistan Region of Iraq, the accounting processes of salary are manual. This leads to low speed in the calculation processes of deductions and allowances, easy making errors, difficulty to maintain salaries of previous months, low efficiency, and delay in generating reports. This paper represents the design, implementation, and evaluation of a web-based payroll management system (WPMS). This system can calculate the salary of every employee per month and annum efficiently and effectively. Moreover, it can keep the records of employees’ data including their pay, allowances, and deductions on monthly bases in the data mart. Additionally, the system can speedily and automatically generate employees’ payslips, accurate reports, and detailed statistics. Furthermore, WPMS provides a user-friendly environment and enables users to easily access, update, and delete data. The system was implemented by using programming languages: HTML, PHP, JavaScript, jQuery, AJAX, and MySQL. To conclude, the system was investigated for its usability by using the system usability scale tool. The results achieved an 87.8% score of usability satisfaction.