jonayet miah

Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image representations learned by CNNs, we demonstrate how to separate and manipulate image content and style, enabling the synthesis of high-quality images that combine content and style in a harmonious manner. We describe the methodology, including content and style representations, loss computation, and optimization, and showcase experimental results highlighting the effectiveness and versatility of the approach across different styles and content.

This study investigates the utilization of Graph Neural Networks (GNNs) within the realm of traffic forecasting, a critical aspect of intelligent transportation systems. The accuracy of traffic predictions is pivotal for various applications, including trip planning, road traffic control, and vehicle routing. The research comprehensively explores three notable GNN architectures—Graph Convolutional Networks (GCNs), GraphSAGE (Graph Sample and Aggregation), and Gated Graph Neural Networks (GGNNs)—specifically in the context of traffic prediction. Each architecture's methodology is meticulously examined, encompassing layer configurations, activation functions, and hyperparameters. With the primary aim of minimizing prediction errors, the study identifies GGNNs as the most effective choice among the three models. The outcomes, presented in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), reveal intriguing insights. While GCNs exhibit an RMSE of 9.25 and an MAE ...

Accurate demand forecasting in the retail industry is a critical determinant of financial performance and supply chain efficiency. As global markets become increasingly interconnected, businesses are turning towards advanced prediction models to gain a competitive edge. However, existing literature mostly focuses on historical sales data and ignores the vital influence of macroeconomic conditions on consumer spending behavior. In this study, we bridge this gap by enriching time series data of customer demand with macroeconomic variables, such as the Consumer Price Index (CPI), Index of Consumer Sentiment (ICS), and unemployment rates. Leveraging this comprehensive dataset, we develop and compare various regression and machine learning models to predict retail demand accurately.

The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using Machine Learning techniques.

The target of this research is to analyze the accuracy level for predicting Myocardial Infarction with the help of Machine Learning Algorithms. The rate of heart attacks in Bangladesh is increasing immensely day by day. It is a kind of disease (known as coronary artery disease) that occur when there is a loss of blood supply to the heart. In medical terms heart attack is commonly known as Myocardial Infarction. This is very important to find a way to predict the chances of occurrence of Myocardial Infarction beforehand to reduce the rate before it turns out to be a major issue. Hence our research is based on predicting the chances of occurring Myocardial Infarction so that people can take precautions and take measures to prevent it. This research includes a collection of data and the classification of data using Machine Learning Algorithms. We have collected 345 instances along with 26 attributes. This data have been collected from patients suffering from myocardial infarction along with other symptoms. The class attribute contains three types of category which are Distinctive, Non-Distinctive and Both. The training of the dataset have been done with K-Fold Cross Validation Technique and specifically three Machine Learning algorithms have been used which are Bagging, Logistic Regression and Random Forest. And, this research could be able to show accuracy for the above mentioned machine learning algorithms are 93.913%, 93.6323% and 91.0145% respectively.

With the mandate of light-weight working practices, iterative development, customer collaboration and incremental delivery of business values, Agile software development methods have become the de-facto standard for commercial software development, worldwide. Consequently, this research aims to empirically investigate the preparedness and the adoption of agile practices in the prominent software companies in Bangladesh. To achieve this goal, an extensive survey with 16 established software companies in Bangladesh is carried out. Results exhibit that the Scrum agile methodology is the highest practiced one. Alongside, to a great extent these software companies have the readiness to effectively adopt the Scrum methodology. However, with regard to practicing the Scrum principles, they fall short in many key aspects.

We are living in a world where almost every system is getting smart and automated in the industry, in business sectors, and also in homes. In smart home automation system, it involves of controlling various home appliances automatically with the help of exploiting technologies over desktops, laptops, smart phones, or tablets. The home automation system attains great popularity in the last decades and it improves the life of the people by providing the people to feel comfort as well as to feel safe. In this paper, we are developing services for a smart home automation system that will monitor the air quality, water quality, security system, fire system as well as control the temperature, lights, and access control of home. Precise semantics of the service specification is necessary to develop the service accurately. Thus, we use UML activity to provide the service specification and formalize our service specification by the temporal logic cTLA so that verification can be done before implementing this in the real setting.

The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using M...