Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient’s heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.

Train incidents with animals and even humans have gotten more attention in the past. Every year, many animals are killed on train tracks, causing an imbalance of the ecosystem and significant delays in railway traffic. Similarly, sometimes train accident is prevalent at the rail gates, where motor vehicles are crossing the train line. All that happens due to the lack of detecting the objects correctly on the train line. Since the detection depends on the driver or human, there is a possibility of occasionally making an error in honking the horn at the right moment, leading to the accident. That’s why there needs to be an automated solution to detect the objects on the train line and inform the driver about them. In this research, we proposed a system that detects objects only on the train line. Sometimes, there is an object just beside the train line, which is at a safe distance. To eliminate this kind of wrong detection, we detected the train line first and then detected the objects on the detected train line. Here, we used the Mask R-CNN algorithm to detect the train line and things on the train line. A railway traffic dataset with an input size of 512 x 512 pixels was used to test the proposed methodology, which came up with results with a mean average precision of 0.9375 and a frame rate of 30 frames per second. According to the findings of the experiments, the suggested approach can apply to identify objects on railroads in the real world.

The population of Bangladesh reached beyond 16 Crores, and the rate of population is increasing every day. There will be a severe food issue after 25-30 years, so it is important to improve the agriculture. The farmers today suffer from a shortage of rainfall and water shortages. The key objective of this paper is to include an integrated irrigation network, and also to measure the quantity of the three major macronutrients, nitrogen (N), phosphorus (P), and potassium (K), in the soil, thereby saving the farmer's time, money, and strength. Comparing the solution to a color diagram determines the amount of N, P and K in the soil sample. It will describe the high, medium and low levels of N, P and K. Manual control is necessary for the conventional farmland techniques. Human interference can be minimized with automatic irrigation systems. This paper presents a smart system that will sense and analyze the NPK rate of soil and will detect the moisture rate of that land, then based on these values, automatic irrigation will happen. As a master of system ATtiny Microcontroller used here to make the device cost-effective and instead of pH meter here used TCS230 color sensor to detect the soil pH or NPK rate. To make the system more cost-effective, there used a Mobile app as the monitoring device instead of any external hardware display.

The integration of Artificial Intelligence (AI) into the field of quantum communication has emerged in recent years as a promising avenue for enhancing secure data transmission, error correction, and scalability in quantum networks. This paper offers a comprehensive survey of AI applications in quantum communication, with a focus on machine learning (ML) models such as neural networks and reinforcement learning, which are adapted to manage complex quantum challenges. Despite the growing body of research, current studies often concentrate on specific AI techniques or quantum applications, lacking a unified framework that evaluates their practical effectiveness, scalability, and cost-efficiency. Addressing this gap, our survey systematically reviews and categorizes AI-driven methodologies across core aspects like system security, transmission speed, and reliability. Through a critical comparison and ranking of existing techniques, this study identifies best practices and key advancements while highlighting areas requiring further exploration. By providing an in-depth analysis of AI’s potential to transform quantum communication, this paper aims to serve as a foundational resource for researchers and practitioners seeking to develop resilient, adaptive, and economically viable quantum communication systems.

The integration of Artificial Intelligence (AI) into the field of quantum communication has emerged in recent years as a promising avenue for enhancing secure data transmission, error correction, and scalability in quantum networks. This paper offers a comprehensive survey of AI applications in quantum communication, with a focus on machine learning (ML) models such as neural networks and reinforcement learning, which are adapted to manage complex quantum challenges. Despite the growing body of research, current studies often concentrate on specific AI techniques or quantum applications, lacking a unified framework that evaluates their practical effectiveness, scalability, and cost-efficiency. Addressing this gap, our survey systematically reviews and categorizes AI-driven methodologies across core aspects like system security, transmission speed, and reliability. Through a critical comparison and ranking of existing techniques, this study identifies best practices and key advancements while highlighting areas requiring further exploration. By providing an in-depth analysis of AI's potential to transform quantum communication, this paper aims to serve as a foundational resource for researchers and practitioners seeking to develop resilient, adaptive, and economically viable quantum communication systems.

Background: Typhoid fever remains a significant public health challenge, especially in developing countries where diagnostic resources are limited. Accurate and timely diagnosis is crucial for effective treatment and disease containment. Traditional diagnostic methods, while effective, can be time-consuming and resource-intensive. This study aims to develop a lightweight machine learning-based diagnostic tool for the early and efficient detection of typhoid fever using clinical data. Methods: A custom dataset comprising 14 clinical and demographic parameters—including age, gender, headache, muscle pain, nausea, diarrhea, cough, fever range (°F), hemoglobin (g/dL), platelet count, urine culture bacteria, calcium (mg/dL), and potassium (mg/dL)—was analyzed. A machine learning metamodel, integrating Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Tree classifiers with a Light Gradient Boosting Machine (LGBM), was trained and evaluated using k-fold cross-validation. Performance was assessed using precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results: The proposed metamodel demonstrated superior diagnostic performance, achieving a precision of 99%, recall of 100%, and an AUC of 1.00. It outperformed traditional diagnostic methods and other standalone machine learning algorithms, offering high accuracy and generalizability. Conclusions: The lightweight machine learning metamodel provides a cost-effective, non-invasive, and rapid diagnostic alternative for typhoid fever, particularly suited for resource-limited settings. Its reliance on accessible clinical parameters ensures practical applicability and scalability, potentially improving patient outcomes and aiding in disease control. Future work will focus on broader validation and integration into clinical workflows to further enhance its utility.