This work has been designed to develop an a dynamic road signal based on an emergency with density. The sync signal automatically switches to detecting traffic density at the intersection. Traffic congestion is a serious problem in many large cities around the world and has become a nightmare for travelers in these cities. The conventional traffic light system is based on the concept of fixed time assigned to each side of the join that cannot be varied by varying traffic density. (...) The tie times assigned are fixed. But in an emergency, emergency timings change according to the distance and depth of the traffic. At the time the emergency vehicle is read by the receiver it will execute the management of the traffic light. These projects are infrared proximity sensors in the sightline configuration through the load to detect the traffic light density. The vehicle density is measured in several sectors based on the times assigned as a result. Every crossing of the emergency vehicle logs the data's to the cloud. This log signal would help with the destination acknowledgment. This synchronization will greatly reduce traffic jam. -/- . (shrink)

The Movie Recommendation System using Machine Learning Techniques is a data-driven approach designed to provide personalized movie suggestions based on user preferences and historical data. This system leverages advanced machine learning algorithms, including collaborative filtering, content-based filtering, and hybrid methods, to predict the most relevant movies for individual users. The system's primary goal is to enhance user experience by recommending movies that align with their tastes, thereby promoting user engagement and satisfaction. The recommendation process starts by collecting user data, such (...) as viewing history, ratings, and genre preferences, which are then processed to generate personalized suggestions. Collaborative filtering analyzes the behavior of similar users to recommend movies based on common interests, while content-based filtering focuses on movie features such as genre, director, actors, and plot summary. By combining both methods, the hybrid approach further refines the recommendation, ensuring diverse and accurate results. The model's effectiveness is evaluated using standard metrics like precision, recall, and F1-score, demonstrating its ability to accurately predict user preferences. The system can be integrated into various platforms, offering real-time recommendations and improving user satisfaction. This project can be expanded by incorporating more sophisticated algorithms, such as deep learning, to further enhance recommendation accuracy and scalability. Overall, this movie recommendation system aims to optimize content discovery and cater to diverse audience needs. (shrink)

Although Internet of Things (IoT) devices are being rapidly embraced worldwide, there are still several security concerns. Due to their limited resources, they are susceptible to malware assaults such as Gafgyt and Mirai, which have the ability to interrupt networks and infect devices. This work looks into methods based on machine learning to identify and categorize malware in IoT network activity. A dataset comprising both malware and benign traffic is used to assess different classification techniques, such as Random Forest, XGBoost, (...) Logistic Regression, etc., for multi-class malware detection. After a great deal of empirical testing, XGBoost comes out on top, providing 99.9% recall and accuracy. Both known and unknown malware can be detected by the trained model with remarkable precision. The use of transfer learning, where the XGBoost model is used as a basis for the rapid construction of new malware detection models, is one of the major innovations put forth. This makes it possible to quickly adjust to new dangers. More information about how real-time network traffic can be monitored with the help of the created model to find irregularities and sound the alarm. An intelligent and proactive security solution for IoT environments is offered by the machine learning technique that is being discussed. This is an efficient defense against malware because of its high accuracy, low false positive rate, real-time detection capability, and adaptability to new malware varieties changing risks associated with IoT. The suggested methods will assist in securing susceptible IoT devices and networks from obstructive malware assaults. (shrink)

This paper aims at exploring the uses and practices of English as a Foreign Language (EFL) Teachers pedagogical capital in the classroom. It examines how the use and usages of EFL teachers’ pedagogical capital affects the way they deal with their students in the language classroom. The research was grounded within the interpretive research paradigm. The in-depth unstructured interview and observation were the research tools under narrative inquiry design. The data were collected from three different participants and themes were substantiated (...) by Clandinin and Connelly to replicate their voices and experiences. The research finding reveals that teachers’ profound knowledge and skills about their capital are useful to integrate various cultures and content in the classrooms. Pedagogically inbuilt potentials of teachers and their pedagogical capital maximally enhance learners’ learning opportunities and foster students’ potential. However, it contributes to teachers’ education by rationalizing the essence of teachers’ pedagogical knowledge to transform our ELT practice in Nepal. (shrink)

This paper presents a Human Emotion Detection system utilizing Convolutional Neural Networks (CNN). The model is trained on facial expression data to classify various human emotions such as happiness, sadness, anger, and surprise. The CNN approach allows the system to automatically learn features that distinguish different emotions. We describe the model architecture, data preprocessing, and training process in detail. Key results demonstrate the system's high accuracy in detecting emotions in real-time applications. This work highlights the potential of CNNs in emotion (...) recognition and its implications for applications in human-computer interaction, security, and behavioral analysis. (shrink)

This is review of the book "Rightful Relations with Distant Strangers: Kant, the EU, and the Wider World" by Aravind Ganesh, which discusses the relevance of Immanuel Kant's legal philosophy for the European Union's exercise of extraterritorial jurisdiction. The book explores this issue from the perspectives of public international law and private law theory as well.