A Multiview Point Perspective on Clustering Algorithms for Similarity Evaluation
Optimal Routing for Emergency Vehicles using A*(A-Star) Algorithm
IoT-Driven Examination Security: A Multi-Module Approach to Prevent Paper Leaks
A Comprehensive Study on Emotion Detection from Facial Expressions using AI and Ml (Python)
Optimal Routing for Emergency Navigation using Swarm Intelligence
Optimal Routing for Emergency Vehicles using A*(A-Star) Algorithm
Optimal Routing for Emergency Navigation using Swarm Intelligence
A Multiview Point Perspective on Clustering Algorithms for Similarity Evaluation
A Comprehensive Study on Emotion Detection from Facial Expressions using AI and Ml (Python)
IoT-Driven Examination Security: A Multi-Module Approach to Prevent Paper Leaks
Optimal Routing for Emergency Navigation using Swarm Intelligence
Optimal Routing for Emergency Vehicles using A*(A-Star) Algorithm
A Multiview Point Perspective on Clustering Algorithms for Similarity Evaluation
IoT-Driven Examination Security: A Multi-Module Approach to Prevent Paper Leaks
A Comprehensive Study on Emotion Detection from Facial Expressions using AI and Ml (Python)
The rapid expansion of digital content on the internet has significantly increased the volume of online documents, making the tasks of managing, searching, and retrieving information more complex. One of the most challenging aspects of this process is accurately assessing the similarities and differences between documents or their attributes. Various clustering algorithms have been proposed to address these challenges by determining the degree of similarity between elements in a dataset. Cluster analysis involves partitioning a set of N objects into smaller groups or clusters, such that the objects in the same cluster exhibit higher similarity to each other than to objects in other clusters. The similarity between objects can be explicitly or implicitly defined, depending on the context. This paper introduces a new approach to measuring similarity in document clustering based on multiple viewpoints (MVS). This method is compared with traditional K-means clustering algorithms. The fundamental difference between MVS and traditional approaches lies in the use of multiple viewpoints instead of a single viewpoint, which is common in traditional clustering methods. In this new approach, objects such as documents are measured not only with respect to their own cluster but also from viewpoints external to the clusters they belong to. The comparison between the K-means algorithm and the proposed incremental multiviewpoint-based clustering method is conducted, and simulation results demonstrate that the latter offers improved accuracy in clustering document data. The proposed method is implemented using Java programming, and the results highlight the advantages of the Incremental Multiviewpoint-Based Clustering approach in document similarity measurement.
Quick access to medical facilities during crises is crucial for improving results and maybe saving lives. In order to help people in need by promptly locating and directing them to the closest hospitals after an accident, this paper presents a web-based application. When every second matters, the application provides essential location-based services by utilizing the Google Maps API package, enabling prompt decision-making. The Google Maps Geocoding API is used by the program to convert the user's current location—designated as the accident site—into geographic coordinates. After that, it uses the Places API to find hospitals within five kilometers, showing customers a number of options so they can make an informed decision. To find the nearest hospital and cut down on journey time, the Haversine formula is used to calculate distances. A caching method lowers latency and API call costs by storing frequently visited routes for performance improvement. The A* algorithm further improves pathfinding by taking current conditions into account to guarantee the most effective navigation in intricate urban environments.
Examinations are increasingly vulnerable to question paper leaks, posing urgent challenges to educational institutions. Breaches, such as unauthorized access to exam materials, logistical weaknesses in exam transfers, and the lack of real- time surveillance and monitoring, are jeopardizing the integrity of exams, tarnishing the reputation of institutions, and unfairly benefiting certain individuals. In order to solve these issues, this paper has designed an improved Exam Paper Leak Avoidance System using GPS, GSM, RFID, and a timer, which an Arduino UNO microcontroller will control. An RFID for identification of students, GPS for tracking student location with an accuracy of ± 5 meters, GSM for alerting in 10 seconds, and a timer for the release of papers at a fixed time. After the testing process, which involved simulated exam scenarios and real-time trials, the efficiency of the proposed system is confirmed when the number of unauthorized tries is decreased to a minimum level of up to 95%, thus providing the examination process is secure, real-time, and fully reliable. Providing a cost-solving and easy-to-implement solution helps create a better structure to enforce exam security and maintain the institution's credibility.
Emotion detection from facial expressions is a rapidly advancing field within computer vision and artificial intelligence, with profound implications for human-computer interaction, mental health assessment, marketing research, and driver safety systems. This paper provides a comprehensive overview of developing systems for recognizing human emotions such as happiness, sadness, anger, surprise, fear, disgust, and neutrality from facial images or video streams using Python, artificial intelligence (AI), and machine learning (ML) techniques. It covers the entire pipeline, including data acquisition and preparation, facial detection, preprocessing, feature extraction methodologies both handcrafted and learned, selection and training of appropriate ML/AI models, and robust evaluation strategies. The paper also highlights key Python libraries and frameworks essential for implementation, discusses common challenges such as variations in expression intensity and cultural differences, and outlines potential future study directions in this dynamic area.
Emergencies are abrupt, unanticipated, and frequently life-threatening circumstances that need prompt attention and action in order to avoid injury, reduce damage, or save lives. People must be able to quickly find the closest emergency services, including hospitals, fire stations, or police stations, in these situations. Because of things like shifting traffic patterns and erratic road conditions, navigating today's complicated metropolitan environments to find the most effective path to these services can be difficult. By supplying real-time data, such as traffic conditions, weather updates, vehicle speeds, and details about different zones within a region, Vehicle Ad-Hoc Networks (VANETs) provide a solution. The best paths to the closest emergency services can be quickly determined by utilizing the data gathered by Wireless Sensor Networks (WSNs) and swarm intelligence algorithms, particularly Ant Colony Optimization (ACO).This creative method has the potential to save many lives in dire circumstances in addition to saving a significant amount of time.
