Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
Data Lake System for Essay-Based Questions: A Scenario for the Computer Science Curriculum
Creating Secure Passwords through Personalized User Inputs
Optimizing B-Cell Epitope Prediction: A Novel Approach using Support Vector Machine Enhanced with Genetic Algorithm
Gesture Language Translator using Morse Code
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
Integrated Atlas Based Localisation Features in Lungs Images
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
The aim of this paper is to greatly decrease the use of physical devices. To achieve this, the approach of human- computer interaction was employed, implementing a system that uses only finger movements to control the mouse pointer on the screen without relying on any hardware like a physical mouse. This system uses OpenCV as a medium for input obtained from a live camera and uses media pipe models to convert plane inputs from OpenCV into useful data, which is more efficient compared to traditional models like object and color detection. Along with the contactless mouse, this paper also implemented a voice system to support the contactless mouse in its activation and deactivation. This is achieved with the help of modules like speech recognition and PyAudio. This system also assists the user with simple tasks, such as opening Google or browsing content. By combining both voice recognition and a contactless mouse using hand movements, the system was converted into a seemingly contactless device.
In contemporary society, encountering individuals afflicted with various diseases is a common occurrence, emphasizing the critical need for accurate disease prediction as an integral facet of effective treatment. This paper focuses on leveraging classification algorithms such as Naive Bayes, Random Forest, Decision Tree, and KNN to predict diseases based on patient symptoms. This system enables users to input symptoms and, through meticulous analysis, accurately forecast the disease the patient may be suffering from. The prediction model extends to specific diseases like heart disease and diabetes, providing the outcome of the presence or absence of a particular ailment. The potential impact of such a predictive system on the future of medical treatment is substantial. Upon disease prediction, the system not only identifies the ailment but also recommends the appropriate type of doctor for consultation. This paper reviews recent advancements in utilizing machine learning for disease prediction and emphasizes the creation of an interactive interface as the front-end for user-friendly symptom input. By leveraging machine learning algorithms, this system extracts valuable insights from medical databases, aiding in early disease prediction, patient care, and community services. A comprehensive analysis was conducted using a dataset comprising 4920 patient records with 41 diseases. This integrated machine learning-based disease prediction system represents a significant step forward in leveraging advanced technologies for enhancing healthcare outcomes.
As the global community faces the escalating challenges posed by climate change, there is an increasing need for innovative tools that enhance public awareness and understanding of the complex and dynamic nature of environmental shifts. This paper introduces a Climate Change Visualization Awareness System (CCVAS), designed to bridge the gap between scientific data and public comprehension through immersive and accessible visualizations. Leveraging cutting-edge technologies, including augmented reality, interactive mapping, and data analytics, CCVAS provides users with real-time and historical insights into key climate indicators such as temperature variations, sea level rise, and extreme weather events. The CCVAS employs a user-centric approach, tailoring information to diverse audiences and promoting engagement through intuitive interfaces. Community engagement features facilitate collaborative efforts among users, enabling the sharing of experiences, knowledge, and initiatives aimed at addressing climate change challenges at the local and global levels. Users will be able to participate in discussions, organize events, and access community-driven resources within the CCVAS platform. Personalized user profiles enable individuals to customize their climate change experience within CCVAS, tailoring content and visualizations to their specific interests, expertise, and geographical locations. By providing personalized recommendations, alerts, and action plans based on user preferences and behavior, CCVAS will empower individuals to take meaningful steps towards climate resilience and sustainability in their daily lives. This paper outlines the architecture, functionality, and potential applications of the Climate Change Visualization Awareness System.
The rapid growth of network infrastructures and the increasing volume of data transmitted through them have led to a critical need for efficient and accurate anomaly detection systems in network transport. This paper proposes a novel anomaly detection system that utilizes machine learning techniques to identify abnormal patterns and deviations in network traffic. The proposed system follows a multi-layered approach, starting with the collection of network traffic data from various sources, including routers, switches, and gateways. The data is then preprocessed to extract relevant features and eliminate noise. Feature extraction is carried out using statistical, time-series, and flow-based analysis to capture the inherent characteristics of network communication. Machine learning algorithms, such as neural networks, or in this case, auto-encoders, will be trained to learn the patterns of normal network behavior and subsequently detect deviations from these patterns as anomalies. The system provides alerts and notifications to network administrators, allowing prompt investigation and response to potential security threats or network performance issues. It effectively differentiates between benign and malicious network activities, enabling network administrators to take proactive measures to secure their infrastructure and ensure uninterrupted communication.
This paper introduces an innovative IoT-enabled crop storage monitoring system designed to revolutionize the preservation and quality assurance of stored agricultural produce. Central to this system are IoT sensors strategically deployed within storage facilities. These sensors are tasked with the continuous tracking and monitoring of crucial environmental parameters, specifically temperature, humidity, and gas levels. Leveraging this real-time data, the system is engineered to promptly detect and respond to any deviations from the prescribed optimal storage conditions. The core strength of this system lies in its ability to generate instantaneous alerts upon detecting irregularities. These alerts serve as preemptive measures, effectively averting potential spoilage and curtailing post-harvest losses by enabling timely interventions and corrective actions. By harnessing IoT technology, this paper aims to create a proactive, automated, and responsive framework that ensures the integrity and safety of stored agricultural produce, ultimately contributing to enhanced food security and sustainability.
