Evaluating the Effectiveness and Challenges of the Solid Waste Management System in Lilongwe City Council, Malawi
Posture and Stress Detection System using Open CV and Media Pipe
City Council Help Desk Support System
DDoS Attacks Detection using Different Decision Tree Algorithms
Comprehensive Study on Blockchain Dynamic Learning Methods
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
This paper presents a methodology for the early detection and diagnosis of breast cancer using the Wisconsin dataset. The methodology involves four main steps, including data collection, preprocessing, feature selection, and classification. Fine needle aspiration technique is used to extract the ultrasound image features of breast cancer, and preprocessing is performed to eliminate outliers, null values, and noise. Redundant parameters are removed during the feature selection process to improve accuracy. Six machine learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Tree, and Gaussian Naive Bayes, are employed for the classification of the breast cancer dataset. Support Vector Machine and K-Nearest Neighbor achieved the highest accuracy, with Logistic Regression, Gaussian Naive Bayes, Random Forest, and Decision Tree having lower accuracy scores. The proposed methodology could aid in the timely detection and diagnosis of breast cancer, and help doctors in selecting the optimal clinical treatment plan for their patients. Further work will be carried out to investigate the effectiveness of additional preprocessing algorithms in improving the classification accuracy of the breast cancer dataset.
The lack of security in Internet of Things (IoT) infrastructure across different applications has attracted the attention of researchers to work on IoT security issues. The paper presents a scenario where blockchain technology is combined with IoT to provide a decentralized security mechanism. Secure authentication and Key-Agreement technique are proposed for IoT nodes and peers to ensure proper authorization before communication can take place. The proposed protocol uses public key cryptography to generate a shared symmetric key for mutual authentication and two-party conversation. The protocol was tested using Scyther and was found to be robust enough to withstand all known authentication-related attacks, including replay, and typing attacks. Hyperledger, a blockchain technology, was employed to provide a more efficient IoT-enabled infrastructure for the scalability of IoT devices on the network. The proposed technique provides a secure and scalable system for device authentication in a blockchain-enabled IoT environment.
Communication has been a struggle for everyone since the covid outbreak and in the aftermath, people have had to get accustomed to video conferencing applications. However people with physical or mental limitations are still unable to use video conferencing apps and their interfaces. This necessitates the development of web-based video chat applications. These applications can aid those who are unable to communicate verbally and/or operate using standard mouse and keyboard inputs, but yet need to feel close to others when they are apart. The proposed application incorporates various accessibility features such as speech-to-text and text-to-speech, gaze tracking and pictorial speech interfaces. It enables individuals with disabilities to participate in virtual meetings on an equal footing with their peers. The goal is to remove barriers and promote inclusiveness in remote work and collaboration for all users, regardless of their abilities using this application.
SpeechDocs is a web application that aims to solve the difficulties faced by users with unsupportable conditions such as Arthritis in the hand, Parkinson's, Carpal Tunnel Syndrome, or Essential tremors, who have trouble using traditional keyboard and mouse-based software. With its fully-featured voice-controlled document editing system, SpeechDocs enables users to perform any operation such as creating, editing, opening, writing, or replacing words in a document, simply by using their voice. It uses speech-to-text converter and natural language processing technology to allow users to manage documents efficiently. The system also includes features such as facial recognition authentication, document history, and a favorites tab for easy access to frequently used documents. Although some challenges exist, such as accurately capturing speech in noisy environments, SpeechDocs has the potential to greatly improve the lives of users who have disabilities or other conditions that limit their ability to use traditional word processing software.
Video tracking and processing have become increasingly important in modern technologies due to their ability to provide data for various research projects and system representation. This paper sheds light on the capabilities for processing images using OpenCV, MediaPipe Library, Python programming language and a Convolutional Neural Network (CNN). To achieve the desired results, a wide range of methods for data processing and manipulation are utilized. OpenCV (Open Source Computer Vision) library is used to process real-time webcam data, allowing the user to write or draw simply by moving their hand and following their fingertip. Also, the CNN model is used to facilitate gesture detection.
