Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
A comprehensive cholera detection system leveraging cutting-edge technologies such as neural networks, machine learning, chatbots, live maps, and real-time statistical graphs is proposed. The system integrates a user-friendly chatbot interface to interact with individuals, prompting them to input relevant health information and symptoms. Behind the scenes, neural networks and machine learning algorithms analyze the data to detect potential cholera cases, offering users instant insights into their health status. The system incorporates live maps to track reported cases geographically, enabling a swift response from health authorities. Moreover, real-time statistical graphs provide dynamic visualizations of cholera trends, aiding in the identification of potential outbreak hotspots. By amalgamating these technologies, the cholera detection system not only facilitates early diagnosis and intervention but also enhances public health monitoring and management, contributing to the overall control and prevention of cholera outbreaks.
A chatbot is a conversational device designed to provide smart communication with humans through speech and text. Chatbots emerge as virtual machines that interact with humans through their dialect, offering both spoken and textual conversation and responses to questions. In recent times, there has been tremendous progress in the development of virtual assistants and chatbots with advanced technologies based on speech recognition. This paper presents a chatbot for the web-based Crime Reporting and Handling System in the Malawi Police Service. The chatbot utilizes a combination of classification and generative models. It features a complaint registration system that allows users to file complaints. A custom named entity recognition model is employed to extract structured information such as location, time, and crime type from unstructured complaints, enabling authorities to comprehend complaints effectively and efficiently. The chatbot strives to provide efficient and user-friendly methods for registering complaints using these techniques, as well as informing individuals about the crime reporting and handling system. Thus, the system aspires to apply Natural Language Processing (NLP) for social good. In this paper, we will discuss how chatbots are designed, the machine learning and deep learning techniques and algorithms used, compare the technologies, and evaluate the performance of chatbots. Chatbots are becoming friendlier to humans, evolving beyond mere communication machines and representing future aspects of human and technology interaction.
Automatic lip-reading, the process of decoding spoken language through visual analysis of lip movements, presents a promising avenue for advancing human-computer interaction and accessibility. This research proposes an innovative model integrating 3D Convolutional Neural Networks (3D-CNN) and Long Short-Term Memory (LSTM) networks to enhance the accuracy and efficiency of lip-reading systems. The model addresses challenges related to lighting variations, speaker articulation, and linguistic diversity. This contrasts with traditional 2D-CNN, which focuses solely on spatial information, often missing temporal intricacies vital for accurate lip-reading. By incorporating 3D-CNN alongside LSTM, the proposed model significantly enhances recognition accuracy, offering a more comprehensive understanding of speech nuances. Extensive training on a diverse dataset and the exploration of transfer learning techniques contribute to the robustness and generalization of the model.
Currently, the internet serves as the predominant means of communication and is utilized by a vast number of individuals worldwide. Simultaneously, the commercial aspect of the internet is contributing to a rise in susceptibility to cybercrimes, leading to a significant surge in the occurrence of distributed Denial of Service (DDoS) assaults over the last decade. DoS/DDoS assaults primarily target network resources such as network bandwidth, CPU time, memory consumption, web servers, and network switches. Network security is an essential and crucial problem in the modern interconnected society. Numerous studies have been undertaken by multiple researchers thus far in order to identify this attack. However, there is still room for improvement in past investigations. This paper presents a novel approach for detecting and simulating DoS/DDoS attacks in modern networking environments, introducing a new paradigm. It is done in a controlled environment. The primary focus of this work is to simulate an attacker's perspective of a DoS/DDoS attack by repeatedly sending huge SYN flood packets to a specific target or network server using the hping3 tool. On the server side, the proposed attacker detector script continuously monitors incoming network connections on the network server using the netstat command. It identifies potential DoS/DDoS attacks by analyzing the connection count and comparing connections count with an assumed threshold. This experiment results in 61% CPU usage and 7.1% memory consumption while a DDoS attack triggers on the target server. Additionally, the proposed script performs statistical analysis and displays warning messages on the console when suspicious activity is detected on the network server. Wireshark is also utilized in this work to detect anomalous network traffic patterns in order to identify distributed denial-ofservice (DDoS) attacks that are targeting a network server. Additionally, it offers the capability to block the IP address of the attacker if the configuration allows for it. This proposed approach efficiently identifies DDoS activity in real-time network traffic, further helping to improve network security.
In recent years, the confluence of computer vision and natural language processing, propelled by advancements in deep learning, has garnered significant interest. Among its notable applications, image captioning stands out, enabling computers to comprehend visual content through one or more sentences. This process entails not only identifying objects and scenes but also analyzing their attributes, states, and interrelations, culminating in the generation of meaningful descriptions encapsulating high-level image semantics. While inherently complex, image captioning has seen remarkable progress thanks to the efforts of numerous researchers. This paper offers a comprehensive review of three prominent image captioning methodologies leveraging deep neural networks: CNN-RNN, CNN-CNN, and Reinforcement-based frameworks. Each approach is accompanied by a detailed analysis of representative works, elucidating their respective contributions. Furthermore, evaluation metrics pertinent to these methods are discussed, followed by a synthesis of their advantages and primary challenges. Through this thorough examination, insights into the evolving landscape of image captioning are aimed to be provided, highlighting avenues for further exploration and innovation.
