Bøker utgitt av MEEM PUBLISHERS

This focuses on enhancing the classification of medical data using learning algorithms. With the increasing availability and complexity of medical data, accurate and efficient classification techniques are crucial for effective healthcare decision-making. The research aims to explore various learning algorithms and their potential to improve the classification accuracy of medical data.By leveraging machine learning algorithms, this research seeks to optimize the process of categorizing medical data into specific classes or categories. The study will investigate the performance and effectiveness of different algorithms, such as decision trees, support vector machines, neural networks, and ensemble methods. These algorithms will be evaluated based on their ability to handle diverse medical data types, including patient records, diagnostic reports, medical images, and laboratory results.The outcomes of this research have the potential to contribute significantly to the field of medical data analysis. The enhanced classification techniques can help healthcare professionals accurately interpret and utilize medical data, leading to improved diagnoses, treatment planning, and patient care. Additionally, the findings may pave the way for developing automated systems that can assist medical professionals in data-driven decision-making, reducing human errors and enhancing overall healthcare efficiency.Overall, its aims to advance the field of medical data classification by leveraging learning algorithms to achieve more accurate and reliable results. The research findings have the potential to positively impact healthcare practices, facilitating better healthcare outcomes and improving patient well-being.

As the use of Software Defined Networking (SDN) becomes increasingly prevalent, securing these dynamic and programmable network architectures becomes paramount. The "Design of Intrusion Detection System for Software Defined Networking Using Machine Learning Algorithms" offers a groundbreaking solution to safeguard SDN environments against potential threats and attacks.This innovative Intrusion Detection System (IDS) leverages the power of machine learning algorithms to continuously monitor and analyze network traffic, behavioral patterns, and anomalies in real-time. By learning from historical data and network behaviors, the system can accurately identify deviations and malicious activities, enabling swift responses to potential intrusions.The integration of machine learning algorithms empowers the IDS to adapt to evolving threats, ensuring a proactive defense strategy and reducing the risk of false positives. As a result, network administrators can stay one step ahead of attackers and protect critical data and resources effectively.The application of this cutting-edge technology in SDN environments not only enhances security but also optimizes network performance. By swiftly detecting and mitigating threats, the IDS contributes to uninterrupted and seamless network operations, maintaining the integrity and availability of services.Furthermore, the design's scalability ensures its effectiveness across various SDN architectures and environments, making it a versatile and future-proof solution for businesses and organizations with diverse networking infrastructures.the "Design of Intrusion Detection System for Software Defined Networking Using Machine Learning Algorithms" represents a significant advancement in network security. By harnessing the capabilities of machine learning, this IDS enables intelligent, efficient, and reliable protection against potential intrusions, bolstering the trust and resilience of Software Defined Networks in the face of ever-evolving cyber threats.