Research Reports on Computer Science

Harnessing Rubik's Cube Algorithm for Counteracting Man-in-the-Middle Attacks

Thu, 20 Jun 2024 00:00:00 +0800

In today's world, businesses and individuals alike rely on cloud computing for storing and accessing data. However, with the growing number of cyber threats, it is crucial to prioritize cloud security. Despite various algorithms designed to prevent cyber-attacks, attackers have developed advanced tactics to bypass security measures. That's why it is essential to understand the three primary platforms of cloud computing and how they work together to create a seamless environment. Cloud cryptography is a highly effective method for encrypting data and enabling authorized users to access it securely. With the aid of an algorithm and a key, cryptography transforms plaintext into ciphertext, providing a defence mechanism against malicious third parties. The use of cryptographic methods ensures that only authorized entities can access the data exchange. Data encryption is a recognized and efficient security tool for safeguarding an organization's information. By converting data into a code that can only be accessed or interpreted by someone with the correct encryption key, data encryption provides a reliable defense against cyber threats. Cloud computing services, such as email, calendars, Skype, and WhatsApp, are becoming increasingly essential to our daily activities. These services allow us to store and manage data on the cloud infrastructure, enabling remote access to our data from anywhere at any time. Before cloud computing, accessing data relied on client-server computing. However, with cloud computing, we have access to a decentralized network of servers worldwide that work together to create a seamless environment. In conclusion, protecting data is crucial, and with the growing number of cyber threats, it is essential to prioritize cloud security. By implementing effective data encryption and cloud cryptography, individuals and organizations can safeguard their information and data from malicious attackers.

Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques

Wed, 08 May 2024 00:00:00 +0800

This study presents an improved version of the Driving Training-Based Optimization (DTBO) algorithm, the Improved Driving Training-Based Optimization (IDTBO). The work addresses fundamental issues in selecting drivers and learners for the conventional DTBO, which substantially impact the algorithm's accuracy and convergence speed. Two significant improvements are proposed: including the crowding distance technique for more diverse driver and learner selection and incorporating the Levy Flight distribution for better exploration and local optima avoidance. The IDTBO's performance is evaluated using twelve benchmark functions, including unimodal and high-dimensional multimodal optimization functions. The results indicate that IDTBO performs exceptionally well, with more extraordinary exploitation ability on unimodal functions and consistent achievement of the global optima. The proposed IDTBO demonstrated exceptional exploration capabilities on high-dimensional multimodal functions and performed competitively with other algorithms in the literature. From six functions, the IDTBO obtained zero optimal values. Again, the rate of convergence analysis demonstrates that IDTBO finds optimal solutions in fewer iterations, demonstrating its capacity to balance exploration and exploitation. To assess the strength of the IDTBO in solving real-world problems, the improved DTBO is further tested on two practical benchmark engineering problems. The IDTBO again produced a competitive performance against other algorithms in the literature. The study shows that IDTBO is a valuable metaheuristic algorithm that can tackle various real-world optimization problems.

Deep Learning Based Fabric Defect Detection

Syeda Rabia Arshad, Muhammad Khuram Shahzad — Wed, 20 Mar 2024 00:00:00 +0800

Ensuring quality standards is a crucial stage within the textile sector. Automated classification of the fabric defects is a vital step during the fabric manufacturing process in order to prevent any faulted fabric from being supplied to the market. The defects on the surface of the fabric were manually identified by the individuals but this poses problems in terms of human-error and is also time-consuming. Efforts have been made to achieve better precision in defect detection through image processing studies, leading to the development of automated systems. In this study, some high-performing deep learning models are applied including ResNet and VGG-16 and illustrated how these algorithms can be used in the domain of textile manufacturing for fabric defect detection. A combination of images are used ranging from patterned and textured to plain for better defects recognition on any given fabric. The algorithm VGG-16 has displayed 73.91% accuracy while the ResNet algorithm has shown 67.59% accuracy.