Daily Archives: November 27, 2024

Abstract-The concept of Shoe Theory emphasizes that everyone is comfortable in their own shoes and should not be forced to wear someone else’s shoes. This theory posits that individual differences, including the effects of various elements such as time and generational perspectives, significantly impact workplace dynamics and organizational effectiveness. The theory highlights the importance of recognizing the unique experiences and backgrounds of team members to foster an inclusive and productive environment. Keeping creative destruction in mind, everything has its loophole to be breached. Although the answer may be yes or no, there always exists a condition of if/situation and but/exception.

Abstract-This paper proposes a flower recognition system using transfer learning with the ResNet-50 architecture. By utilizing pre-trained weights from ResNet-50, the system classifies ten species of flowers, drawing on an extended dataset with over 8,000 labelled images. The study addresses challenges in deep convolutional neural networks, such as overfitting and local optimality, by fine-tuning the ResNet-50 model. Initially, only the final layers of the model are retrained on the flower dataset, while the pre-trained layers remain frozen. After achieving initial convergence, all layers are unfrozen for full model fine-tuning. The dataset is divided into training, validation, and test sets to evaluate the model’s performance, which is measured using accuracy, and F1-score. The experimental results demonstrate that the transfer learning approach significantly improves classification accuracy and generalization, outperforming traditional methods. This approach proves especially effective in handling visually similar flower species and diverse environmental conditions. The study highlights the potential of transfer learning in enhancing the efficiency and robustness of flower recognition systems, contributing to broader applications in image classification tasks.

Abstract-The demand for robust authentication systems has risen significantly as cyberattacks become increasingly sophisticated. Current authentication mechanisms, such as textual passwords, biometrics, and graphical systems, each have unique vulnerabilities. This research explores the concept of a 3D password system, which integrates various authentication schemes into a virtual 3D environment to enhance security. The system allows users to interact with objects in a 3D space, forming unique and complex passwords based on sequences of interactions. This paper elaborates on the system’s design, implementation, and potential applications in critical and non-critical systems. Detailed analyses reveal that the 3D password provides superior resistance to timing attacks, brute force attempts, and well-studied schemes, while maintaining user-friendliness. Future research avenues include the incorporation of AR/VR and IoT technologies to further expand the utility of the 3D password system.

Abstract-Phishing attacks remain a prevalent cybersecurity threat, exploiting vulnerabilities in digital platforms to compromise sensitive user data. This paper introduces a novel machine learning-based framework for phishing URL detection, combining advanced feature engineering techniques and classification algorithms. By integrating lexical attributes, WHOIS data, and ranking metrics like PageRank and Alexa Rank, our approach enhances detection accuracy and minimizes false positives. Experimental results demonstrate superior performance across classifiers, achieving an accuracy of 99.8% using Support Vector Machines. The framework’s modular design ensures adaptability to evolving phishing tactics and scalability for enterprise deployment. This research lays the foundation for future advancements in AI-driven cybersecurity solutions.

Abstract-The plight of homeless children remains one of the most urgent global challenges, with millions of vulnerable children deprived of basic human rights such as shelter, healthcare, and education. Despite the rapid advancement of technology, child welfare systems in many developing countries still face significant hurdles, marked by inefficiencies and fragmented services. This paper proposes an innovative AI-driven system for adoption and rehabilitation that aims to address these systemic challenges holistically. By harnessing cutting-edge artificial intelligence (AI) algorithms, the system streamlines the adoption process, delivers personalized healthcare recommendations, and optimizes resource allocation for child welfare organizations. Through the integration of predictive analytics, data-driven decision-making, and a robust ethical framework, the system ensures transparency, fairness, and scalability. Early simulations and case studies highlight the transformative potential of AI in enhancing adoption success rates and improving healthcare outcomes for homeless children. The findings emphasize the system’s ability to drive meaningful improvements in global child welfare efforts, offering a scalable, ethical solution that can have a lasting impact on vulnerable children worldwide.