Syed Anas Ansar

In modern communication networks, particularly satellite-based systems, data security faces significant challenges from vulnerabilities such as signal interception, jamming, and latency during long distance transmissions. Traditional cryptographic methods are increasingly vulnerable to quantum computing threats, underscoring the need for advanced solutions to protect data integrity, confidentiality, and availability. This research investigates the fusion of quantum cryptography and Machine Learning (ML) to improve security in satellite communication. The Quantum Key Distribution (QKD), which is grounded in quantum mechanics, enables unbreakable encryption by detecting eavesdropping via quantum state disturbances. The CatBoost ML algorithm is applied to a dataset of 10,000 records featuring categorical attributes for prioritizing security elements such as anomaly detection, encryption types, and access controls. The model yields an accuracy of 89.23% and Area under Curve the Receiver Operating Characteristic (AUC-ROC) score of 94.56%, effectively predicting threat levels. Feature importance reveals anomaly detection (28.5%) and quantum encryption (22.3%) as primary contributors. While hurdles such as high implementation costs and transmission range limitations persist, this quantum ML synergy provides a proactive, adaptive framework for resilient, future-ready communication networks.

Blockchain technology, renowned for its decentralized nature and robust security features, is increasingly integrated into web applications. The blockchain web application is not risk-free from the security issue, just like any other application. This paper examines the fundamental vulnerabilities of blockchain web applications, namely intelligent contract vulnerabilities, consensus mechanism vulnerabilities, and private key management risks. It also reviews the current literature regarding blockchain security, revealing critical findings and finding the research gap. The paper further describes several robust techniques helpful in mitigating such risks: strengthening smart contract security through code auditing and formal verification, reinforcement of consensus protocols with hybrid models, and private critical protection using multi-signature wallets and hardware security modules. The study concludes with a discussion on future directions in blockchain security, emphasizing emerging technologies and the importance of collaborative efforts between blockchain projects and security researchers. The following analysis is designed to contribute more secure blockchain-based web applications.

A new fifteen-level stepped DC to AC hybrid converter is proposed for Solar Photovoltaic (SPV) applications. A boost chopper circuit is designed and interfaced with the fifteen-level hybrid converters specific to Electric Vehicles’ Brushless DC Motor (BLDC) drive systems. In chopper units, the output of solar panels is regulated and stepped up to obtain the nominal output voltage. In the stepped DC-link hybrid converter configuration, fifteen-level DC-link voltage is achieved by the series-operated DC-link modules with reduced electrical energy compression. From the comprehensive structure, it is anecdotal that the proposed topology has achieved minimum switching and power loss. Elimination of end passive components highlights the merits of the proposed hybrid systems. The reduction of controlled power semiconductor switches and gate-firing circuits has made the system more reliable than other hybrid converters. From the extensive analysis, the experimental setup has reported that 7% reduction in harmonics and a 54% reduction in controlled power switches than the existing fifteen-level converter topologies. Mitigation of power quality issues in the voltage profile of a fifteen-level multilevel hybrid converter is achieved through the implementation of dsPIC digital-controller-based gate triggering circuits.

A reliable career guidance system has become more crucial in this era of rapid technological advancements and ever-evolving industries. Career Prediction Application emerges as a trailblazer, reshaping our perception of professional journeys. This cutting-edge web-based platform, meticulously crafted using Django, leverages the wonders of machine learning to forecast potential career options for students and professionals alike accurately. The application puts the power of a personal career advisor right at fingertips, guiding you toward the most suitable paths in the vast landscape of opportunities. With its interactive and user-friendly interface, this system takes career planning to a whole new level, making it a must-have tool in today's fast-paced world. The platform boasts a plethora of powerful features, including a cutting-edge Resume Analyzer, an extensive Knowledge Network, and a curated collection of Courses, all aimed at empowering users to augment their skills and excel in their chosen careers. The centerpiece of this application lies in its Career Prediction module, which leverages the K-Nearest Neighbors (KNN) algorithm, a state-of-the-art machine learning technique, to scrutinize and evaluate user data meticulously. The resume analyzer utilizes Natural Language Processing to analyze resumes, ensuring industry-standard content and formatting for increased visibility and job prospects. Overall proposed application leverages sophisticated algorithms and data analysis techniques to effectively match individuals' unique aptitudes, skills, and interests with the most suitable professions, enabling informed decision-making and fostering heightened career satisfaction.

The advent of quantum computing has ignited unprecedented anticipation within the realm of IT. This has transcended the limitations of classical computation and revolutionized various facets of technological advancement. At the forefront of this quantum revolution stands the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by a delicate interplay of immense potential and persistent imperfections. This paper delves into the intricacies of NISQ-era quantum computing, unraveling its fundamental principles, challenges, and transformative implications for the IT landscape. Beginning with quantum computing basics-qubits, superposition, entanglement, and gates-the NISQ era's essence addresses error-prone operations and decoherence. Quantum algorithms for NISQ devices, like Grover's search, Shor's factorization, and Variational Quantum Eigensolver (VQE), are scrutinized for their computational potential. Central to NISQ's viability, quantum error correction and mitigation strategies are surveyed, stabilizing quantum computations amidst noise. Examining quantum hardware architectures and performance metrics unveils NISQ devices' potential. Applications of NISQ quantum computing in cryptography, optimization, machine learning, and more are explored, showcasing quantum advantage's real-world potential. This paper embarks on a multidimensional exploration of NISQ quantum computing, illuminating its intricate facets, applications, and ethical implications. As quantum computing evolves from a theoretical notion to a tangible paradigm, the insights gleaned herein contribute to a holistic understanding of its role in shaping the next wave of computing.