Category Archives: Uncategorized

Authors: Aye Pa Pa Myo, Liping Chen

Abstract: This study aims to explore the linguistic structures and the concept of power underlying King’s speech through structural – emotional perspectives, adopting the dual lens of Functional Discourse Grammar (FDG) and Conceptual Metaphor Theory (CMT). FDG provides a robust framework to examine the functional structures of King’s language at the syntactic, semantic, and pragmatic levels, while CMT allows for a nuanced understanding of how metaphors in the speech contribute to the construction of power, social change, and collective identity. The study employs a mixed quantitative -qualitative research method. Findings reveal that King prefers using linguistic structures at the phonological and morphosyntactic levels more than at the representational and interpersonal levels. He further emphasizes concepts of power using 15 instances of metaphors in his speech. His masterful employment of linguistic structures and metaphors brings ideology, stance, and power to his political discourse, grasping the attention of his audiences and making significant efforts in demanding rights for freedom, justice, equality, and job opportunities, as well as in promoting business in the Black community, which is being oppressed by the White Society. Future research could further explore King’s linguistic structures and metaphors by utilizing digitalization in the modern era.

DOI: https://doi.org/10.5281/zenodo.19668145

Published by: vikaspatanker

Authors: H. M. Pawar, Deore Shrawani Shashikant, Kapadnis Tejas Sudhakr, Pagar Shubham Manik

Abstract: With the rapid increase in road accidents and traffic violations, ensuring driver authenticity and safety has become a major concern. This paper presents an IoT-Based Driving License Detection and Safety System designed to verify the validity of a driver’s license and enhance road safety through real-time monitoring. The proposed system integrates RFID/QR code-based license identification with IoT-enabled devices to authenticate drivers before vehicle ignition. A microcontroller-based unit processes the data and checks it against a stored or cloud-based database. If the license is invalid, expired, or not detected, the system restricts vehicle operation and sends alerts to concerned authorities or vehicle owners. Additionally, safety features such as alcohol detection, seat belt monitoring, and accident detection are incorporated to minimize risks. The system uses wireless communication technologies to transmit real-time data and alerts. This approach not only prevents unauthorized vehicle usage but also promotes responsible driving behavior. Experimental results demonstrate that the system is efficient, reliable, and suitable for smart transportation and intelligent traffic management systems.

DOI: https://doi.org/10.5281/zenodo.19667350

Published by: vikaspatanker

Authors: Malith Jayasinghe

Abstract: Big Data Analytics has become a fundamental component of modern cloud-based enterprise systems, enabling organizations to extract valuable insights from massive volumes of structured and unstructured data. This study explores the integration of big data analytics within cloud computing environments, highlighting how cloud platforms provide scalable storage, high-performance processing, and cost-efficient infrastructure for handling complex datasets. The paper examines key technologies such as distributed computing frameworks, data lakes, real-time streaming, and advanced analytics techniques including machine learning and predictive modeling. It also discusses how enterprises leverage cloud-based analytics to enhance decision-making, optimize operations, and gain competitive advantages across domains such as finance, healthcare, retail, and manufacturing. Furthermore, the study addresses critical challenges including data security, privacy, data governance, and latency issues, along with strategies to mitigate these concerns. The findings emphasize that the combination of big data analytics and cloud computing empowers organizations to become more agile, data-driven, and innovative in a rapidly evolving digital landscape.

DOI: https://doi.org/10.5281/zenodo.19666895

Published by: vikaspatanker

Authors: Andi Saputra

Abstract: DevSecOps has emerged as a critical evolution of the DevOps paradigm, integrating security practices seamlessly into every phase of the software development lifecycle. This study presents a comprehensive evaluation of DevSecOps in modern software development, emphasizing its role in enabling faster, more secure, and reliable software delivery. By embedding security controls into continuous integration and continuous deployment (CI/CD) pipelines, DevSecOps ensures that vulnerabilities are identified and mitigated early in the development process. The paper examines key components such as automated security testing, infrastructure as code (IaC) security, container security, and continuous monitoring. It also explores how organizations leverage DevSecOps to achieve compliance, reduce risk, and enhance collaboration between development, operations, and security teams. Real-world use cases and industry practices are analyzed to highlight the effectiveness of DevSecOps in addressing evolving cyber threats. Furthermore, the study discusses challenges such as cultural resistance, toolchain complexity, and skill gaps, along with strategies to overcome them. The findings suggest that DevSecOps is essential for building resilient, secure, and scalable software systems in today’s fast-paced digital environment.

DOI: https://doi.org/10.5281/zenodo.19666850

Published by: vikaspatanker

Authors: H. M. Pawar, Kawar Arpita Chandrashekhar, Aher Vaishnavi Sanjay, Birari Prafull Pravin, Jadhav Rushikesh Hiraman

Abstract: The increasing demand for reliable and efficient energy storage systems has led to the widespread use of lithium-ion batteries in various applications such as electric vehicles, renewable energy systems, and portable electronics. However, these batteries are highly sensitive to conditions like overcharging, over-discharging, overheating, and short circuits, which can reduce their lifespan and pose safety risks. This paper presents a Smart Lithium-Ion Battery Monitoring, Protection, and Automatic Switching System designed to enhance battery performance and safety. The proposed system continuously monitors key parameters such as voltage, current, and temperature using embedded sensors and a microcontroller-based control unit. It incorporates protection mechanisms to prevent hazardous conditions and ensures optimal battery operation. Additionally, an automatic switching feature is implemented to seamlessly transition between power sources or backup batteries during faults or low charge conditions. The system improves reliability, efficiency, and longevity of lithium-ion batteries while minimizing human intervention. Experimental results demonstrate the effectiveness of the proposed system in real-time monitoring and protection, making it suitable for modern energy management applications.

DOI: https://doi.org/10.5281/zenodo.19666788

Published by: vikaspatanker

Authors: Putri Anggraini

Abstract: Network anomaly detection has become a critical component of modern cybersecurity, driven by the increasing complexity and scale of network infrastructures. Traditional rule-based and signature-based detection methods are often insufficient to identify sophisticated and evolving cyber threats. This study explores AI-based approaches for network anomaly detection, emphasizing the use of machine learning (ML) and deep learning (DL) techniques to identify unusual patterns and behaviors in network traffic. It examines various models such as supervised, unsupervised, and semi-supervised learning, along with advanced techniques including neural networks, clustering algorithms, and autoencoders. The paper also highlights the role of real-time data processing, feature engineering, and big data analytics in enhancing detection accuracy and responsiveness. Applications across sectors such as healthcare, finance, and cloud computing are discussed to demonstrate the effectiveness of AI-driven anomaly detection systems. Furthermore, the study addresses key challenges including high false positive rates, data imbalance, scalability, and privacy concerns, and proposes solutions such as hybrid models, adaptive learning, and explainable AI. The findings suggest that AI-based approaches significantly improve the efficiency, accuracy, and adaptability of network anomaly detection systems in dynamic and distributed environments.

DOI: https://doi.org/10.5281/zenodo.19666655

Published by: vikaspatanker

Authors: Fajar Nugroho

Abstract: Intelligent automation has emerged as a transformative force in enterprise IT operations, combining artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to streamline and optimize complex workflows. This study provides a comprehensive overview of intelligent automation and its impact on modern IT operations, including infrastructure management, incident response, service delivery, and system monitoring. By integrating AI-driven analytics with automation tools, organizations can achieve proactive issue detection, predictive maintenance, and faster resolution of operational challenges. The paper explores key technologies such as AIOps, natural language processing (NLP), and cognitive automation, highlighting their role in enhancing decision-making and reducing human intervention. It also examines practical applications across industries, including healthcare, finance, and cloud-based enterprises. Furthermore, the study addresses challenges such as integration complexity, data quality, skill gaps, and governance concerns, along with strategies to overcome them. The findings emphasize that intelligent automation is essential for improving efficiency, reducing operational costs, and enabling scalable, resilient IT operations in a rapidly evolving digital landscape.

DOI: https://doi.org/10.5281/zenodo.19666617

Published by: vikaspatanker

Authors: Sri Wahyuni

Abstract: The rapid advancement of the Internet of Things (IoT) has significantly transformed the way devices, systems, and services interact within modern digital ecosystems. When integrated with cloud computing, IoT systems gain enhanced capabilities in terms of scalability, storage, processing power, and real-time analytics. This analytical study explores the integration of IoT with cloud systems, focusing on architectural models, communication protocols, data management strategies, and system performance. It examines how cloud platforms enable efficient handling of massive data generated by IoT devices and facilitate intelligent decision-making through advanced analytics and machine learning techniques. The study also highlights key application domains such as smart homes, healthcare, industrial automation, transportation, and smart cities, where IoT-cloud integration plays a critical role. Furthermore, it addresses major challenges including data security, latency, interoperability, and bandwidth limitations, and discusses potential solutions such as edge computing, fog computing, and enhanced security frameworks. The findings emphasize that the synergy between IoT and cloud computing is essential for building scalable, reliable, and intelligent systems capable of supporting next-generation digital services and innovations.

DOI: https://doi.org/10.5281/zenodo.19666543

Published by: vikaspatanker

Authors: Karthikeyan D, Dhanush D, Harikrishnan S, Jagadesh J, Jagan K

Abstract: Varicose veins are a prevalent vascular disorder caused by weakened vein walls and malfunctioning valves, resulting in improper blood circulation and vein enlargement in the lower extremities. Early identification and timely intervention are essential to prevent complications such as venous ulcers and chronic discomfort. This paper presents a wearable healthcare system designed to detect and manage varicose vein conditions using a concurrent sensor network integrated with a heat treatment module. The proposed system employs multiple sensors, including photoplethysmography (PPG), temperature, infrared, and pressure sensors, to acquire physiological data related to blood circulation and skin temperature variations. The collected signals are processed using a microcontroller-based system that performs real-time analysis and identifies abnormal vascular patterns. Upon detecting irregularities, the system activates a controlled heat therapy module to improve blood flow and reduce discomfort. The integration of sensing and therapeutic functionality enables continuous monitoring and immediate intervention, enhancing patient convenience and reducing dependency on hospital visits. The proposed framework demonstrates the effectiveness of IoT-based wearable systems in improving vascular health monitoring and providing automated therapeutic response for varicose vein management.

Published by: vikaspatanker

Authors: Ayush Patel, Sangeeta Vhatkar, Namdeo Badhe

Abstract: AI and machine learning are shaping up drug discovery and it is about time. The old way- slow, expensive and full of dead-ends- are outdated. Tools like deep learning, graph neural networks, GANs and reinforcement learning are stepping up. These tools actually help scientists spot new targets, sift through virtual libraries for promising compounds, predict how molecules will behave, dream up brand new drug designs, find fresh uses for old drugs and even streamline clinical trials. Graph models, in particular, shine because they get the complicated shape and connections in molecules. These all let researchers simulate how tiny structures interact in the messy reality of biology. Generative AI pushes boundaries even further by designing all sorts of molecules- each tailored for certain properties- across an almost endless chemical universe. Technology is making and creating waves everywhere: cancer, heart conditions, brain disorders, infections-you name it. Across the board, the results are better predictions, smarter trade-offs, more molecular variety and a smoother path from lab to clinic. Of course, it’s not all smooth sailing. Challenges remain like messy data, black-box designing making, regulatory headaches and the tricky business of converting code into medicine. But even with those bumps, AI-powered drug discovery isn’t another upgrade. It is a real-shift: more data-driven, more scalable and a lot more personal. The evidence keeps piling up-AI is speeding up therapeutic breakthroughs and rewriting the future position of medicine, one algorithm at a time.

DOI: https://doi.org/10.5281/zenodo.19663484

Published by: vikaspatanker