Category Archives: Uncategorized

Authors: R.Ranjith, S.Sudhakar, A.Adhithya, Dr. T. Sengolrajan

Abstract: This project develops a wirelessly connected hair drying system that automatically adjusts airflow speed based on real-time hair moisture detection through integration with existing hair dryer units. The system employs ESP32 microcontroller as the central processing unit with built-in Wi-Fi capability for cloud connectivity and mobile application interface. Capacitive moisture sensors continuously monitor hair wetness levels and transmit data to the ESP32 which processes the information through adaptive algorithms. DS18B20 temperature sensors monitor thermal output while solid-state relays control the heating element through PWM signals. Motor speed regulation utilizes to modulate AC motor performance across three operational levels high speed for very wet hair conditions, medium speed for moderately damp hair and low speed for nearly dry hair conditions. The touch control interface integrates mounted on the dryer surface for manual operation while the mobile application communicates through Firebase cloud platform enabling remote parameter adjustment. An OLED display module presents real-time operational data including moisture levels and temperature readings. The integration process involves mounting the moisture sensor near the dryer nozzle, installing temperature sensors adjacent to heating elements and housing the ESP32 module within the dryer handle.

DOI: https://doi.org/10.5281/zenodo.19675047

Published by: vikaspatanker

Authors: Mrs. S. Revathi, M. Aarthi Sree, N.Deepika, K.Rajalakshimi

Abstract: Medication management plays an important role in maintaining good health, especially for elderly individuals and patients undergoing long-term treatment. Forgetting medication times or improper organization of medicines can lead to serious health issues. A smart medicine box is developed to assist users in managing daily medication schedules in an efficient and reliable manner. The system operates using a microcontroller (Arduino) integrated with a real time clock to monitor predefined medication timings and generate timely reminders. Multiple medicine compartments are provided to store different medicines separately, reducing the chances of confusion and incorrect usage. Visual and audio alerts notify users at scheduled times, ensuring regular intake of medicines. The system also monitors medicine availability and provides alerts when medicine levels become low, helping users refill medicines on time. Simple controls and a user- friendly interface make the system suitable for home use without requiring technical knowledge. The smart medicine box enhances medication adherence, improves patient safety, and reduces dependence on caregivers. Such a system is especially useful in households with elderly people and patients requiring continuous medication, offering an effective solution for organized and timely medicine management.

DOI: https://doi.org/10.5281/zenodo.19672584

Published by: vikaspatanker

Authors: Jiya Bhatt, Manthan Khopkar

Abstract: Digital surveillance has emerged as an integral feature of governance in contemporary India. The desire for national security, prevention of crime, distribution of welfare services, and administrative efficiency has prompted the Indian state to increasingly adopt digital technologies in the areas of governance. The rapid expansion of digital technologies such as biometric technologies, facial recognition technologies, and extensive communication interception technologies has enabled the Indian state to increasingly use digital surveillance. The use of such technologies has not only provided efficiency in governance but has also posed significant challenges to civil liberties such as the right to privacy, freedom of expression, and the right to due process. The paper seeks to examine the evolution of digital surveillance in India, the constitutional basis of digital surveillance in India, and the implications of digital surveillance on civil liberties in India. The paper seeks to examine the implications of digital governance in India on the constitutional values of a democratic society. The paper seeks to examine the implications of digital governance in India on the constitutional values of a democratic society. The paper seeks to examine the implications of digital governance in India on the constitutional values of a democratic society.

DOI: https://doi.org/10.5281/zenodo.19672251

Published by: vikaspatanker

Authors: Babandi Usman, Salim Ahmad, Zahraddeen Safyanu

Abstract: Student academic failure remains a persistent challenge in higher education, particularly in developing countries where late identification of at-risk students limits timely intervention. Recent advances in Educational Data Mining and Learning Analytics have enabled predictive modelling of student performance; however, many existing models suffer from poor interpretability, data imbalance, and limited integration of behavioral and socio-economic variables. This study presents a comprehensive review and synthesis aimed at guiding the development of an enhanced algorithm for student failure rate analysis. A systematic review methodology was employed, involving structured literature collection, screening, categorization of predictive techniques, and comparative analysis of statistical, machine learning, ensemble, and deep learning approaches. Algorithms were evaluated using established performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC, alongside qualitative criteria such as interpretability, scalability, and real-time applicability. The analysis reveals that while ensemble and deep learning models achieve superior predictive accuracy, they often lack transparency and struggle with imbalanced educational datasets. Based on these findings, the research proposes a hybrid and explainable predictive framework that integrates ensemble learning, neural networks, imbalance-handling techniques, and explainable AI methods. The review demonstrates that hybrid approaches provide the most promising balance between accuracy, interpretability, and early detection capability. The major contribution of this research lies in synthesizing fragmented literature into a unified framework for enhanced student failure prediction, identifying critical research gaps, and establishing a methodological foundation for developing a scalable, interpretable, and real-time predictive system to support data-driven academic interventions.

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Published by: vikaspatanker

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