Category Archives: Uncategorized

Authors: Sheri Srujan Reddy, Thota Ramanna Dora Prabhas, Mancholla Ranateja, Assistant Professor Dr. P Kiran Kumar

Abstract: Traditional control surfaces on aircraft have been based on rigidly hinged flap sections that create unavoidable geometric discontinuities. These generate early flow separation and parasite drag, hindering aerodynamic performance in different flight conditions. This study explores the application of camber morphing, a biological concept involving wing deformation similar to those seen in bird-like flying organisms. The main goal of this study was to develop an adaptable mechanism, capable of changing the average camber line of the airfoil while keeping its structural integrity intact. The focus is put on a “Fishbone Active Camber” (Fish BAC), or [add name of the mechanism used, for instance, SMA or Rib-Linkage] based structure which replaces a hinge mechanism at the rear-spar position of the wing with a continuous flexible skin allowing for an even pressure distribution along the wingspan. The method involved a two-step approach. First, numerical simulations were carried out using an omega SST turbulence model to compare the aerodynamic parameters of the standard NACA 2412 airfoil with a morphing one. It is evident that the morphing wing has successfully reduced pressure drag significantly by removing the "hinge-gap" problem. More precisely, when the Angle of Attack (AOA) is 6 degrees, the morphing wing has shown a Lift-to-Drag ratio improvement of about 12 to 15 percent over the conventional flaps wing system. Also, flow visualization proved that the onset of turbulence occurred much later, thus broadening the aircraft's range of efficient flight.

DOI: https://doi.org/10.5281/zenodo.20706107

Published by: vikaspatanker

Authors: Mr. Hemant Vanjari, Mr. Jay Deshmukh, Assistant Professor Ms.Pratibha Makar, Dr.Vijaykumar Kale, Dr.Mahesh Thakre

Abstract: Lately, more people pay notice to plant-based beauty products – main reason being they tend to be gentler, work well, rarely cause trouble unlike lab-made versions. This research zeroes in on crafting and testing a multi-plant shampoo made entirely from nature's lineup: Amla joins shikakai, those mix with soap nuts while bhringraj slips in beside hibiscus; fenugreek seeds blend with rice extract, stick amaltas pairs up with flaxseeds, then rosemary teams with aloe vera plus curry leaves tag along too. Long before labs existed, these plants earned rep for helping hair grow stronger, cutting down flakes, keeping strands from dropping, boosting scalp condition, adding glow to locks. Put together with earth-friendly carriers, the mix faced checks on looks, acidity level, thickness, how rich the bubbles get, if gunk spreads out when washed, how fast water soaks into fabric, pull at liquid surfaces, even how steady it stays over time. Results? Cleans thoroughly, makes foam just fine, hits the right acid balance, conditions like a charm – all without making scalps itch. From roots up, plant-based mix fed each strand what it needed. Hair grew stronger, smoother – no harsh stuff involved. Results showed this blend worked just as well as lab-made options. Cost stayed low, safety held steady. Folks using it daily found fewer issues than expected. Not one person reported serious irritation. Science backed its role in regular

Published by: vikaspatanker

Authors: R A Shasank

Abstract: In the rapidly evolving landscape of financial technology, the imperative for model interpretability often conflicts with the pursuit of predictive accuracy. Financial institutions heavily rely on automated credit scoring models; however, the lack of transparency in conventional "black-box" approaches—such as deep neural networks and complex ensemble methods—poses significant regulatory and ethical risks. This paper introduces a hybrid credit risk assessment framework that bridges the gap between performance and interpretability. By leveraging First-Order Inductive Learners (specifically the RIPPER algorithm), the proposed model transforms raw financial data into a structured set of human-auditable domain rules. Furthermore, we implement a novel "Abstention-Driven Human Audit" layer, which identifies cases with marginal prediction confidence and redirects them for manual expert review. The experimental analysis, conducted on standard benchmark datasets, demonstrates that this architecture maintains competitive predictive power while providing a clear, logical rationale for every automated decision. The results highlight that the integration of rule-based logic not only fosters regulatory compliance but also enhances stakeholder trust in automated financial systems. This study contributes a scalable, transparent, and robust alternative for modern credit risk management.

Published by: vikaspatanker

Authors: Nikita Khawase, Nishant Kadam, Swarup Chaudhari, Rushikesh Patil, Hrishikesh Kakade

Abstract: Automated vehicle monitoring is a cornerstone of modern security infrastructure, essential for maintaining safety and operational efficiency in high-traffic environments such as industrial complexes, gated communities, and public facilities. Traditional manual surveillance methods are frequently plagued by human error, significant labor costs, and operational bottlenecks that compromise the integrity of security protocols. This paper presents a robust framework for an automated Vehicle Entry Monitoring System (VEMS) utilizing the state-of-the-art You Only Look Once (YOLO) object detection architecture. The proposed system integrates real-time video stream processing with advanced deep learning models to achieve high-speed detection and classification of various vehicle types, including cars, trucks, and motorcycles. A critical component of the methodology involves the integration of Optical Character Recognition (OCR) and tracking algorithms, such as DeepSORT, to automatically extract alphanumeric license plate data and maintain unique vehicle identities across consecutive frames. This integration enables the creation of a comprehensive, searchable database that cross-references detected plates with authorized whitelists for proactive access control. Experimental results demonstrate that the system ensures near 100% operational uptime by automating the data trail for security auditing and regulatory compliance. The framework provides a scalable solution for intelligent transportation management, significantly reducing manpower dependency while enhancing the reliability of entry logs. By combining real-time detection overlays with a centralized monitoring dashboard, this research offers a sophisticated, data-driven approach to facility security, fostering safer and more efficient urban mobility environments.

Published by: vikaspatanker

Authors: Jatin Nikhade, Professor Ar. Vaishali Sharma

Abstract: Vernacular architecture offers a viable and underutilized framework for addressing the sustainability failures of contemporary construction in India. Through a comparative literature review and analysis of documented case studies from Kerala, Rajasthan, Assam, and Ladakh, this study finds that traditional building systems consistently achieve lower embodied energy, superior passive thermal performance, and stronger cultural continuity than their modern counterparts — without reliance on mechanical systems. The study also critically examines the limitations of vernacular methods, including structural vulnerability, maintenance demands, and inability to scale in rapidly urbanizing contexts. It concludes that a hybrid model — integrating vernacular passive design, traditional materials upgraded through modern engineering, and digital fabrication tools — presents the most feasible pathway to a sustainable built environment in India.</

Published by: vikaspatanker

Authors: Deepika Kashyap, Durgesh Singh, Harsh Pandey, Nikhil Ahirwar, Nikhil Gautam, Nishant Kumar, Rahul Singh

Abstract: The increasing use of electric bicycles (E-bikes) in rural areas has created a need for effective security solutions to prevent theft and unauthorized access. Conventional locking mechanisms often provide limited protection and may not alert owners during theft attempts. This paper presents an E-Bike Lock and Anti-Theft Alarm System for Rural Areas that combines electronic locking, motion detection, alarm generation, and wireless communication technologies. The proposed system uses a microcontroller, sensors, GSM/Bluetooth modules, and an electronic locking mechanism to secure the E-bike. When unauthorized movement or tampering is detected, the system activates an alarm and sends notifications to the owner. Performance parameters such as detection accuracy, response time, power consumption, and communication reliability are analyzed. Experimental results indicate that the system provides enhanced security, low power consumption, and improved protection against theft in rural environments.

DOI: http://doi.org/10.5281/zenodo.20701189

Published by: vikaspatanker

Authors: Deendayal Dhakad, Deependra Rajak, Navneet Dhakad, Pooja Kewat, Ritik Dhakad, Dr. Prakhar Singh Bhadoria

Abstract: The advancement of smart home technologies has transformed the way people interact with household appliances. Conventional home automation systems often require manual operation or mobile applications, which may not be convenient for all users. This paper proposes an Intelligent Household System Using Voice Tag that enables users to control household devices through voice commands. The system utilizes voice recognition technology, microcontrollers, wireless communication modules, and smart sensors to automate home appliances efficiently. Voice commands are processed and converted into control signals that operate electrical devices such as lights, fans, air conditioners, and security systems. The proposed system improves user convenience, enhances accessibility for elderly and disabled individuals, and supports energy-efficient operation. Performance parameters such as voice recognition accuracy, response time, communication reliability, and energy consumption are analyzed. The study concludes that voice-based intelligent household systems offer a practical and user-friendly solution for modern smart homes.

DOI: http://doi.org/10.5281/zenodo.20700978

Published by: vikaspatanker

Authors: Dr Ansari Pulickal Abdul Azeez, Farooq Sajjad

 

Abstract: Digitization of business education at an unprecedented rate has made available to educators large amounts of student interaction data that can inform data-driven learning interventions. In this paper, we propose an Artificial Intelligence-driven Learning Analytics (AI-LA) system architecture, which incorporates multi-stream data sources (Learning Management System (LMS) logs, clickstream analysis, test/assignment submissions, and engagement data) to model, explain, and improve student engagement and performance. Our approach leverages a novel combination of techniques that include a Temporal Fusion Transformer (TFT) model for sequential behavior prediction, SHapley Additive exPlanations (SHAP) for interpretable feature importance, and reinforcement learning (RL) engine for personalized intervention recommendations. Our model was tested using longitudinal data from 3,400+ business management students in 24 courses over three academic years (2022-2025). It predicted at-risk students with up to 89.5% accuracy si

DOI: https://doi.org/10.5281/zenodo.20700051

 

Published by: vikaspatanker

Authors: Prerna Charis J

Abstract: Sign Language Recognition (SLR) has emerged as an important research area at the intersection of computer vision and deep learning, and human and machine interaction with an objective of enabling effective communication between deaf and hearing communities. Recent advances in deep learning have improved the performance of vision-based Sign Language Recognition systems, particularly by using hybrid architectures that combine spatial features extraction and temporal sequence modelling. The goal of this review is to provide a overview of the recent developments in hybrid Vision-based Sign Language Recognition and to examine the advantages, limitation and practical deployment challenges of the current approaches. This paper provides a systematic review of the literature, the surveyed methods broadly classified into CNN-LSTM architectures, Transformer-based models and multimodal integrated frameworks which integrates visual and skeletal information. This review further investigates critical challenges affecting the deployment in real-world scenarios which includes domain shift, data scarcity, co-articulation, sign ambiguity and computational constrain. We will also discuss about emerging research direction such as self-supervised learning, cross-linguistic transfer learning, generative domain adaptation, multimodal bio signal integration, and community-centered dataset development. This survey also highlights the significant progress achieved in continuous sign language recognition while identifying the remaining technical and practical barriers that must be removed to develop robust, scalable, and user-independent SLR systems capable of operating in real-world environments.

Published by: vikaspatanker

Authors: Rushikesh Pawar, Vijaykumar Kale, Sakshi Mane, Mahesh Thakare, Vaibhav Narwade

Abstract: Traditional herbal medicines serve as a primary healthcare pillar for approximately 80% of the population in various Asian and African nations. Despite their extensive experiential evidence and therapeutic benefits, conventional herbal formulations face significant pharmacokinetic limitations. These include poor aqueous solubility, unstable gastrointestinal pH degradation, high presystemic metabolism, and an inability to cross lipid biomembranes effectively, often resulting in sub-therapeutic blood levels.[1] Modern quality control has transitioned from single-marker assays to comprehensive metabolic profiling using High-Performance Liquid Chromatography coupled with Mass Spectrometry (HPLC-MS) and genomic DNA barcoding for precise species identification. Concurrently, international bodies (including the WHO, ASEAN, EU, and FDA) are collaborating to harmonize regulatory frameworks. To enhance therapeutic efficacy, nanotechnology is being deployed to engineer nano-phytomedicines. Various carrier systems including polymeric nanoparticles, solid lipid nanoparticles, liposomes, nanoemulsions, and phytosomes are evaluated. Notably, while liposomes encapsulate extracts within an aqueous core or lipid bilayer, phytosomes chemically anchor phytochemicals directly to phospholipid head groups, drastically improving lipophilicity and membrane permeation.[2] Incorporating plant actives into nanostructured matrices significantly optimizes their hydrophilic-lipophilic balance. This structural modification provides sustained release, shields molecules from chemical degradation, minimizes off-target toxicity (e.g., localized accumulation of chemotherapeutics in healthy tissues), and increases bioavailability. However, transitioning these formulations from bench to industrial scale introduces complex challenges, including maintaining uniform encapsulation efficiency within multi-component plant extracts, preventing nanoparticle aggregation driven by high surface energy, and satisfying stringent regulatory safety assays regarding tissue accumulation.[3].

Published by: vikaspatanker