International Journal of Science, Engineering and TechnologyA Case Study On CFDs Superiority In Isolating Centrifugal And Coriolis Effects In Rotating Slurry Flows.
Authors: Aditya Singh, Rajveer Yadav, Shreyash Tamrakar, Abhishek Kumar Gupta
Abstract: This study examines the advantages of Computational Fluid Dynamics (CFD) over experimental methods for analyzing rotating slurry flows, particularly in isolating centrifugal and Coriolis effects, which are challenging to study empirically. We conduct CFD simulations of dense solid-liquid flows in a rotating straight channel, incorporating governing equations with explicit terms for centrifugal and Coriolis forces, then validate the model against stationary-channel experimental data due to the lack of rotating-flow benchmarks. The results demonstrate that centrifugal forces significantly influence flow behavior at higher rotation rates and larger particle sizes, an effect that cannot be isolated experimentally. Moreover, by selectively disabling the centrifugal term in the governing equations, we reveal distinct flow field variations, highlighting CFD's unique capability to decouple and analyze individual physical mechanisms. Furthermore, parametric studies under varying rotation rates and particle densities uncover nonlinear dependencies in velocity and concentration profiles, providing important insights to industrial applications such as centrifugal pumps and turbines. The study underscores CFD's superiority in scenarios where experimental methods are impractical or insufficient, enabling detailed exploration of complex flow phenomena that would otherwise remain inaccessible. These findings not only validate CFD as a reliable alternative to experimentation but also expand its potential for advancing fundamental and applied research in rotating fluid systems.
DOI: http://doi.org/10.5281/zenodo.20175247
A Case Study On CFDs Superiority In Isolating Centrifugal And Coriolis Effects In Rotating Slurry Flows.
Authors: Aditya Singh, Rajveer Yadav, Shreyash Tamrakar, Abhishek Kumar Gupta
Abstract: This study examines the advantages of Computational Fluid Dynamics (CFD) over experimental methods for analyzing rotating slurry flows, particularly in isolating centrifugal and Coriolis effects, which are challenging to study empirically. We conduct CFD simulations of dense solid-liquid flows in a rotating straight channel, incorporating governing equations with explicit terms for centrifugal and Coriolis forces, then validate the model against stationary-channel experimental data due to the lack of rotating-flow benchmarks. The results demonstrate that centrifugal forces significantly influence flow behavior at higher rotation rates and larger particle sizes, an effect that cannot be isolated experimentally. Moreover, by selectively disabling the centrifugal term in the governing equations, we reveal distinct flow field variations, highlighting CFD's unique capability to decouple and analyze individual physical mechanisms. Furthermore, parametric studies under varying rotation rates and particle densities uncover nonlinear dependencies in velocity and concentration profiles, providing important insights to industrial applications such as centrifugal pumps and turbines. The study underscores CFD's superiority in scenarios where experimental methods are impractical or insufficient, enabling detailed exploration of complex flow phenomena that would otherwise remain inaccessible. These findings not only validate CFD as a reliable alternative to experimentation but also expand its potential for advancing fundamental and applied research in rotating fluid systems.
DOI: http://doi.org/10.5281/zenodo.20175247
A Case Study On CFDs Superiority In Isolating Centrifugal And Coriolis Effects In Rotating Slurry Flows.
Authors: Aditya Singh, Rajveer Yadav, Shreyash Tamrakar, Abhishek Kumar Gupta
Abstract: This study examines the advantages of Computational Fluid Dynamics (CFD) over experimental methods for analyzing rotating slurry flows, particularly in isolating centrifugal and Coriolis effects, which are challenging to study empirically. We conduct CFD simulations of dense solid-liquid flows in a rotating straight channel, incorporating governing equations with explicit terms for centrifugal and Coriolis forces, then validate the model against stationary-channel experimental data due to the lack of rotating-flow benchmarks. The results demonstrate that centrifugal forces significantly influence flow behavior at higher rotation rates and larger particle sizes, an effect that cannot be isolated experimentally. Moreover, by selectively disabling the centrifugal term in the governing equations, we reveal distinct flow field variations, highlighting CFD's unique capability to decouple and analyze individual physical mechanisms. Furthermore, parametric studies under varying rotation rates and particle densities uncover nonlinear dependencies in velocity and concentration profiles, providing important insights to industrial applications such as centrifugal pumps and turbines. The study underscores CFD's superiority in scenarios where experimental methods are impractical or insufficient, enabling detailed exploration of complex flow phenomena that would otherwise remain inaccessible. These findings not only validate CFD as a reliable alternative to experimentation but also expand its potential for advancing fundamental and applied research in rotating fluid systems.
DOI: http://doi.org/10.5281/zenodo.20175247
A Case Study On CFDs Superiority In Isolating Centrifugal And Coriolis Effects In Rotating Slurry Flows.
Authors: Aditya Singh, Rajveer Yadav, Shreyash Tamrakar, Abhishek Kumar Gupta
Abstract: This study examines the advantages of Computational Fluid Dynamics (CFD) over experimental methods for analyzing rotating slurry flows, particularly in isolating centrifugal and Coriolis effects, which are challenging to study empirically. We conduct CFD simulations of dense solid-liquid flows in a rotating straight channel, incorporating governing equations with explicit terms for centrifugal and Coriolis forces, then validate the model against stationary-channel experimental data due to the lack of rotating-flow benchmarks. The results demonstrate that centrifugal forces significantly influence flow behavior at higher rotation rates and larger particle sizes, an effect that cannot be isolated experimentally. Moreover, by selectively disabling the centrifugal term in the governing equations, we reveal distinct flow field variations, highlighting CFD's unique capability to decouple and analyze individual physical mechanisms. Furthermore, parametric studies under varying rotation rates and particle densities uncover nonlinear dependencies in velocity and concentration profiles, providing important insights to industrial applications such as centrifugal pumps and turbines. The study underscores CFD's superiority in scenarios where experimental methods are impractical or insufficient, enabling detailed exploration of complex flow phenomena that would otherwise remain inaccessible. These findings not only validate CFD as a reliable alternative to experimentation but also expand its potential for advancing fundamental and applied research in rotating fluid systems.
DOI: http://doi.org/10.5281/zenodo.20175247
A Case Study On CFDs Superiority In Isolating Centrifugal And Coriolis Effects In Rotating Slurry Flows.
Authors: Aditya Singh, Rajveer Yadav, Shreyash Tamrakar, Abhishek Kumar Gupta
Abstract: This study examines the advantages of Computational Fluid Dynamics (CFD) over experimental methods for analyzing rotating slurry flows, particularly in isolating centrifugal and Coriolis effects, which are challenging to study empirically. We conduct CFD simulations of dense solid-liquid flows in a rotating straight channel, incorporating governing equations with explicit terms for centrifugal and Coriolis forces, then validate the model against stationary-channel experimental data due to the lack of rotating-flow benchmarks. The results demonstrate that centrifugal forces significantly influence flow behavior at higher rotation rates and larger particle sizes, an effect that cannot be isolated experimentally. Moreover, by selectively disabling the centrifugal term in the governing equations, we reveal distinct flow field variations, highlighting CFD's unique capability to decouple and analyze individual physical mechanisms. Furthermore, parametric studies under varying rotation rates and particle densities uncover nonlinear dependencies in velocity and concentration profiles, providing important insights to industrial applications such as centrifugal pumps and turbines. The study underscores CFD's superiority in scenarios where experimental methods are impractical or insufficient, enabling detailed exploration of complex flow phenomena that would otherwise remain inaccessible. These findings not only validate CFD as a reliable alternative to experimentation but also expand its potential for advancing fundamental and applied research in rotating fluid systems.
DOI: http://doi.org/10.5281/zenodo.20175247
A Case Study On CFDs Superiority In Isolating Centrifugal And Coriolis Effects In Rotating Slurry Flows.
Authors: Aditya Singh, Rajveer Yadav, Shreyash Tamrakar, Abhishek Kumar Gupta
Abstract: This study examines the advantages of Computational Fluid Dynamics (CFD) over experimental methods for analyzing rotating slurry flows, particularly in isolating centrifugal and Coriolis effects, which are challenging to study empirically. We conduct CFD simulations of dense solid-liquid flows in a rotating straight channel, incorporating governing equations with explicit terms for centrifugal and Coriolis forces, then validate the model against stationary-channel experimental data due to the lack of rotating-flow benchmarks. The results demonstrate that centrifugal forces significantly influence flow behavior at higher rotation rates and larger particle sizes, an effect that cannot be isolated experimentally. Moreover, by selectively disabling the centrifugal term in the governing equations, we reveal distinct flow field variations, highlighting CFD's unique capability to decouple and analyze individual physical mechanisms. Furthermore, parametric studies under varying rotation rates and particle densities uncover nonlinear dependencies in velocity and concentration profiles, providing important insights to industrial applications such as centrifugal pumps and turbines. The study underscores CFD's superiority in scenarios where experimental methods are impractical or insufficient, enabling detailed exploration of complex flow phenomena that would otherwise remain inaccessible. These findings not only validate CFD as a reliable alternative to experimentation but also expand its potential for advancing fundamental and applied research in rotating fluid systems.
DOI: http://doi.org/10.5281/zenodo.20175247
Performance, Mechanisms, And Optimization Of Glass Fiber– Reinforced Geopolymer Concrete: A Comprehensive Review
Authors: Anil Kumar, Vivek Pahuja
Abstract: Geopolymer concrete (GPC) — an emerging low-carbon alternative to Portland cement concrete — has attracted considerable attention for its reduced CO₂ footprint and excellent chemical resistance. The growing demand for sustainable construction materials has intensified research into geopolymer concrete as an environmentally friendly alternative to conventional Portland cement concrete. Although geopolymer concrete exhibits excellent compressive strength, chemical resistance, and thermal stability, its inherently brittle nature and limited tensile performance restrict broader structural applications. The incorporation of glass fibers has emerged as an effective strategy to enhance the mechanical performance and durability of geopolymer matrices. This review paper presents a comprehensive synthesis of existing research on glass fiber–reinforced geopolymer concrete (GFRGPC), with a focus on performance characteristics, underlying reinforcement mechanisms, and optimization strategies. The effects of glass fiber on fresh properties, compressive strength, tensile strength, and flexural behavior. Durability aspects such as alkali resistance of glass fibers, chloride penetration, chemical attack, and high-temperature performance are also reviewed, highlighting both the benefits and limitations of glass fiber incorporation in highly alkaline geopolymer environments. The review identifies optimal ranges of fiber content that maximize mechanical performance while minimizing adverse effects on workability and fiber dispersion. Furthermore, current challenges, including long-term durability of glass fibers, lack of standardized mix design procedures, and variability in experimental methodologies, are discussed. Based on the analyzed literature, future research directions are proposed, focusing on long-term performance evaluation, and life-cycle assessment. The findings demonstrate that, with proper optimization, glass fiber–reinforced geopolymer concrete has strong potential as a high-performance and sustainable material for future structural and infrastructure applications.
DOI: http://doi.org/10.5281/zenodo.20175276
Performance, Mechanisms, And Optimization Of Glass Fiber– Reinforced Geopolymer Concrete: A Comprehensive Review
Authors: Anil Kumar, Vivek Pahuja
Abstract: Geopolymer concrete (GPC) — an emerging low-carbon alternative to Portland cement concrete — has attracted considerable attention for its reduced CO₂ footprint and excellent chemical resistance. The growing demand for sustainable construction materials has intensified research into geopolymer concrete as an environmentally friendly alternative to conventional Portland cement concrete. Although geopolymer concrete exhibits excellent compressive strength, chemical resistance, and thermal stability, its inherently brittle nature and limited tensile performance restrict broader structural applications. The incorporation of glass fibers has emerged as an effective strategy to enhance the mechanical performance and durability of geopolymer matrices. This review paper presents a comprehensive synthesis of existing research on glass fiber–reinforced geopolymer concrete (GFRGPC), with a focus on performance characteristics, underlying reinforcement mechanisms, and optimization strategies. The effects of glass fiber on fresh properties, compressive strength, tensile strength, and flexural behavior. Durability aspects such as alkali resistance of glass fibers, chloride penetration, chemical attack, and high-temperature performance are also reviewed, highlighting both the benefits and limitations of glass fiber incorporation in highly alkaline geopolymer environments. The review identifies optimal ranges of fiber content that maximize mechanical performance while minimizing adverse effects on workability and fiber dispersion. Furthermore, current challenges, including long-term durability of glass fibers, lack of standardized mix design procedures, and variability in experimental methodologies, are discussed. Based on the analyzed literature, future research directions are proposed, focusing on long-term performance evaluation, and life-cycle assessment. The findings demonstrate that, with proper optimization, glass fiber–reinforced geopolymer concrete has strong potential as a high-performance and sustainable material for future structural and infrastructure applications.
DOI: http://doi.org/10.5281/zenodo.20175276
Application Of Lean Construction Techniques
Authors: Aryan Prabhakar, Domendra Kumar Dewangan, Megha Sahu, Divya Tamrakar
Abstract: Lean Construction is a systematic approach adapted from lean manufacturing principles to improve performance, reduce waste, optimize resources, and enhance value in construction projects. In traditional construction management, inefficiencies such as rework, schedule delays, and cost overruns are common. Lean Construction techniques — including the Last Planner System (LPS), Just-in-Time delivery, pull scheduling, and waste elimination — focus on minimizing non-value-adding activities and fostering collaborative workflows. This study reviews Lean principles and analyzes outcomes from real implementations, showing measurable benefits including improved planning, time savings, cost control, and increased stakeholder satisfaction. The paper concludes that Lean Construction significantly improves efficiency and project outcomes when properly implemented.
DOI: http://doi.org/10.5281/zenodo.20175289
Design And Performance Comparison Of 32-Bit Risc-V Alu Accelerators: From Combinational To Pipelined Architecture With Flag Support
Authors: Debika Rani Sahu, Tapas Kumar Patra, Debi Prasad Dash
Abstract: The demand for more efficient and high-performing computing in embedded and edge systems has led to the creation of application-specific hardware acceleration on FPGAs. This includes an integrated two-stage pipelined Arithmetic Logic Unit (ALU) accelerator with an AXI4-Lite interface, designed for use in Zynq-based processing systems. Two design variants were explored: a baseline non-piped ALU and a pipelined ALU, both offering support for Zero, Carry, Overflow, and Negative flags. The proposed accelerator is implemented on the Xilinx PYNQ-Z2 FPGA board. The processing system communicates with the programmable logic through an AXI-based memory-mapped interface. A Python-based layer is used to set up, manipulate, check, and verify the hardware module, which facilitates prototype development and testing. The pipelined architecture balances manageable design complexity with computational throughput by overlapping instruction execution stages. Experimental evaluation shows that the pipelined design achieves 150 MHz and 150 MOPS in operating frequency and throughput, respectively. This demonstrates a 50% improvement over the non-pipelined version. The implementation incurs a modest resource usage penalty of 35% LUTs, and the overall power consumption stays below 0.1 W. These results highlight how effective pipelining is in enhancing ALU performance on FPGA platforms. It also confirms its suitability for high-performance embedded applications that need efficient hardware acceleration.
DOI: http://doi.org/10.5281/zenodo.20175302
A Comprehensive Comparative Analysis Of WEKAand Tanagra For Higher Education Data Mining
Authors: Dr. Rama Soni
Abstract: Now in days higher education is very important for development of our country as well as for the bright future of individual one . That’s why the performance of students is abigdeal. Every education center is claiming that it gives quality education and best environment toits students. [1] But outcomes are not satisfying to our expectations so many researchers aretrying to research in this area to get accurate result of student’s accuracy. [2] With this research we will calculated the accuracy and analyze the student performance withthe help of dif erent algorithms like K-NN, Decision Tree, Random Forest and Naive Bayes andwe will compare that which algorithm give accurate result from data set and which one is best. With this research paper students and educational institutions can predict and increase self performance as required.
DOI: http://doi.org/10.5281/zenodo.20175328
Heatmaps : A Modern Visualization Technology
Authors: Dr. Vijay R. Tripathi
Abstract: In the contemporary data-driven landscape, the ability to rapidly interpret complex information is paramount. Heatmaps have emerged as a powerful and intuitive visualization technology that translates multidimensional numerical matrices into a visual language of color. This paper provides a comprehensive exploration of heatmaps as a modern visualization tool. It delineates the working principle, from data collection and matrix structuring to color encoding and visual rendering. The study categorizes various types of heatmaps—including cluster, geographic, web, correlation, and temporal—and illustrates their diverse applications across industries such as healthcare, business intelligence, and urban planning through in-depth case studies. The paper also critically examines the advantages and inherent limitations of heatmaps, proposing mitigation strategies. Finally, it discusses the future trajectory of heatmap technology, focusing on its integration with Artificial Intelligence (AI), real-time interactivity, immersive environments, and ethical design, concluding that heatmaps are a cornerstone of modern visual analytics.
DOI: http://doi.org/10.5281/zenodo.20175338
Heat Transfer Characteristics Of Buoyancy Induced Flow In Ducts – An Application To Solar Water Heating System
Authors: Gitesh Kumar, Mr. Homeshwari, Mr. Narendra Suryavanshi
Abstract: The present work deals with experimental studies on heat transfer and flow characteristic for buoyancy induced flow through inclined tubes. The parameters varied during the experimentation are; tube inclination and heat supply. It was found that mass flow rate and heat transfer coefficient increases with increase in heat flux supplied. The flow rate decreases for increase in tube inclination.
DOI: http://doi.org/10.5281/zenodo.20175351
Heat Transfer Characteristics Of Buoyancy Induced Flow In Ducts – An Application To Solar Water Heating System
Authors: Gitesh Kumar, Mr. Homeshwari, Mr. Narendra Suryavanshi
Abstract: The present work deals with experimental studies on heat transfer and flow characteristic for buoyancy induced flow through inclined tubes. The parameters varied during the experimentation are; tube inclination and heat supply. It was found that mass flow rate and heat transfer coefficient increases with increase in heat flux supplied. The flow rate decreases for increase in tube inclination.
DOI: http://doi.org/10.5281/zenodo.20175351
Design And Fabrication Of Solar Powered Pollution Control System: A Performance And Economic Analysis
Authors: Jayant, Ganesh Lal, Ujjwal, Abhishek Kumar Gupta
Abstract: Air pollution is a critical global challenge, particularly in urban and industrial corridors. This study presents the design, fabrication, and performance evaluation of an autonomous solar powered air pollution control system. The prototype integrated a 100 W photovoltaic (PV) panel, a 12V 20Ah battery, and a multistage filtration unit consisting of a pre-filter, HEPA filter, and activated carbon filter. The experimental results indicate a maximum solar power generation of 38.8 W and a battery backup time of 8.5 h. The system achieved a significant reduction in pollutants, with average efficiencies of 70.4% for PM2.5 and 68% for PM10. Economic analysis shows a 40–60% reduction in the total project cost compared to commercial alternatives, demonstrating the feasibility of the system for small-scale industrial and roadside applications.
DOI: http://doi.org/10.5281/zenodo.20175393
Accident And Crime Detection From Surveillance Video Simulation Using YOLO
Authors: Mr. Kunal Kanchankar, Dr. Ravindra Kale
Abstract: The quick growth of the cities and smart cities made the need for surveillance systems that can automatically spot incidents and make the public safer even greater. People are in charge of watching video feeds in the traditional surveillance systems, which can be slow it and make mistakes when there is a lot of footage to watch. Recently advancements in (AI) artificial intelligence, Computer Vision, and deep learning have facilitated automated-systems in the analysis of real-time video streams, enhancing an accuracy of abnormal activity detection. This review paper looks at current research on the AI-based surveillance systems that are meant to find incidents in cities. It talks about the popular methods like (CNN), (RNN), and real-time object detection models like YOLO. The study also reviews available surveillance datasets, evaluation metrics, and implementation approaches used in previous work. A comparison of existing methods highlights their strengths and limitations. The paper also emphasizes the need for integrated multi-incident detection systems capable of improving safety in modern smart cities.
DOI: http://doi.org/10.5281/zenodo.20175429
Real-Time Emergency Vehicle Priority Control Using YOLO-Based Detection In Intelligent Traffic System
Authors: Mr. Rahul Desai, Mr Domendra Kumar Verma, Ms Purnima Dutta
Abstract: Rapid urbanization and increasing vehicle density have made traffic management a major challenge in smart cities, particularly during emergency situations where immediate road access is essential. This paper presents an intelligent emergency traffic handling system that utilizes deep learning, IoT devices, and mobile-based communication to support faster movement of emergency vehicles. The framework uses a YOLO-powered detection algorithm integrated with live video surveillance to recognize ambulances, police vehicles, and fire engines in real time. Once an emergency vehicle is detected, a wireless communication mechanism transfers the information to an IoT-enabled traffic controller that automatically changes traffic signals to ensure priority movement. Simultaneously, an audio announcement module alerts nearby commuters to clear the route. To strengthen coordination, a mobile application sends instant notifications and live location updates to traffic personnel. The system also maintains cloud-based records for monitoring and future traffic analysis. The proposed approach improves emergency response efficiency, minimizes delays caused by congestion, and supports the development of scalable and intelligent urban transportation infrastructure.
DOI: http://doi.org/10.5281/zenodo.20175447
Human Memory Backup AI Based System
Authors: Ms Priyanka Chatterjee, Ms Aparna Parmar, Ms.Purnima Dutta, Mr.Reetesh Sharma
Abstract: It focus on the problem of not remembering each and everything happening in day-to-day life or the best memories that are needed to be remembered or rely on our mind, Human memory is limited and often unreliable, Leading to the loss of best experiences and information specially for dementia patients. This research paper proposes a Memory Backup system that digitally records, stores and retrieves human memories using Artificial Intelligence (AI). This system captures data such as voice, images, location, and time, and organizes it into a structured format. It allows users to recall past memories easily and improves personal productivity and memory retention. The proposed system also focuses on privacy protection through encryption, user control, and secure storage. This technology can be especially useful for individuals with memory loss and for managing daily life things efficiently.
DOI: http://doi.org/10.5281/zenodo.20175461
Smart Healthcare And Lifestyle Prediction Using Logistic Regression: A Classification Approach
Authors: Shail Sahu, Neelam Sahu, Harish Kumar
Abstract: This study introduces a predictive healthcare framework that applies Logistic Regression to assess individual health risks based on lifestyle and physiological indicators. The system incorporates variables such as age, body mass index (BMI), exercise frequency, dietary habits, smoking behavior, and prior medical records to estimate the probability of developing lifestyle-related illnesses. The model was trained and validated on a structured dataset, achieving a classification accuracy of 98.8%. Findings highlight that Logistic Regression, though relatively straightforward compared to more complex algorithms, delivers dependable and interpretable outcomes. Its transparency makes it particularly suitable for healthcare contexts, where understanding the influence of each factor is essential. The proposed approach has potential applications in early risk detection and preventive health planning, supporting clinicians and individuals in making informed decisions.
DOI: http://doi.org/10.5281/zenodo.20175475
AI-Enhanced IoT-Based Fall Detection And Emergency Response System
Authors: Swati Chaitandas Hadke, Ashish Pralhad Katore
Abstract: Falls among the elderly continue to be a big problem since they frequently result in fatalities, serious injuries, and medical consequences. In order to improve real-time monitoring reaction efficiency, this study proposes an Internet of Things-based senior fall detection and alarm system. In order to ensure precise identification, the system uses accelerometer and gyroscope sensors to detect falls using threshold-based algorithms. An emergency button that lets users manually accept or reject notifications helps reduce false alarms. The technology employs Wi-Fi connectivity to quickly send warnings to connected devices in the event of an emergency, warning caregivers or other people in the vicinity. By providing a reliable and timely fall detection and emergency aid solution, this concept increases the efficacy and dependability of elder safety monitoring.
DOI: http://doi.org/10.5281/zenodo.20175491
A Critical Review Of Material Management Practices For Enhancing Building Construction Project Performance
Authors: Yemesh Sahu, Trilokinath, Akansha Ekka
Abstract: Material management is a crucial component of construction project management, as materials significantly influence project cost, quality, and completion time. Inefficient handling, storage, and procurement of construction materials often result in delays, wastage, and cost overruns. This review paper examines various material management strategies adopted in building construction projects, with a focus on planning, inventory control, transportation, storage, and handling of materials. Previous research highlights that effective material coordination from the design phase to project execution can substantially improve productivity and reduce losses. The study emphasizes the importance of adopting systematic inventory control techniques and modern management tools to enhance overall project performance.
DOI: http://doi.org/10.5281/zenodo.20175516
Machine Learning In Healthcare
Authors: Poonam Sharma, Nimi Gautam
Abstract: Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved the ability to detect health emergencies, analyze disease patterns, and understand patient conditions and immune responses. Despite these advancements, there are still concerns about how reliably these models can be applied and interpreted in real-world healthcare environments. However, the adoption of ML-based solutions is growing rapidly. This paper presents an overview of key machine learning methods, including supervised, unsupervised, and reinforcement learning, along with their practical examples. It also explores the use of these techniques in various healthcare domains such as radiology, genomics, electronic health records, and neuro imaging. In addition, the study highlights major challenges, including data privacy and ethical issues, and discusses potential directions for future research and applications.
DOI: http://doi.org/10.5281/zenodo.20178263
Inclusive Digit And Alphabet Recognitionusing Deep Convolutional Neural Networks
Authors: Miss Pratiksha S. Kotkar, Ms. Hemlata Dakhore
Abstract: This paper presents a web-based system for handwritten digit and alphabet recognition designed to support interactive learning. The proposed approach combines Convolutional Neural Networks (CNNs) with real-time computer vision techniques to recognize user input directly within a browser environment. Handwritten input is captured through a digital canvas and processed using OpenCV.js for noise reduction, scaling, and centering, ensuring consistency with the training data. The processed input is then classified using a CNN model implemented in TensorFlow.js, enabling fast and efficient prediction without reliance on external servers. The system is developed using the EMNIST dataset, which includes both digits and alphabets, allowing it to handle a wide range of inputs. Experimental results show that the model achieves high accuracy while maintaining low latency, providing immediate feedback to users. In addition, the application includes a performance tracking mechanism that records user progress over time, supporting continuous learning. The proposed system demonstrates how browser-based artificial intelligence can be used to create accessible and responsive educational tools. By integrating handwriting practice with instant feedback, it offers a practical solution for improving basic literacy and numeracy skills in a digital environment.
DOI:
Recent Developments In Electric Vehicle Onboard Charging Systems: Converter Topologies, Control Techniques, And Emerging Challenges
Authors: Mr. Ashutosh Tiwari, Mr. Khilendra Nirmalkar, Mr, Sachin Patel, Mr. Satyam Manikpuri, Mr.Tushar Sahu, Mr. Ashutosh Tiwari
Abstract: The rapid growth of electric vehicle (EV) adoption has led to a substantial rise in the need for high-performance and dependable charging solutions. Among these, the onboard charger (OBC) plays a vital role by transforming grid-supplied alternating current (AC) into direct current (DC) required for battery charging. Despite ongoing advancements, challenges such as current ripple, power losses due to switching, and thermal constraints continue to affect OBC performance and design efficiency.This review presents an overview of recent progress in EV onboard charging technologies, with particular attention to converter configurations, integrated charging structures, bidirectional power flow capabilities, and methods for mitigating current ripple. In addition, it explores emerging trends including chargers supported by renewable energy sources, security concerns in charging systems, and multifunctional charger designs.The study also highlights the significance of analyzing both high-frequency and low-frequency behaviors to enhance efficiency, maintain power quality, and ensure system reliability. Lastly, it identifies key research gaps—such as issues related to scalability, heat management, fault resilience, and cost-effectiveness—to provide direction for future innovations in onboard charger development.
DOI: http://doi.org/10.5281/zenodo.20201670
Encrypted Cloud Storage Using Zero-Knowledge Architecture
Authors: Dr. D. Prabhu, Nithishwaran A, Someshwaran C, Sasikumar K, Sachin S
Abstract: Modern cloud storage platforms raise serious concerns about data privacy, since most service providers retain access to user encryption keys. This paper presents Nimbus Cloud, a client-side encrypted storage system built on a zero-knowledge architecture in which the server stores only ciphertext and has no knowledge of plaintext data or encryption keys. All cryptographic operations—including AES-256-GCM encryption and RSA-based key exchange— execute entirely within the user's browser before any data leaves the device. The system supports single-user and group based file sharing through Public Key Encryption (PKE) and an RSA-wrapped AES group-key scheme. The implementation employs React.js on the frontend, Node.js with Express on the backend, MongoDB for metadata management, and Cloudinary (backed by Amazon AWS) for encrypted file storage. Structured testing across eight functional scenarios—spanning authentication, encryption, upload, download, sharing, and access control—confirmed complete correctness and validated the zero-knowledge property under every evaluated condition.
DOI: http://doi.org/10.5281/zenodo.20201705
Muscle-Signal Controlled Assistive Mobility Interface With Multi-Sensor Safety Integration
Authors: Mr. Yash Bansod, Prof. Ranjana Shende
Abstract: People with disabilities in their mobility are a large population worldwide who have a need for intuitive and reliable assistive technologies. In these settings, the human machine interaction is a very interesting approach through electromyography (emg) signals, which are naturally produced during voluntary muscle movement. This work explains the design of a low-cost robotic mobility vehicle for a muscle signal controlled assistive prototype, which is able to independently process forearm emg signals of both arms. Two muscle bioamp candy sensors record the signal from the user's forearm and then the signal is filtered by a 4th order butterworth band pass filter from 74.5hz to 149.5hz, followed by a moving average envelope detector. Using an arduino uno microcontroller and l298n motor driver, a threshold-based decision layer is used to convert the envelope values into motor direction control. In addition to motion control, the system includes an ultrasonic sensor for detecting obstacles near the driver and two infrared sensors to detect the edges of the surfaces where the driver is standing, to send this information to a safety interlock that can override the motor control system when dangerous conditions are detected. Three subjects were tested, and consistent and reliable directionality was achieved in all intended modes of movement. The integrated safety architecture proved successful for avoiding unwanted motion in the event of an obstacle or edge detection, thus confirming the multi-sensor system approach for low-cost assistive mobility systems.
DOI:
AI-Based Fake News Detection Using Machine Learning And Explainable AI
Authors: Ms. Sayana Garudik, Ms. Pari Purohit, Mrs. Neeku Sahu, Mrs. Shruti Mehta
Abstract: Fake news has become a serious issue in the digital world, spreading misinformation rapidly through social media and online platforms. Detecting fake news manually is difficult due to the large volume of data. In this research, we propose an AI-based fake news detection system using machine learning (ML) models. The dataset is preprocessed and multiple ML algorithms such as Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), and XGBoost (XGB) are applied. Feature selection techniques are used to improve accuracy, and Explainable AI tools like SHAP and LIME are used to interpret model predictions. The best-performing model is deployed for real-time fake news detection through web-based applications.
DOI: http://doi.org/10.5281/zenodo.20201805
AI-Based Modeling: Techniques, Applications And Research Issues Towards Automation, Intelligent And Smart Systems
Authors: Nisha Gautam, Nimi Gautam, ku Nalesh
Abstract: Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI based modeling is the key to build automated, intelligent, and smart systems according to today’s needs. To solve real-world issues, various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on “AI-based Modeling” with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains.
DOI: http://doi.org/10.5281/zenodo.20201864
Python Versus R Language: A Comparative Analysis for Data Science and Statistical Analysis
Authors: Adarsh Ravi Mishra
Abstract: Two of the most popular computer languages for statistical analysis and data research are Python and R. Large libraries and frameworks that support machine learning, data manipulation, and visualization are available in both languages. This study compares Python with R, emphasizing their advantages, disadvantages, and applicability to various data science and statistical analysis applications. In addition to reviewing previous research, the study looks at data-driven research approaches and assesses how well both languages perform in a range of data science applications. The findings show that R is still the best option for statistical analysis and visualization, even while Python excels in scalability, integration with other technologies, and machine learning capabilities. The ramifications of these findings for practitioners, scholars, and data-driven companies are examined. The conclusion offers suggestions for selecting the right language depending on particular use cases and highlights the most important discoveries.
DOI: