Category Archives: Uncategorized

Authors: Saurabh Singh

Abstract: The present study investigates the temporal variation of river water quality during Maha Kumbh 2025 in the Prayagraj Sangam region using Central Pollution Control Board (CPCB) monitoring observations. The assessment was carried out using major physicochemical and biological parameters including turbidity, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), pH, and fecal coliform. Temporal trend analysis and statistical interpretation were performed to evaluate the impact of large-scale pilgrimage activities on river water quality. The results indicated noticeable fluctuations in turbidity, COD, BOD, and fecal coliform concentrations during major bathing events, suggesting enhanced anthropogenic influence and sediment disturbance in the river system. Dissolved oxygen remained relatively stable throughout the monitoring period, while pH values stayed within acceptable environmental limits. Correlation analysis revealed positive relationships among turbidity, COD, and fecal coliform, indicating combined effects of organic and microbial contamination during intensive bathing periods. The study highlights the significance of continuous water quality monitoring during mass religious gatherings for sustainable river management and environmental protection.

DOI: http://doi.org/10.5281/zenodo.20133734

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Authors: Miss Gaikwad Janhvi Anurath, Miss. Kadam Vaishnavi Raju, Mr. Chinmay Shinde, Mr. Narayanpure Sujal, Prof. Dr.Ashish Kumar

Abstract: Friction Stir Welding (FSW) is an advanced solid-state joining technique used for welding similar and dissimilar metals without melting the base materials. In this project, an experimental investigation has been carried out to study the mechanical properties of dissimilar joints between Aluminium Alloy AA6061 and Copper (ETP Copper) using the Friction Stir Welding process. The purpose of this study is to evaluate the effect of welding parameters on the strength and quality of the welded joints. The welding experiments were performed using a carbide conical ball nose tool under different process conditions such as rotational speed, welding speed, and plunge depth. Proper fixture arrangements and clamping systems were used to obtain defect-free joints. AA6061 aluminium and copper were selected due to their wide applications in aerospace, automobile, marine, electrical, and heat transfer industries where light weight materials with high thermal and electrical conductivity are required. After the welding process, the joints were examined through visual inspection and tested for various mechanical properties including tensile strength, hardness, and microstructural characteristics. The experimental results showed that welding parameters greatly affect heat generation, material flow, and intermetallic compound formation at the weld interface. Optimized welding conditions produced sound joints with better tensile strength and uniform hardness distribution. The investigation concludes that Friction Stir Welding is an efficient and economical process for joining dissimilar aluminium-copper materials with fewer defects and Improved mechanical properties compared to conventional fusion welding methods. The results of this project can be useful for industrial applications requiring strong, lightweight, and conductive dissimilar metal joints.

DOI: https://doi.org/10.5281/zenodo.20133147

Published by: vikaspatanker

Mrs. Malati Vaibhav Tribhuwan
Affiliation	Assistant Professor and HOA, Blockchain Technology, Department of Technology, SPPU
Email-Id:	tmalativ@gmail.com
Publication:  Books: Python Programming – T.Y.B.Sc.(Computer Science), Sem-IV Vision Publication 2022. Publications: An Analysis of Internet of Behaviour (IOB): A Crucial Footprint on Healthcare Sector ,London Metropolitan University, London, UK (Venue Partner)2025. Intelligent Agriscience: Epoch of AI in Indian Agriculture IJREAM, Volume-6, Issue-11, ISSN: 2454-9150 February 2021. 3D Password: A Secured Authentication System IJRAR, Volume-6 Issue-2, ISSN No: 2349-5138 April 2019. AIoT with PUF: A Concrete Security IJITEE, Volume-9 Issue-7, ISSN: 2278 ,May 2020. Dr. D. Y. Patil ACS College, Pimpri, Pune ,A study of Artificial Intelligence and its Applications in different areas February 2018

Published by: vikaspatanker

Mrs. Malati Vaibhav Tribhuwan
Affiliation	Assistant Professor and HOA, Blockchain Technology, Department of Technology, SPPU
Email-Id:	tmalativ@gmail.com
Publication:  Books: Python Programming – T.Y.B.Sc.(Computer Science), Sem-IV Vision Publication 2022. Publications: An Analysis of Internet of Behaviour (IOB): A Crucial Footprint on Healthcare Sector ,London Metropolitan University, London, UK (Venue Partner)2025. Intelligent Agriscience: Epoch of AI in Indian Agriculture IJREAM, Volume-6, Issue-11, ISSN: 2454-9150 February 2021. 3D Password: A Secured Authentication System IJRAR, Volume-6 Issue-2, ISSN No: 2349-5138 April 2019. AIoT with PUF: A Concrete Security IJITEE, Volume-9 Issue-7, ISSN: 2278 ,May 2020. Dr. D. Y. Patil ACS College, Pimpri, Pune ,A study of Artificial Intelligence and its Applications in different areas February 2018

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Authors: R.Renuka, Dr.S.Mohana

Abstract: Typically, sleep disorders like insomnia, sleep apnea, and narcolepsy may not receive appropriate diagnosis until serious physical and mental health issues develop. Traditional techniques, though effective, involve polysomnography, which is not only labor-intensive and time-consuming but also demands special clinical conditions. Hence, this study aims to develop a framework that relies on AI techniques to utilize a hybrid model of Deep Learning techniques, including Convolutional Neural Networks (CNN) and Long Short- Term Memory (LSTM), to process EEG signals to identify sleep disorders. The CNN model can automatically identify spatial features in the raw signals, and the LSTM model can identify temporal dependencies in the signals to correctly classify Awake, REM, and NREM stages. Preprocessing techniques have been employed to clean and normalize the signals. The system, trained and validated using standardized data sets like PhysioNet, exhibits robustness and generalization in dealing with different patterns of sleep. It can also be used to analyze new EEG signals in real-time, detect abnormal sleep patterns, and predict the occurrence of sleep disorders. This intelligent system can greatly improve the efficiency of diagnosis and reduce the need to rely on manual diagnosis. It can also prove to be a cost-effective solution.

DOI: https://doi.org/10.5281/zenodo.20132129

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Authors: Prof. G. R. Padule, Shweta Anil Bhosale, Dnyaneshwari Ravikant Patil, Vrushali Vishal Zambare

Abstract: Induction motors are vital components in industrial and commercial systems, where unexpected failures can lead to costly downtime and reduced productivity. Traditional maintenance strategies such as corrective and preventive maintenance are often inefficient, either reacting too late or performing unnecessary servicing. Predictive maintenance, powered by machine learning (ML) techniques, offers a smarter approach by forecasting motor health conditions based on real-time data analysis. This review paper presents an overview of recent advancements in predictive maintenance for induction motors using ML algorithms. Various techniques such as support vector machines (SVM), artificial neural networks (ANN), random forests, and deep learning models are discussed for fault detection, diagnosis, and remaining useful life (RUL) estimation. The paper also highlights the importance of feature extraction from vibration, current, and temperature signals, as well as the integration of Internet of Things (IoT) and cloud computing for real-time monitoring. Comparative analysis of different ML approaches is provided to identify their strengths, limitations, and potential for industrial application. Finally, the review outlines current challenges and future research directions for developing efficient, scalable, and interpretable predictive maintenance frameworks for induction motors.

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Authors: Mr. Prajwal Narayan Chaudhary, Mr. Pranav Prasad Kulkarni, Mr. Chetan Ashok Bhalekar, Mr. Aditya Ganesh Gunjal, Professor Kalyani Zirpe

Abstract: Corrosion is a significant cause of damage in industrial infrastructure, transportation systems, marine equipment, pipelines, and metal parts. Traditional methods for inspecting corrosion mainly rely on manual observation and regular maintenance. These processes are time-consuming, labor intensive, and are subjective, which can lead to human error. Delays in spotting corrosion can lead to serious structural failures, higher maintenance costs, operational downtime, and safety risks. To address these issues, this paper introduces a real-time AI-based Corrosion Detection and Monitoring System. This system uses the YOLOv5 deep learning framework along with a modern web-based structure. The new system combines computer vision, deep learning, and web technologies to automate the detection of corrosion and assess its severity. It uses the YOLOv5s object detection model to find corrosion areas in uploaded images and live camera feeds. A React.js frontend offers an engaging and responsive user interface. Meanwhile, a FastAPI backend handles image processing, runs the necessary calculations, and communicates results. The system evaluates detected corrosion areas using bounding box calculations to estimate the amount of corrosion and categorize its severity as mild, moderate, or severe. It also features graphical visualizations, historical tracking, and repair suggestions to support preventive maintenance. This framework provides nearly real-time detection with higher accuracy and less reliance on manual inspection. Its modular and scalable design allows it to be used in various industries, including maritime, civil infrastructure, manufacturing, automotive, and aviation. Tests show that the system successfully identifies corrosion under different environmental conditions while maintaining good computational performance. This solution represents a cost- effective and smart way to monitor structural health and perform predictive maintenance.

DOI: https://zenodo.org/records/20121779

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Authors: Tanu Yadav, Neelam Sahu, Deepak Sahu

Abstract: The rapid expansion of the pre-owned automobile industry has increased the demand for reliable and intelligent digital platforms for vehicle trading. Traditional used-car marketplaces often face challenges such as lack of transparency, inefficient search mechanisms, inconsistent pricing, and fraudulent listings, which reduce user trust and overall customer satisfaction. This research proposes an AI- powered car marketplace designed to improve the process of buying, selling, and exchanging second- hand vehicles through intelligent automation and secure digital infrastructure. The proposed system integrates advanced technologies including intelligent search optimization, personalized recommendation systems, automated listing moderation, and secure authentication mechanisms to enhance platform reliability and usability. The platform provides users with detailed vehicle listings, filtering and comparison features, responsive communication channels, and mobile-friendly accessibility to simplify customer interaction and decision-making. The backend architecture is developed to support scalable data management and efficient transaction handling using modern web technologies. Artificial Intelligence modules are incorporated to improve recommendation accuracy, optimize search relevance, and identify suspicious or duplicate listings. Experimental evaluation indicates that the proposed system improves search efficiency, recommendation precision, and operational transparency compared to conventional online used-car trading systems. The research demonstrates how AI-driven digital marketplaces can enhance trust, user engagement, and efficiency within the pre-owned vehicle industry while providing a scalable solution suitable for modern automotive e-commerce applications.

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Authors: M.R.Manas, Dr. Umakanta Choudhury

Abstract: This study provides an in-depth analysis of the electromagnetic comparative assessment of the unskewed and skewed rotors for a 1 kW, three-phase, inner-rotor permanent magnet synchronous generator intended for small-scale direct-drive wind power applications. The generator has 36 stator slots and 12 rotor poles, with a 220 mm outer diameter of the stator and a stack length of 60 mm. The unskewed generator uses a ring magnet rotor design and features a gap size of 2.0 mm, while the skewed rotor design uses a block magnet rotor with a linear step of 10 degrees in three stages, with the air gap size of 1.5 mm. Performance criteria used for the finite-element-based simulations using Altair FluxMotor include the following: cogging torque, back-EMF waveform quality, losses, torque ripple, voltage, and efficiency, combined with thermal analysis. The reduction of the peak-to-peak cogging torque of the skewed rotor reaches 84.5phase back-EMF decreases by 67requirements of IEEE 519 regarding harmonics. Both the unskewed and skewed rotors show comparable efficiency at the same operating point (19 N·m and 500 rpm): 95.34full-load efficiency of the unskewed rotor (92.87the corresponding efficiency of the skewed rotor (91.96.

DOI: https://doi.org/10.5281/zenodo.20120373

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Authors: Abdullahi Idris, Aminu A. Abdullahi, Jamilu Awwalu, Abdullahi Uwaisu Muhammad

Abstract: Clinical time-series data are inherently complex, characterized by temporal dependences, irregular sampling and missing observations making accurate longitudinal risk prediction a challenging task. The study presents a novel hybrid Transformer framework for temporal representation learning and longitudinal risk prediction in clinical time-series that integrates the strengths of self-attention mechanism of Transformers to capture long-range interactions across time steps with the LSTM networks in modeling short-term temporal dependencies. A fusion module is introduced to adaptively combine representations from both components, enabling robust learning from irregular and partially observed clinical data. The experimental results demonstrate that the hybrid transformer framework effectively categorized patients into high-risk and low-risk categories based on their attributes. The training results indicate that the model performed well, with an accuracy of 98.6%, a sensitivity of 96.2% and a specificity of 97.8%. The model correctly identified 11 out of 18 high-risk patients and 16 out of 22 low-risk patients, with apparent errors of 38.9% and 27.3% respectively. These findings indicate that the hybrid Transformer framework can successfully learn patterns associated with cardiovascular risk from training data. Similarly, the test results confirm the model’s ability to predict previously unseen data. The model correctly categorized 9 out of 12 high-risk cases and 6 out of 8 low-risk cases, resulting an overall accuracy of 91.2%, sensitivity of 89.3% and specificity of 92.0% with a 25% apparent error in both cases.

DOI: https://doi.org/10.5281/zenodo.20118703

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