Authors: Prachi Babasaheb Desai

Abstract: Mental health is an essential aspect of human well- being, yet millions remain undiagnosed or untreated due to stigma and lack of access to care. This research presents an AI-Based Mental Health Detection and Therapy Recommendation System designed to identify early signs of stress, anxiety, and depression using natural language processing (NLP), voice tone analysis, and user responses to validated questionnaires. The system recommends tailored therapeutic interventions such as mindfulness techniques, journaling, and referrals to professionals. This scalable, explainable, and user- friendly solution aims to democratize access to mental health support.

DOI: https://doi.org/10.5281/zenodo.15813444

 

Published by: vikaspatanker

Authors: Jupaka Mukesh Kumar, Kasaboina Mahesh, Thulugu Dileep, Dr. Yagya Dutta Dwivedi

Abstract: This project presents an overall aerodynamic analysis of an Audi R8 using computational fluid dynamics (CFD) for performance enhancement in terms of decreasing drag and increasing downforce. The study proposes to investigate the effect of a Selig S1223 (s1223-il) rear spoiler at varying angles of attack 0°, 3°, and 5° at varying inlet speeds of 20 m/s, 30 m/s, and 40 m/s. The analysis was conducted by simulating the model in SolidWorks for geometry and ANSYS Fluent for the flow study. The car was first analyzed in the base state without a spoiler, exhibiting growth in Coefficient of lift (CL) and Coefficient of drag (CD) coefficients as speed increases. After installing the spoiler, the lift decreased by a remarkable margin (with creation of downforce) while the drag increased. The study presents that the higher the angle of attack, the higher the downforce, thus improving the vehicle's stability but at greater drag forces. Using a experimental analysis of the result from the two cases involving a spoiler and no spoiler, this project proves optimal aerodynamic design changes that minimize drag and increase the aerodynamic efficiency of the vehicle as a whole. Such results are useful in designing performance vehicles with greater handling and lesser aerodynamic drag.

DOI: http://doi.org/

 

Published by: vikaspatanker

Authors: Nithin Nanchari

Abstract: The Internet of Things in healthcare provides healthcare with its delivery of patient care from real-time data monitoring, remote diagnostics, and personalized treatment. However, due to this advancement, there are data privacy and security issues like data breaches, cyber threats, and unauthorized access. The paper contributes by identifying the potential key security issues and vulnerabilities in IoT healthcare and how data has been routed through vulnerabilities, ensuring the security of the healthcare system.

DOI: http://doi.org/10.5281/zenodo.15796381

 

Published by: vikaspatanker

Authors: Nithin Nanchari

Abstract: With the rise of the web of things in the health sector, the personalized treatment of people in real-time using real data has evolved into shape. Through IoT and personalized healthcare, individual medical interventions are delivered so that patients can monitor themselves and gain better treatment effectiveness. The contribution of this paper consists of how IoT enables custom healthcare solutions through AI-driven health assistants, real-time data analytics, a patient-centric approach, and wearable technology. Also, the study highlights the utility of IoT in improving the accuracy of precision medicine and improving healthcare services. Such a personalized healthcare solution could progress by integrating IoT and AI.

DOI: http://doi.org/10.5281/zenodo.15796148

 

Published by: vikaspatanker

Authors: Nithin Nanchari

Abstract: The Internet of Things (IoT) is revolutionizing the healthcare industry by enabling unprecedented levels of connectivity, operational efficiency, and patient-centered care. With the help of smart medical devices and real-time data analytics, healthcare providers can now predict, monitor, and automate various clinical and administrative functions more effectively than ever before. This paper introduces the concept of IoT in healthcare, explores its primary applications such as remote patient monitoring, smart hospitals, and medication management, and outlines the benefits it delivers to patients and providers. While challenges such as cybersecurity threats and lack of standardization persist, the overall impact of IoT in healthcare continues to grow, driving improvements in outcomes, access, and efficiency.

DOI: http://doi.org/10.5281/zenodo.15795982

 

Published by: vikaspatanker

Authors: Mrs. Penki Tulasi Bai, Mrs. P. Manasa

Abstract: This study suggests employing predictive maintenance to enhance the operational efficiency and prolong the lifespan of industrial machinery and equipment through machine-learning techniques. As producers prioritize reducing downtime and cutting expenses, proactive maintenance strategies are becoming increasingly vital for ensuring operational reliability. The research aims to gather historical data to train machine-learning models that can predict equipment failures and develop an algorithmic framework for scheduling preventive maintenance. The primary objective is to assist in forming an effective anticipatory maintenance strategy, which can lower industrial maintenance costs and improve product prices. Various machine-learning techniques, along with extensive data preprocessing and feature engineering methods, will be utilized in this research. Data preprocessing will involve tasks such as cleaning, dataset conversion, and normalization prior to model training. Feature engineering will focus on identifying the most important characteristics for accurate prediction of machine failures. Numerous machine-learning methods, including Random Forest (RF), Long Short-Term Memory (LSTM), and Support Vector Machines (SVM), will be evaluated to determine the most effective model for precise forecasting. The performance of these models will be compared using metrics such as Root Mean Square Error (RMSE), R-squared (R²), and Mean Absolute Error (MAE) as indicators. Ultimately, the top-performing machine-learning models will be integrated into real industrial settings, with the optimal model expected to achieve a 5-10% increase in operational efficiency.

DOI: http://doi.org/10.5281/zenodo.15790457

 

Published by: vikaspatanker

Authors: Ms. Bobbili Bhargavi, Mrs. K. Sowjanya

Abstract: The complex evolution of railway cars influences transit routes. Many mistakes arise from the utilization of train lines. Both Manufacturing mistakes and improper rail usage are responsible. For these deficiencies. There are numerous techniques for detection. Errors must be recognized promptly and rectified. The camera-based technique is One of these methods. By utilizing cameras affixed to the railway vehicle, images of the rail components are examined. Flaws are identified in the rail components. A method for detecting and analysing defects in rail tracks. Surfaces are proposed in this document. The recommended method employs image processing to identify the rail surface. High resolution images captured by specialized cameras mounted on the proposed system encompasses railway inspection cars. A Variety of track issues, including cracks, weld defects, and track Misalignment and ballast degradation are detected. These images were utilized to perform an analysis. Pre-processing and feature extraction. Image processing entails the application of segmentation techniques. Procedures to isolate the track area and emphasize any Potential defects. Fuzzy logic is employed to prioritize maintenance tasks. Based on urgency once issues have been identified and their Severity has been evaluated. Fuzzy logic is particularly adept at capturing the subjective assessments involved in evaluating. track conditions as it offers a flexible framework. Processing ambiguous and imprecise data. To assign appropriate severity ratings for the identified features of each issue. Type, fuzzy rules, and membership functions are developed.

DOI: http://doi.org/10.5281/zenodo.15790262

 

Published by: vikaspatanker

Authors: Ms . Nellipudi Sai Sravani1, Dr Sivabalan Settu Ph.D, Postdoc 2

Abstract: Despite the numerous advantages that electric vehicles (EVs) offer in terms of environmental protection and emission reduction, their widespread acceptance is primarily influenced by their pricing. By utilizing machine learning (ML) algorithms, it is possible to forecast these costs. This study seeks to evaluate the effectiveness of several prominent ML algorithms to ascertain which one is most capable of accurately predicting the prices of electric vehicles. In order to pinpoint the essential factors, we conducted a literature review to investigate the elements that influence the pricing of electric vehicles, facilitating our cost estimation. We theoretically assessed these ML algorithms to corroborate our results and subsequently compared the findings of this comparative analysis with the results obtained from the simulations.

DOI: https://doi.org/10.5281/zenodo.15790332

 

Published by: vikaspatanker

Authors: Mrs . Kolli Kundana Bhavya Sree, Mrs. B Sirisha, Mtech, Associate Professor

 

 

Abstract: To ensure the reliability of connected devices, machine learning is employed to analyse network traffic, facilitating quicker identification of unusual behaviour and congestion. The application of machine learning methods improves the ability to manage traffic and supports the maintenance of service quality. Furthermore, the role of machine learning in network security is to identify anomalies and classify traffic in real-time, aiming to optimize network performance and uncover potential threats. This study highlights the beneficial effects of utilizing machine learning techniques to improve network reliability and security. One of our contributions is an examination of an example of HTTP/3 traffic interacting with a web server. We implemented machine learning algorithms to differentiate between standard traffic and possible HTTP/3 flood attacks. Additionally, we developed a dataset of traffic samples featuring 23 attributes categorized into six subgroups. From traffic captured in a simulated environment, we evaluated the significance of these attributes and discovered that employing machine learning techniques can greatly enhance both network security and reliability. We utilized four supervised classification algorithms: Support Vector Machine (SVM), Logistic Regression, Random Forest, and K-Nearest Neighbours (KNN). These algorithms represent a category of supervised classification methods. They played a crucial role in training datasets of network traffic, which were carefully labelled to distinguish between Distributed Denial-of-Service (DDoS) attacks and normal traffic. The results of this research demonstrate the efficacy of machine learning algorithms in analysing network traffic to detect specific types of DDoS attacks, especially those that use QUIC traffic. This illustrates the significant potential of machine learning techniques in strengthening the overall security and reliability of networks.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Ms. Manepalli Kavya, Mrs. Jitendar Ahuja

Abstract: Recent years have seen a significant increase in phishing attacks targeting websites, posing persistent challenges to digital security. While numerous detection tools have been developed, they often fall short in comprehensively identifying all threats and struggle with subtle, evolving forms of deception. Integrating machine learning (ML) techniques has emerged as the most effective strategy to overcome these limitations, significantly enhancing detection accuracy and computational efficiency. This approach is crucial for addressing the shortcomings of existing phishing detection models. This paper introduces an Intelligent Phishing defence paradigm, leveraging an ENASSEMBLE-driven ML model specifically trained on a designed dataset for high-fidelity website identification. Our objective is to demonstrate how the ENASSEMBLE model not only bolsters the overall accuracy of phishing detection but also offers a robust and efficient solution capable of recognizing complex and evasive fraudulent sites, thereby fortifying online security.

DOI: https://doi.org/10.5281/zenodo.15790035

 

Published by: vikaspatanker