Authors: Professor Fiza Pathan, Professor Hirkanya Bhole, Professor Nageshwari Sarade, Professor Sushma Borewar, Professor Bhavesh Thakre

Abstract: Polymeric materials have become increasingly significant due to their wide-ranging applications and adaptability to modern societal needs. These materials exhibit remarkable properties, including thermal stability, chemical resistance, conductivity, and ion-exchange capacity. The development of polymeric ligands and coordination polymers, particularly those containing donor atoms such as N, S, and O, has expanded their utility in various fields, including catalysis, electronics, surface coatings, and biomedical applications. Chelate polymers, formed by coordinating metal ions with organic ligands, exhibit both organic and inorganic characteristics, offering desirable magnetic, thermal, and electrical properties. Despite challenges such as poor solubility and plasticity, chelate polymers are utilized in the aerospace, automotive industries, and semiconductor sectors due to their thermal resilience and functional diversity. Recent research has focused on designing low-band-gap conducting polymers and synthesizing various metal complexes with Schiff bases, hydroxamic acids, and thiosemicarbazones, leading to advances in coordination chemistry and material science. These developments underline the transformative role of polymers in science, industry, and everyday life.

DOI: https://doi.org/10.5281/zenodo.16420178

Published by: vikaspatanker

Authors: Ms. Suman, Mr. Kartik Gossain

Abstract: Healthcare appointment scheduling is crucial for improving patient access and optimizing medical resources. Traditional booking methods often lead to inefficiencies, delays, and poor user experience. This research presents the design and implementation of MediConnect, a Full-Stack Healthcare Appointment Scheduling System, developed using the MERN (MongoDB, Express.js, React, Node.js) stack. The system enables seamless appointment booking, user authentication, real-time availability updates, secure payments, and role-based access for patients, doctors, and admins. The MERN stack was chosen for its scalability, flexibility, and performance, offering advantages over traditional web technologies. Implementation results demonstrate improved efficiency and accessibility in healthcare booking. Future work may include AI-driven doctor recommendations, telemedicine integration, and enhanced security measures.

DOI: https://doi.org/10.5281/zenodo.16419927

Published by: vikaspatanker

Authors: Assistant Professor Mr Pradeep, Mr. Kunal Pandey, Mr Deepanshu Tyagi

Abstract: Effective financial management is essential for individuals and businesses to track income, expenses, and overall financial health. This study presents Finlytics AI, an intelligent finance and budget management platform that leverages machine learning to enhance financial tracking, budgeting, and analysis. By integrating real-time transaction categorization, AI-powered receipt scanning, and interactive financial visualizations, the system provides users with deeper insights into their spending habits. The platform also supports multi-account tracking, recurring transaction management, and automated budget alerts to help users maintain financial discipline. With AI-driven financial reports and trend analysis, Finlytics AI empowers users to make informed financial decisions, improving both short-term budgeting and long-term financial planning. Through advanced data analytics and automation, this approach enhances the efficiency, accuracy, and accessibility of financial management, offering a scalable and intelligent solution for personal and business finance.

DOI: https://doi.org/10.5281/zenodo.16419749

Published by: vikaspatanker

Authors: Mohammed Obaid Baba, Muddam Siddartha, Pulluri Sai Vardhan, Swati Sucharita, M.A Jabbar

Abstract: The rapid evolution of machine learning (ML) technologies has significantly impacted various sectors, including education. This analysis reviews the advancements in machine learning-driven models within the educational system, highlighting their roles in enhancing teaching methods, supporting personalized learning, and predicting student performance. By employing a range of ML techniques from traditional algorithms to hybrid and deep learning approaches educators can better assess student engagement, identify at-risk learners, and tailor interventions to improve academic outcomes. The review also explores key applications such as early academic performance prediction, intelligent tutoring systems, and adaptive learning environments that respond dynamically to individual student needs. Despite the promising results, challenges such as data privacy concerns, ethical considerations, and the need for comprehensive, unbiased datasets persist. This review aims to provide a holistic view of how machine learning is reshaping the educational landscape, while discussing existing limitations and suggesting future directions to maximize the benefits of ML in education.

DOI: https://doi.org/10.5281/zenodo.16419553

Published by: vikaspatanker

Authors: Assoc. Prof. Swati Mehta, Dr. Ujjvala M. Tiwari

Abstract: For hundreds of years, creators and designers have looked to nature for inspiration. Sustainability and environmentalism have led to the widespread use of natural textures, shapes, and colours in many areas of design. More lately, graphic designers have been unable to ignore the beauty of mother nature as a source of inspiration, in contrast to paintings, sculpture, architecture, and textiles. According to a number of studies, logos that represent real-world objects—such as plants, animals, or locations—require less processing work than abstract ones because they are easier to recognize. Because they appeal to a particular target demographic and offer a personal touch, animals are a popular symbol for logos. For instance, the image of the lion, who rules the jungle, stands for power, strength, bravery, and justice. On the one hand, jewellery logos use the beauty and grace of a swan, while logos that use lions may symbolize a brand's strength or authority within its industry. Certain companies' logos use plants, trees, and flowers to symbolize life, growth, creativity, freedom, harmony, prosperity, value, and tranquillity. Unilever's emblem features 25 natural symbols: a lion and palm tree representing the RBI, a galloping horse for TVS Motor Company, a soaring swan with the Konark Chakra for Air India, and a banyan tree for Dabur India Limited. The current study focuses on how different types of nature are portrayed in logos for Indian advertising firms.

DOI: http://doi.org/10.5281/zenodo.16408435

 

 

Published by: vikaspatanker

Authors: Sudhanshu Gupta, Avinash Aganihotri, Harsh Sharma, Tanya Handa

Abstract: More people understand the use of technology and that is being used on their daily life. This will increase the chances of losing valuable data and information to the scammers who might use your data for your own detriment or have a word or a spell with you or harm you in any possible manner or way. Consequently, fraud detection Systems are employed in different fields of businesses such as banking, e-commerce, healthcare, and cybers security to identify and terminate fraud. They are essential because of the prevention of monetary losses, the protection of private information, the attainment of client confidence, and compliant with legal requirements. Some of the modern systems employ machine learning methods, while supervised learning methods are adopted to ascertain pre-defined fraud patterns and the unsupervised ones to extract anomalies. Techniques to increase precision of the identification of fraud include anomaly detection, graph based method and ensemble. Consequently, to guarantee an effective fraud detection for user it is necessary to find best fraud detection algorithm while maintaining regulatory standards and customer satisfaction , the best fraud detection algorithm must handle all aspects; efficiency, false positive disrupts, F1 score, dealing with imbalanced data and cost.

DOI: https://doi.org/10.5281/zenodo.16419380

Published by: vikaspatanker

Authors: Jeslin Hashly, Jestin K Sunil, Alby Shinoj

Abstract: This paper introduces CyberGuardian, a new LLM-based agent for autonomous penetration testing. CyberGuardian is composed of two parts: a planner and a summarizer. These parts cooperate to create and carry out commands in an iterative manner. To evaluate CyberGuardian, we introduce two new benchmark suites based on the popular Capture the Flag (CTF) systems PicoCTF and OverTheWire, comprising 200 challenges in various domains and levels of difficulty. Our experiments check CyberGuardian's most critical parameters, such as levels of creativity and token usage, on LLM. Results reaffirm the need of good security procedures and show how LLM-based agents can advance autonomous penetration monitoring.

DOI: https://doi.org/10.5281/zenodo.16419170

Published by: vikaspatanker

Authors: Sharath Chandra Kodtihyala, Riddhi kinnera, Shashikiran Sangisetti, Praveem Kumar, Prasanthrao A

Abstract: This study presents Polyglot, a deep learning-based language translation system that utilizes Transformer, LSTM, Attention, and Seq2Seq models to enhance context-aware translation. While well-known systems like Google Translate provide reliable translations, Polyglot offers improved contextual understanding through a hybrid approach that balances accuracy and efficiency. The study evaluates Polyglot’s performance using BLEU scores and user satisfaction, demonstrating its effectiveness. It includes a detailed discussion on the dataset, model architecture, training process, and evaluation criteria. The results indicate a significant improvement in translation quality compared to baseline models. Future work will focus on real-time improvements and customization to further enhance translation accuracy and user experience

DOI: https://doi.org/10.5281/zenodo.16418885

Published by: vikaspatanker

Authors: Deepti Ram Gangurde, Arun Mishra, Shantanu Singh

Abstract: This research proposes a decentralized courier tracking system using blockchain technol- ogy and smart contracts, aimed at enhancing transparency, trust, and security in logistics. The solution is implemented on the Volta blockchain and integrates MetaMask[3] to facilitate se- cure interactions between participants without relying on centralized intermediaries. The sys- tem automates shipment updates, maintains immutable tracking records, and protects sensitive data through cryptographic mechanisms. The implementation demonstrates improved trace- ability, operational efficiency, and stakeholder accountability. Performance evaluation indi- cates significant enhancements in security, cost-effectiveness, and scalability when compared to conventional courier tracking methods. The proposed system offers a robust alternative for modern supply chain management[6].

DOI: https://doi.org/10.5281/zenodo.16418691

Published by: vikaspatanker

Authors: Yassine Dahbi, Hamza Naciri, Hamza Zaouri, Ouahib Alaoui

 

Abstract: This study examines the use of GradientBoostingRegressor, StackingRegressor, and Gradient Boosting Regression with HistGradientBoosting in developing models that predict the compressive strength (fcu) and splitting tensile strength (fsp) of steel fiber-reinforced recycled aggregate concrete (SFR-RAC). The information comprises 465 compressive strength and 339 splitting tensile strength data of concrete mixes with varied ratios. Training and model testing were performed using 80/20 split with PSO for the hyperparameter optimization. The performance of the model was measured with four statistical metrics: coefficient of determination (R²), mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). Out of the models, Gradient Boosting Regression with HistGradientBoosting performed better in terms of prediction, with StackingRegressor taking the second rank. SHapley Additive exPlanations (SHAP) and feature importance were employed to determine the influence of input parameters on model predictions. From the results obtained, it was evident that the water content, cement content, and fiber ratio influence considerably the strength of SFR-RAC. The models give good insights regarding SFR-RAC mixture behavior, which is helpful in the production of environmentally friendly concrete with greater enhanced strength. Future research can enhance the data and use other predictor variables to further support these models.

DOI: https://doi.org/10.5281/zenodo.16420152

 

Published by: vikaspatanker