Daily Archives: October 30, 2025

Authors: Abhijit Ranjan, Chandraveer Singh

Abstract: In the recent years, the demand for intelligent medication recommender systems has increased tremendously with the evolution of digital health technology. This research targets the development of a symptom-based medication recommender system from a structured and diversified healthcare database. The database is descriptive in nature with information regarding patient symptoms, associated medicines, dietary advice, exercise plans, precautions, and doctor specialties. Early steps of this project include extensive data exploration and preparation from several CSV files to create a clean and solid base for model training. For building the central recommendation engine, traditional machine learning algorithms such as Decision Tree, Random Forest, Naive Bayes, and Logistic Regression were utilized, which try to predict symptoms and suggest the most suitable medicines out of a pre-defined list. Among the models used, the Decision Tree classifier had the best performance, followed by Random Forest, Naive Bayes, and Logistic Regression. The system is smart enough for users to put in symptoms and get suggested medicines for the same, providing useful help for non-emergency medical conditions and employment in resource-constrained environments. Future developments will involve incorporating patient medical history, dosage calculation, drug interaction screening, and implementing the system through a mobile platform to enhance accessibility and real-time use.

DOI: http://doi.org/10.5281/zenodo.17482512

Published by: vikaspatanker

Authors: Dr Anitha S, Mohammed Muzammil S, Prince P, Sridhar L

Abstract: This proposed work designs an Automatic Waste Segregation System that focuses on separating metallic waste using a magnetic belt mechanism. This IoT based system uses a conveyor belt that carries mixed waste materials through a detection unit containing an electromagnet. When the magnet is activated, it attracts and separates the metallic components from the remaining waste. The metallic waste is then released into a separate compartment once the magnet is deactivated. A sensor is used to measure how much of the metal compartment is filled, and the fill level is displayed as a percentage on a digital screen. The entire operation is controlled by a microcontroller to ensure smooth and accurate functioning. This system helps reduce manual labor, increases sorting efficiency, and supports effective recycling management. It offers a simple, low-cost, and eco-friendly device to waste segregation that can be applied in both domestic and industrial settings

DOI: https://doi.org/10.5281/zenodo.17482056

 

Published by: vikaspatanker

Authors: Kanak Patil

Abstract: This paper provides an overview of cybersecurity and digital forensics, two related fields that are critical for digital security. It explains the basic idea that cybersecurity is about preventing attacks (like a shield), while digital forensics is about investigating them after they happen (like a sword). The paper will look at how both fields developed over time, the main areas within cybersecurity, and the standard frameworks used, like the one from NIST. It will also cover the step-by-step process of a digital forensics investigation, including the importance of keeping a "chain of custody" for evidence. Using real-world examples like the Stuxnet worm, the Equifax data breach, and the WannaCry ransomware attack, this paper shows how these concepts are used in practice. It also discusses the legal and ethical challenges, such as data privacy laws like GDPR and CCPA. Finally, the paper looks at future challenges, including the shortage of skilled professionals, new ways hackers are hiding their tracks, the role of Artificial Intelligence, and the threat of quantum computing to modern encryption. The main point is that to be effective, cybersecurity and digital forensics must work together, with the results of investigations helping to build stronger defenses for the future.

 

 

Published by: vikaspatanker

Authors: Abhendra Pratap Singh, Nandini Sharma, Prince Kumar Sharma, Arpit Dwivedi, Aakriti Sharma

Abstract: The growing use of Artificial Intelligence (AI) in surveillance technologies changes how societies observe, predict, and manage security. From predictive policing to facial recognition, AI surveillance technologies offer real-time analysis, risk detection, and improved efficiency. Still, the rapid proliferation of such technologies brings issues of privacy, ethics, and accountability to the forefront. This review assesses the balance between human rights, AI ethics, and the surveillance technologies themselves. It demonstrates how China, the UK, and the USA have vastly different approaches toward data regulation, transparency, and consent. It also illustrates the major technical issues of algorithmic bias, data abuse, interoperability of privacy frameworks, and the ethics of large-scale surveillance and digital autonomy. By defining the gaps and analyzing the global pattern of such technologies, the paper aims to provide the most responsible and human-centric AI surveillance possible to guarantee privacy while also providing the oversight that people need

DOI: http://doi.org/10.5281/zenodo.17481315

Published by: vikaspatanker

Authors: Abhendra Pratap Singh, Nandini Sharma, Vanshika Dua, Arpit Dwivedi, Aakriti Sharma

Abstract: Composite materials are generated by intermingling two or more diverse components that are individually not able to do various tasks but when put together have become critically important in modern engineering due to their superior mechanical and structural traits. Fiber reinforced polymer (FRP) composites are utilized frequently in the aerospace automotive and construction industries more prominently. Despite their growing adoption, a continuing dilemma involves assessing natural fiber reinforced polymers (NFRP) over synthetic fiber reinforced polymers (SFRP) which differ greatly at the levels of performance cost and environmental impact. Both natural and synthetic composites have their own benefits and drawbacks such that synthetic composites offer excellent strength and durability and natural composites are gaining popularity due to their lightweight renewability and sustainability. This lack of unambiguous data driven comparison often leads to unclear judgment and leads to confusion in choosing the most viable composite for certain technical objectives. To eradicate this gap, the study examines three natural composites flax FRP, hemp FRP and jute FRP and three synthetic composites glass FRP, carbon FRP and aramid FRP. The paper uses computationally intensive analysis and machine learning methods such as linear regression and support vector machine (SVM) to figure out four crucial properties which mostly defines about the composite materials namely density, tensile strength, elastic modulus and moisture absorption. The visualized results of matplotlib based graphs provide a clear insight of how natural and synthetic composites perform individually and collectively through comparative analysis. This research incorporates AI assisted analytical modeling with scientific visualization to give a systematic and sustainable structure for selecting innovative composite materials.

DOI: http://doi.org/10.5281/zenodo.17481163

Published by: vikaspatanker