Category Archives: Uncategorized

Authors: Shivendra Singh, Manoj Yadav

Abstract: Tannery effluents are a major source of chromium contamination, particularly hexavalent chromium [Cr(VI)], which is highly toxic, carcinogenic, and persistent in aquatic systems. Conventional treatment methods for chromium removal are often costly and environmentally unsustainable. This study investigates the potential of low-cost, eco-friendly agro-waste materials—sawdust, clay, and used tea leaves—as adsorbents for the removal of both total chromium and hexavalent chromium from tannery effluent. Batch adsorption experiments were carried out to evaluate the effect of parameters such as pH, contact time, adsorbent dosage, and initial chromium concentration. The results demonstrate that all three materials exhibit significant adsorption capacities, with efficiency varying across the different adsorbents. Sawdust and used tea leaves showed higher affinity towards Cr(VI), while clay exhibited better overall performance in reducing total chromium levels. The adsorption process was found to follow pseudo-second-order kinetics and fit well with the Langmuir isotherm model, suggesting monolayer adsorption on homogeneous surfaces. This study highlights the feasibility of employing locally available agro-waste adsorbents as sustainable alternatives to conventional methods for the treatment of tannery wastewater, thereby contributing to cost-effective and environmentally friendly wastewater management

DOI: http://doi.org/10.5281/zenodo.17098961

Published by: vikaspatanker

Authors: Mrs. Kocherla Jayanthi

Abstract: The rapid evolution of cyber threats demands effective and adaptive intrusion detection systems to protect critical network infrastructures. This study seeks to evaluate the efficacy of supervised machine learning models in detecting network intrusions using the NSL-KDD dataset. The NSL-KDD dataset, a well-established benchmark for intrusion detection, undergoes thorough pre-processing, including handling missing values, feature normalization, and categorical encoding to ensure high-quality input data. We implement a range of supervised machine learning algorithms Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbours (KNN), Gradient Boosted Trees, and Support Vector Machine (SVM) to classify network traffic as either benign or malicious. The process involves splitting the dataset into training and testing subsets, followed by hyperparameter optimization through grid search to enhance model performance. We evaluate the models using key metrics such as accuracy, confusion matrix, Receiver Operating Characteristic (ROC) curve, and Area Under the Curve (AUC). Our findings reveal that Random Forest and Gradient Boosted Trees achieve superior accuracy and lower false positive rates compared to other classifiers. The comparative analysis provides practical insights into each algorithm’s strengths and limitations for cybersecurity applications.

DOI: http://doi.org/10.5281/zenodo.17098664

Published by: vikaspatanker

Authors: Vishal Yadav, Manas Mishra, Aditya verma

Abstract: In Ayodhya, Uttar Pradesh, India, the Saryu River is revered as a sacred river. However, the quality of the water is declining as a result of human anthropogenic activities. The goal of the current study was to use established techniques to evaluate the quality of river water by analyzing bacterial populations and physicochemical characteristics with seasonal fluctuations. The majority of the physicochemical parameters, primarily pH, DO, BOD, and TDS, were found to be within the allowable levels that regulatory bodies had suggested. Other parameters, such as Alkalinity or Fluorite and chemical oxygen demand (COD), were marginally above the allowable limits. Microbial investigations revealed the existence of both fungal and bacterial communities. The rainy season has the highest bacterial concentration, followed by the summer and winter seasons. The results of this study may help with irrigation and drinking water quality monitoring throughout the year

DOI: https://doi.org/10.5281/zenodo.17098581

 

Published by: vikaspatanker

Authors: Miss. Mounika Lokavarapu, Dr.G. Sharmila Sujatha

Abstract: Object recognition plays a crucial role in computer vision applications, particularly in assisting visually impaired individuals for safe and independent navigation. Despite its significance, existing techniques often face limitations in recognizing multiple objects efficiently and accurately. The aim of this work is to develop a robust multi-label object recognition framework capable of detecting and classifying surrounding objects in real time to enhance situational awareness for visually impaired users. The proposed system takes real-world images as input and processes them using machine learning and advanced computer vision algorithms. A multi-label classification approach is employed to simultaneously detect and group objects, reducing detection time while improving recognition accuracy. By leveraging deep learning models with optimized type/grouping techniques, the system achieves faster execution with best-in-class time complexity. Experimental analysis demonstrates that the framework not only improves detection performance but also provides reliable object recognition in both indoor and outdoor environments, making it highly effective for real-world navigation assistance. The proposed framework, “AI-Powered Assistive Vision: A Novel Deep Learning Framework for Object Detection and Recognition for the Visually Impaired,” is developed using Python with TensorFlow/Keras and OpenCV libraries, and implemented under embedded hardware with camera and processing units, enabling real-time deployment for assistive navigation applications.

DOI: http://doi.org/10.5281/zenodo.17098418

Published by: vikaspatanker

Authors: Ms. S. Sushma Rawath

Abstract: The rapidly evolving global scenario, characterized by technological advancements, geopolitical shifts, and socio-economic transformations, has led to the emergence of "new markets." These markets are driven by a combination of digital innovations, environmental imperatives, demographic changes, and evolving consumer preferences. Opportunities in areas like renewable energy, digital finance, sustainable agriculture, and advanced healthcare define new markets that are no longer constrained by traditional industrial or geographic boundaries. Furthermore, the globalization of technology and digital platforms has enabled businesses to access previously untapped regions and demographics, particularly in developing economies. This paper explores the drivers behind these emerging markets, their implications for global trade, and strategies businesses can adopt to thrive in this dynamic environment. It also highlights the challenges associated with navigating regulatory complexities, cultural differences, and technological disparities. Understanding and adapting to these new markets is crucial for fostering inclusive and sustainable economic growth in the 21st century.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Priya Jaiswal

Abstract: This study presents the development and evaluation of an Environmental Quality Index (EQI) for Lucknow city, aimed at assessing the environmental factors that influence public health outcomes. The EQI is designed to integrate three critical environmental components—air quality, water quality, and green cover—which are known to have direct and indirect effects on human health. Data spanning from 2020 to 2024 were collected from reputable government sources, including the Central Pollution Control Board (CPCB), Central Ground Water Board (CGWB), and the Forest Survey of India (FSI). These datasets were systematically processed, analyzed, and normalized to create a composite index that represents the overall environmental condition of the city in relation to public health risks. The results indicate that Lucknow’s environmental quality generally falls within the moderate to poor range, reflecting significant challenges for maintaining population health. Rising levels of air pollutants, persistent water contamination, and limited improvement in urban green spaces collectively contribute to increased vulnerability to respiratory, cardiovascular, and waterborne diseases. Year-wise analysis reveals gradual deterioration in air and water quality, highlighting the urgent need for targeted public health interventions and environmental management strategies. The EQI developed in this study provides a valuable tool for policymakers, health authorities, and urban planners to identify high-risk areas, prioritize interventions, and monitor the effectiveness of measures aimed at reducing environmental health hazards.

DOI: https://doi.org/10.5281/zenodo.17098074

 

Published by: vikaspatanker

Authors: Amir Shabbir Patel, Sahil AA Khan, Dhairya Suryawanshi, Rohan Karchuli

Abstract: The real estate industry is currently experiencing a rapid digital transformation, largely fueled by the integration of artificial intelligence (AI) technologies. Among the most promising applications is the use of AI-powered recommendation systems, which aim to redefine how buyers, sellers, and agents interact with property platforms. These intelligent systems are designed to analyze large volumes of property data and user preferences, offering highly personalized recommendations that improve the overall user experience. By leveraging data-driven insights, AI has the potential to simplify property discovery, reduce the complexity of decision-making, and enhance overall market efficiency. This study explores the implementation of different AI models, including machine learning algorithms, deep learning techniques, and natural language processing (NLP), within the context of real estate platforms. We evaluate their ability to process structured and unstructured data such as location, price, amenities, and even user reviews or natural language queries. A prototype recommendation system was developed and tested using real user behavioral data, including browsing history, clicks, and saved properties. The case-based experiment demonstrated that AI- enabled recommendations not only improved engagement but also significantly reduced search time, making the property- hunting process more efficient and user-centric. In addition to the technical benefits, this paper also examines the broader challenges and ethical considerations associated with AI adoption in real estate. Issues such as data privacy, algorithmic bias, and transparency in recommendations are highlighted as key areas that require careful attention. Furthermore, the study identifies opportunities for future research, such as integrating predictive analytics for market trends, enhancing trust through explainable AI, and expanding personalization by considering emotional and lifestyle factors. By addressing these challenges and advancing the current models, AI-driven recommendation systems can play a transformative role in shaping the future of the real estate industry. [4].

Published by: vikaspatanker

Authors: Saiyed Aiyatullah Kalimullah, Malek Mohammadarsh Mohammedasif, Jethava Shyam Hiteshbhai, Chudasma Dhruv Dineshbhai

Abstract: This project presents Care Smart AI, a compre- hensive Hospital Management System (HMS) integrated with artificial intelligence to improve healthcare delivery and op- erational efficiency. The system leverages modern full-stack technologies including Flask for backend API services, MongoDB for data persistence, React and TailwindCSS for responsive user interfaces, and machine learning for symptom assessment and diagnostic report summarization. Care Smart AI enables secure, efficient patient management with role-based access for patients, doctors, and administrators. It demonstrates a scalable, acces- sible, and intelligent platform that enhances clinical decision- making, automates administrative tasks, and improves patient care quality across healthcare institutions.

Published by: vikaspatanker

Authors: Mohamed Asiq .A, Santhosh M, Vishal.S, D.Jeevanantham,B.E

Abstract: Wastewater treatment is essential for safeguarding public health, protecting ecosystems, and supporting sustainable urban development. This report presents insights from anacademic field visit to the Government Wastewater Treatment Plant (WWTP) at Pollachi, Tamil Nadu. The plant is based on Sequential Batch Reactor (SBR) technology, which provides an efficient and compact solution for secondary treatment of municipal sewage. During the visit, students observed the general layout of the facility, including preliminary units (receiving chamber, screens, grit chambers), secondary biological treatment (SBR reactors, decanters), tertiary treatment (chlorination chambers, contact tanks), and sludge handling units (sludge well, centrifuge building). The plant also houses supporting infrastructure such as laboratory facilities, blower rooms, and landscaped green belts that enhance both aesthetics and environmental protection. The visit provided practical exposure to treatment operations, sludge management, effluent quality monitoring, and safety protocols. It also highlighted the broader significance of WWTPs in ensuring sustainable sanitation, preventing water pollution, and promoting wastewater reuse. This report connects classroom knowledge of environmental engineering with real-world field practice, emphasizing the critical role of wastewater treatment plants in urban infrastructure.

Published by: vikaspatanker

Authors: Sangeeth Kumar.J, Janarthanan.V, Logeshwaran.S, D.Jeevanantham,B.E

Abstract: Water treatment plants (WTPs) play a fundamental role in delivering safe and reliable drinking water to urban and rural populations. This journal paper documents a field visit to the Pollachi Water Treatment Plant (WTP) located at Kolathur Village, Pollachi Taluk, Coimbatore District, which is part of the Combined Water Supply Scheme (CWSS) supplying Pollachi North, Pollachi South, Kinathukadavu, Gudimangalam, and adjoining habitations. The scheme sources water from the Aliyar River, with an intake well and raw water pump house that lifts water for treatment. The plant consists of headworks, aerator, stilling chamber, flash mixers, dividing chambers, clariflocculators, filter beds, clear water sump, and chemical treatment units for coagulation, flocculation, and disinfection. During the visit, the operation of raw water pumping mains, filter media layers, chlorination arrangements, laboratory facilities, booster pumping stations, and service reservoirs were observed. With a designed treatment capacity of 26.38 MLD, the scheme ensures reliable water supply to urban wards and more than 200 rural habitations. This field exposure enabled students to understand the engineering design and operational aspects of drinking water treatment and distribution, bridging theoretical knowledge with field practice

Published by: vikaspatanker