A Network Security Monitoring System using Deep Learning
In an era of evolving and increasingly complex cyber threats, the importance of robust network security is paramount. This paper presents a novel method of strengthening network defenses by building a highly flexible and durable Network Security Monitoring System (NSMS). By utilizing deep learning, more especially self-taught learning (STL), we set out to reinvent network security. In this study, we apply STL to the well-known NSL-KDD dataset, which is a commonly used network security monitoring system benchmark. We thoroughly analyze our NSMS solution's performance utilizing a range of important metrics, such as accuracy, precision, recall, and F-measure, to determine its overall effectiveness. Impressively, this method produced a 92.84% accuracy on the training set. As we use both the training and testing datasets in our work, our research expands on this basis and provides a distinct advantage for comparison, allowing a straight comparison to this earlier work. This study's main importance comes from its ability to prevent intentional attacks and to proactively identify unanticipated and unforeseeable security breaches. This research represents a milestone in the development of NSMS technology in the dynamic cybersecurity landscape, enabling enterprises to strengthen their security posture and protect their assets in a world that is becoming more interconnected.
Published by: Pramodh Puthota, MR. G.Sivannarayana, Pasupuleti Bhavana Pradeepa Rani, Siriki Sravya, Vaddeswarapu Rahul
Author: Pramodh Puthota
Paper ID: V11I4-1201
Paper Status: published
Published: August 4, 2025
Exploration of Biosurfactant Producing Microorganism from Garage Soil: Production, Characterization, and its Application
Biosurfactants are bioactive surface molecules produced by microorganisms, gaining notoriety for their environmentally friendly and biodegradable characteristics. This research emphasizes the extraction, production, and analysis of biosurfactants from hydrocarbon-polluted soils collected from a garage and truck terminal in the Yeshwanthpur industrial region. The samples were enriched using Mineral Salt Medium (MSM), and bacterial strains were isolated through serial dilution and pour plate methods. The identification of biosurfactant-producing bacteria was performed utilizing drop collapse, oil displacement, and emulsification assays. Among the isolates, isolate 2 exhibited the most promising results and was chosen for further research. Gram staining, endospore staining, and biochemical tests revealed the organism to be Bacillus cereus. Optimization of biosurfactant production was achieved by adjusting pH, temperature, incubation duration, inoculum volume, and nutrient sources. The maximum biosurfactant yield was attained with 250 µl of inoculum and with optimum physical parameters of pH 6 and temperature 35°C at a 24-hour incubation period, with glucose and peptone as carbon and nitrogen sources, respectively. The biosurfactants were extracted through acid precipitation followed by solvent extraction using chloroform and methanol. The characterization of the crude biosurfactant was performed. The antimicrobial properties against selected bacterial and fungal strains were assessed using the agar diffusion method, and bioremediation potential was evaluated. Distinct zones of inhibition confirmed the antimicrobial efficacy of the biosurfactant. These results imply that Bacillus cereus isolated from garage soil contains effective biosurfactant-producing potential and can be used in environmental bioremediation and antimicrobial property, offering a sustainable substitute for synthetic surfactants.
Published by: Namitha K, Bindu P, Mohammed Faizal, Anuroopa N
Author: Namitha K
Paper ID: V11I4-1200
Paper Status: published
Published: August 4, 2025
MediNav: An AI-Driven Specialist Referral Tool to Reduce Wait Times in Indian Public Hospitals
Public hospitals in India face significant challenges with long patient wait times, particularly due to disorganized referral systems. Many patients approaching these hospitals come from underserved backgrounds or have limited health literacy and often struggle to identify the appropriate type of doctor for their specific health issues. As many public hospitals in India lack a structured first point of contact or General Practitioner (GP) system, this confusion contributes to unnecessary delays and increased wait times. To address this issue, the study introduces MediNav, an AI-powered tool designed to evaluate patient symptoms and guide them to the right type of doctor for consultation. By doing so, MediNav enhances patient flow and minimizes unnecessary delays, particularly benefiting those who may not know how to navigate the healthcare system. The AI model, developed using XGBoost on symptom–specialty data, achieved an overall accuracy of 85.58% in live primary healthcare (PHC) settings. Through a comparative assessment of wait times, MediNav has the potential to reduce patient waiting time stemming from misreferrals or department transfers by an average of 39.4 minutes per individual in public Indian hospitals. In the absence of a GP or structured referral layer, such inefficiencies are common in India’s public hospitals. With typical patient volumes of 500 or more per day, this translates to over 328 clinical hours saved daily. This significant reduction can enhance clinical efficiency within strained public health systems, ultimately improving access to care for all patients, especially those with limited understanding of the healthcare process.
Published by: Akshita Mangal
Author: Akshita Mangal
Paper ID: V11I4-1191
Paper Status: published
Published: August 1, 2025
Empowering Indian Educators through AI: Transforming Faculty Development and Pedagogical Practices in Higher Education
The rapid integration of Artificial Intelligence (AI) into education systems is redefining teaching and learning paradigms across the globe. In India, with the rollout of the National Education Policy (NEP) 2020 and the push for digital transformation, AI holds the potential to completely change faculty development and pedagogical practices in higher education. This research investigates the impact of AI-based tools on Indian educators, focusing on personalized teaching, adaptive learning systems, and data-driven decision-making. The study examines how AI technologies empower faculty through continuous upskilling and reskilling, enabling them to adapt to evolving learning environments. Using AI-driven Learning Management Systems (LMS), teachers can now access real-time analytics, automate assessments, and personalize student feedback. However, the adoption of these technologies depends on institutional readiness, faculty digital literacy, and infrastructure availability. A structured questionnaire was distributed to 100 faculty members across Indian universities. The data was analyzed using SPSS tools, including percentage analysis, multiple regression, and chi-square analysis. The study identifies eight key factors influencing faculty empowerment: digital literacy, institutional support, training programs, AI integration in LMS, perceived usefulness, ease of use, policy awareness, and resistance to change. Results reveal a strong correlation between AI adoption and enhanced teaching effectiveness. Faculty members exposed to AI tools demonstrated increased engagement, better course customization, and improved student performance metrics. However, challenges such as lack of AI training, fear of redundancy, and inadequate infrastructure remain. The study emphasizes the need to implement structured AI competency programs and clear policy directives under NEP 2020. This research contributes to ongoing discourse by offering a faculty-centric perspective on AI adoption in Indian higher education. It offers concrete recommendations for policymakers, academic leaders, and EdTech developers to collaboratively design future-ready academic ecosystems.
Published by: Dr.V.Victor Solomon
Author: Dr.V.Victor Solomon
Paper ID: V11I4-1188
Paper Status: published
Published: July 30, 2025
Gender, Violence, and Memory in Meena Alexander’S Nampally Road
Meena Alexander's Nampally Road (1991) is a powerful postcolonial feminist novel that weaves together themes of gender, violence, and memory within the socio-political landscape of contemporary India. Set in Hyderabad, the novel explores the psychological and physical trauma inflicted upon women by systemic patriarchal and political violence. This research paper analyzes Nampally Road as a literary site where female subjectivity, resistance, and memory converge to critique both colonial and postcolonial structures of oppression. Drawing on feminist and postcolonial theories, the study interrogates how Alexander constructs her female protagonist Mira's journey as emblematic of the broader struggles faced by Indian women. The novel ultimately becomes a space for reimagining justice, healing, and agency in the face of deeply rooted violence.
Published by: Dr. Ganesh Pundlikrao Khandare
Author: Dr. Ganesh Pundlikrao Khandare
Paper ID: V11I4-1186
Paper Status: published
Published: July 30, 2025
Towards Inclusivity: An Analysis of the National Education Policy 2020’s Potential to Address the Educational Needs of Marginalised Groups
The National Education Policy (NEP) 2020 represents a paradigm shift in India’s educational landscape, promising equity, inclusivity, and quality for all. Falling into the paradigm of developmental economics, this paper critically examines the policy’s potential to address the needs of historically marginalized communities—including socio-economically disadvantaged groups (SEDGs), Scheduled Tribes, children with disabilities, and gender minorities—through a focused analysis of key provisions such as Early Childhood Care and Education (ECCE), Special Education Zones, the Gender-Inclusion Fund, and Open and Distance Learning (ODL). Drawing upon official policy documents, existing educational statistics, and the broader socio-political context, this study evaluates whether NEP 2020 offers not just symbolic inclusion but substantive structural change. The paper argues that while the NEP makes commendable strides in intent and policy design, its success depends critically on effective implementation, inter-sectoral coordination, and sustained financial commitment. By highlighting both the strengths and gaps within NEP 2020, this analysis aims to contribute to ongoing discourse on educational equity and inform future policy refinements to better serve India’s most vulnerable learners.
Published by: Suhanee Soni
Author: Suhanee Soni
Paper ID: V11I4-1183
Paper Status: published
Published: July 29, 2025
