Authors - Sohesh Gandhe, Aditya Shirwalkar, Prathmesh Jomde, Shreyash Dhavale, Anil M. Bhadgale Abstract - Automatically generating Unified Modeling Language (UML) diagrams from unstructured software requirements remains one of the persistent challenges in modern software engineering. This paper introduces an intelligent project management framework that transforms client-provided requirement documents into accurate UML diagrams with minimal human intervention. Our system leverages Optical Character Recognition (OCR) to extract text from various document formats, employs a fine-tuned model for intelligent prompt synthesis, and utilizes a fine-tuned CodeLLaMA 7B model trained on prompt-to-MermaidJS code mappings. The generated diagrams—including sequence diagrams, flow charts, and Gantt charts—are rendered in real time through an integrated Mermaid Live Editor, providing immediate visual feedback within the project management interface. The experimental evaluation demonstrates substantial improvements in automation efficiency, reduced manual modeling effort, and improved consistency in UML generation. Our approach bridges the gap between natural language requirements and formal system design artifacts, offering a practical solution for automated software documentation and project planning at scale.
Authors - Trupti Shripad Tagare, K.L.Sudha, Nagendra Kumble, Sanketh T S, Belliappa M Abstract - The current developments in the design of aircraft have remarkably improved their overall performance. The parameter Rate of Climb (RoC) plays a very vital role in planning the trajectory of the flight, optimum fuel utilization and flight safety and is of significance for both technicians and pilots. The factors affecting RoC are weight of the aircraft, its design, and the atmospheric state. In this study, the estimation of real time RoC using predictive AI and deep learning is presented. The model is trained on real time flight data collected from Radome Technologies, Bengaluru. The parameters like drag, thrust, weight, climb angle and airspeed are provided as inputs to the model after preprocessing. The results show that the system achieves an enhanced predication accuracy with R2 of 0.9396, Root Mean Squared Error (RMSE) of 861.69 feet per minute and Mean Absolute Error (MAE) of 659 feet per minute. The efficiency and capability of several aircrafts can be measured and analysed using the rate of climb. The work greatly finds its important role in ground-based flight planning tools and in onboard decision-support systems. The fuel requirements for the aircraft can be reduced significantly by setting an optimum ROC. This will result in reduced costs and sustainable solutions. This work contributes to overall performance and safety, as the aircraft will maintain the optimal ascent using AI driven climb profile optimization.
Authors - Glenn Erick Zambrano Estupinan, Maria Genoveva Moreira Santos Abstract - Virtual Reality (VR) has gradually become an increasingly relevant technological tool in higher education, not only because of its innovative nature, but also due to its ability to create immersive experiences capable of capturing students’ attention and generating meaningful emotional responses. In this con-text, the aim of this study was to analyze the immediate emotional impact produced by a virtual reality experience on university students, using data mining techniques to identify patterns within the collected responses. The research followed a quantitative approach, with a descriptive–correlational and cross-sectional design, and included the participation of 305 students from the Faculty of Computer Sciences at the Technical University of Manabí. Each participant engaged in an immersive experience lasting approximately five minutes using the Meta Quest 2 device. After the activity, a Likert-type questionnaire, with a scale ranging from 1 to 5, was applied in order to evaluate variables such as perceived immersion, realism of the environment, level of attention, emotional interaction, empathy, and enthusiasm before and after the experience. The collected data were subsequently analyzed through exploratory and correlational analysis, as well as through several data mining techniques, including Principal Component Analysis (PCA), k-means clustering, and Apriori association rules. Overall, the results suggest that the virtual reality experience generated predominantly positive emotional responses among the students.
Authors - N. Revathy, V. Latha Sivasankari, Nikileshwar V, Surendhiran G, Abijith M, Sheik Mohamed S Abstract - Enterprise networks face escalating cyber threats as cloud, IoT, and remote work adoption expand attack surfaces. Traditional signature-based detection and manual response suffer average breach detection intervals of 287 days, failing to scale against rising alert volumes [9]. CyberSentinel addresses this through an autonomous pipeline processing Windows Security Event Logs: Isolation Forest anomaly detection on engineered behavioral features, large language model (LLM) threat explanations via local Ollama inference, and automated remediation including account deactivation, process termination, and firewall adjustment. A Flask web dashboard provides real-time threat visualization. Evaluation across 72 hours on a controlled Windows 10 Enterprise testbed with 28 injected anomalies confirms an F1-score of 0.78, 84.2% remediation success, and mean end-to-end latency of 24.7 seconds. The modular Python architecture enables fully autonomous operation on standard Windows hosts without dedicated SOC infrastructure.
Authors - Palgulla Rangaswami Reddy, Palla Maheswara Rao, Gogineni Hari Prasad, Guthikonda Akhila, T.V. Sai Krishna Abstract - The implementation and design of a covert communication channel that embeds hidden information within TCP/IP packet headers rather than within the actual payload of the packets is presented as a project. This is different than traditional embedding methods (steganography), which typically embed data into multime dia files, in that steganography in this case utilizes header fields that are not cur rently in use or can be modified so that TCP/IP packets can transmit hidden data. The fields that are used to transmit hidden data are the IP Identification Field, TCP Sequence Number, TCP Acknowledgment Number, and TCP Window Size. The sender module encodes and generates packets, and the receiver retrieves packets, extracts encoded bits, and reassembles data from the encoded bits found in the packets. The integrity of the data is verified using a checksum (SHA-256) and packet loss is reported. The lack of a payload will further enhance the stealth various data transmission methods may enjoy as it will circumvent conventional intrusion detection techniques (which primarily examine the payload data within packets). This project will demonstrate the ability to use this or similar covert communication channels to implement covert communication systems. In addi tion, covert communication channels can be used for different types of files and demonstrate the security and educational value of covert channel research in net work security.
Authors - Busrat Jahan, Kevin Osei-Onomah, Mansi Bhavsar, Hermela Dessie, Apu Chandra Bhowmik Abstract - In the global health sector, Diabetes is a major concern which needs accurate and effective models for early prediction. This work is quantitative re-search work. The dataset was collected from CDC Diabetes Health Indicators, and we used Light Gradient Boosting Machine (LightGBM) model for predicting diabetes. Since this research work is binary classification-based work, in our data preprocessing stage, we used Synthetic Minority Oversampling Technique (SMOTE) for controlling class imbalance and for feature selection we used Chi-square test to improve the model performance. The proposed LightGBM model showed its ability to recognize complex correlation between diabetes-related health indicators with the training accuracy of 92% and a ROC-AUC score of 0.97 on the test dataset. Overall, the findings highlight that predictive accuracy is significantly improved after applying both imbalance data controlling and most correlated feature selection techniques.
Authors - Ruby Bisht, Amit Kumar Uniyal Abstract - Digital transformation is reshaping education systems worldwide, with significant implications for rural and underserved regions. In India, initiatives aligned with the National Education Policy (2020) have promoted online learning platforms, digital classrooms, and technology-enabled teacher training to enhance access, equity, and quality in education. However, rural schools continue to face structural challenges such as limited infrastructure, digital divides, and inadequate teacher preparedness, which influence the effectiveness of digital integration.This conceptual paper examines the transformation of rural education in India from traditional teacher-centred classrooms to digitally enabled learning ecosystems. Grounded in Constructivist Learning Theory, the Technology Acceptance Model (TAM), Diffusion of Innovation Theory, and the TPACK framework, the study proposes an integrated conceptual model linking digital infrastructure, pedagogical innovation, and teacher competence to improved access, engagement, and learning outcomes. The paper argues that digital transformation represents a systemic pedagogical and institutional reform rather than a mere technological shift. Its success depends on inclusive infrastructure development, sustained teacher capacity building, and context-sensitive implementation in rural settings.
Authors - Ruby Bisht, Amit Kumar Uniyal Abstract - The rapid growth of Information and Communication Technologies (ICT) has profoundly altered educational systems by redefining teaching practices, institutional processes, and professional expectations. Within the broader context of sustainable development and smart education, ICT has emerged as an important facilitator of efficiency, accessibility, and innovation. This paper presents a conceptual analysis of how ICT can contribute to sustainable development through its influence on teachers’ work–life balance and job satisfaction in ICT-enabled learning environments. While ICT adoption has the potential to enhance instructional flexibility, autonomy, and efficiency, excessive digital connectivity, intensified workload, and blurred work–life boundaries may adversely affect teachers’ well-being. The paper identifies work life balance as a key mediating factor linking ICT use to job satisfaction and long term professional sustainability. Furthermore, the study situates teachers’ well being within the broader framework of sustainable development, emphasizing its relevance to Sustainable Development Goals such as SDG 3 (Good Health and Well-Being), SDG 4 (Quality Education), and SDG 8 (Decent Work and Economic Growth). The analysis underscores the need for human-centred, policy-driven, and ethically oriented ICT integration strategies that prioritize teacher well-being alongside technological advancement. The paper contributes to the discourse on sustainable and intelligent education systems by highlighting that the long-term effectiveness of ICT-driven educational transformation depends on balanced digital practices that support teachers’ work–life balance and job satisfaction.
Authors - Vasumathi R, Kalpana Y Abstract - Graduate communication competency gaps represent a critical barrier to the workforce readiness in the Indian higher education, yet existing assessment infrastructure measures a credential completion rather than the skill trajectories over time. This paper presents a LSTM-CDSF (Long Short-Term Memory Communication Demand and Skill Forecasting), a temporal deep learning based framework that predicts the future communication skill demand from the sequential monthly assessment records and also quantifies per skill gaps against the industry benchmarks. The framework operates on a synthetic dataset of 240 students observed over a period of 18 months calibrated to published NASSCOM and India Skills Report statistics. LSTM-CDSF achieves a Mean Absolute Error of 1.468, RMSE of 1.837, MAPE of 2.61%, and R² of 0.9249 on a held-out test set of 480 sequences, demonstrating consistent performance improvements over the Linear Regression, ARIMA, and a naïve baseline across all the evaluated metrics. Gap analysis reveals that the Digital Communication (gap: 25.4 points) and the Intercultural Communication (gap: 23.5 points) requires the most urgent curriculum interventions.
Authors - M. Kamaraju, B. Rajasekhar, V.N.V.R. Karthik, V.N.L. Mahima, Y.H.V. Satya Narayana, R. Pujitha Abstract - This manuscript presents a dedicated Application-Specific Integrated Circuit (ASIC) architecture purpose-designed for computing eigenvalues of two-dimensional square matrices in resource-constrained embedded systems. The fundamental challenge motivating this work stems from the computational intensity of eigenvalue decomposition in digital signal processing, robotics control systems, and embedded analytics, where conventional software implementations incur unacceptable latency and power overhead. The proposed solution lever-ages the closed-form algebraic solution inherent to 2×2 matrices, eliminating iterative numerical methods and their associated performance penalties. Our design employs a direct characteristic-equation approach mapped onto dedicated arithmetic circuits including parallel multipliers, adders, and a specialized square-root computation unit implementing the non-restoring digit-re-currence algorithm. The Verilog RTL synthesized using Cadence Genus in a 180 nm CMOS standard cell library yields a compact silicon footprint of 1,703 square micrometers utilizing 196 standard cells, with measured power dissipation of 0.5738 milliwatts at 100-megahertz operation. Timing closure is achieved with positive slack under worst-case process-voltage-temperature conditions. The high dynamic-to-static power ratio of 98.66 percent to 1.34 percent indicates activity-dominated power behavior, confirming successful implementation of low-leakage design principles. These metrics demonstrate that the proposed architecture constitutes an effective hardware acceleration solution for eigenvalue computation in battery-powered and always-on applications where conventional approaches prove infeasible.
