Mahakal Institute of Technology

Structural vibration control in skyscrapers with asymmetric floors is challenging due to complex dynamic responses from seismic, wind, and occupant loads. Traditional Tuned Mass Dampers (TMDs) lack adaptability, whereas Active Tuned Mass Dampers (ATMDs) provide better control but require precise optimization. Hence, a Neuro-Evolutionary Resonance Vibration Optimization is proposed for ATMD systems in asymmetric buildings. Asymmetric floor structures complicate traditional ATMD control due to nonlinear behavior, latent hysteresis effects, and differential stiffness zones, causing unexpected vibrational modes. To tackle this, a Neuro-Adaptive Resonance Vibration Control System is introduced, this model’s nonlinear structural behaviour and latent hysteresis effects using fuzzy logic, while RNN enhances predictive accuracy by maintaining temporal dependencies, thereby providing a real-time vibration suppression. Additionally, SSI complicates existing optimization due to unpredictable variations in natural frequency and damping behavior. To overcome this, a Multi-Objective Elephant Herding Ant Optimization (MEHAO) is presented. Multi-Objective Ant Colony Optimization (MOACO) explores solutions that minimize displacement, acceleration, and energy use, while Elephant Herding Optimization (EHO) refines them for rapid convergence, thus enabling dynamic ATMD adaptation to soil variations. As a result, the suggested model successfully suppresses vibration while attaining low acceleration and displacement when compared to the existing methods.

The study deals with aerodynamic analysis of a general aviation aircraft Piper PA-24, a monoplane with low-wing configuration using FlightStream, a panel-method based vorticity solver. The present-day preliminary design phase requires a full-scale design analysis mainly to understand the design and aerodynamic performance in a fully integrated aircraft with all the external components attached to the aircraft. Unlike the discretised Navier–stokes equation-based solver, FlightStream uses potential flow Laplace equation to solve for the flow physics reducing the dependency on complex three-dimensional meshes for preliminary design stage analysis. The aircraft is predominantly analysed to observe the aerodynamic performance for angle of attacks varying from -4^∘ to +20^∘ , and validated with the available experimental data. The study also includes the grid independence check and thrust coefficient analysis to optimize the aerodynamic performance at cruising conditions. The findings will benefit aerospace engineers and researchers involved in aircraft design, aerodynamic optimization, and rapid preliminary analysis.

Accurate documentation of meetings is essential for effective communication, decision-making, and accountability across various settings, including corporate environments, academic discussions, research collaborations, project reviews, and multi-stakeholder forums. Manual note-taking is often error-prone and time-consuming, highlighting the need for automated solutions. This paper proposes an LLM-based approach for generating structured Minutes of Meeting (MoM) from audio recordings. The method begins with audio transcription using the Vosk model, followed by grammatical and punctuation recovery to enhance text readability. The preprocessing step cleans and segments the transcribed text. Named Entity Recognition (NER) is applied to identify relevant entities such as names, dates, and organizations. The method then segments the conversation into topical sections aligned with the meeting agenda. A Large Language Model (LLM), named OpenAI, has been used to produce abstractive summaries. An action item extraction module identifies tasks, responsible individuals, and deadlines. A speaker analysis component highlights principal participants in decision-making processes. The system organizes the final output into a structured format, such as JSON or PDF, for easy access and distribution. This end-to-end approach enhances the clarity, accessibility, and utility of meeting documentation, reducing manual effort and improving organizational productivity.

The Internet of Things for Medical Devices, also known as IoT-MD, is a network of sensors, actuators, and other mobile communication devices that are all connected to one another. This network has the potential to significantly enhance the delivery of medical care. Connected health technology has been increasingly popular in recent years for a number of reasons, including the growing incidence of chronic diseases and the pressing requirement to reduce the rising cost of medical care that is linked with an ageing population. An IoT network contains a lot of data, making it a target for fraudsters, therefore securing it is essential. The Mirai malware uses a botnet to remotely attack big networks via IoT device vulnerabilities. Several ransomware strains compromised many Internet of Things systems worldwide. Because most IoT devices capture and transfer sensitive data, these assaults should wake up all IoT ecosystems to improve security. This manuscript presents Adaptive Intrusion Detection System towards Secure Internet of Things Enabled Intelligent Healthcare Systems. Methodology consists of input data set and deep learning techniques. NSL KDD data set is used as input in this framework Model is build using LSTM, CNN and AdaBoost algorithms. Experimental results have shown that the accuracy, sensitivity and specificity of LSTM is better for detecting intrusions in order to secure IoT enabled healthcare applications.

The ecosystem degradation and climate change are increasing at a very high rate, and this phenomenon must be monitored with the aid of the most advanced technologies to organize the management of resources in the most efficient manner. In the paper, a smart visual analytics system is proposed that may be applied to utilize the remote-sensed data on the holistic monitoring of the environment. The model applies multispectral and hyperspectral satellite images and machine learning and deep learning models to determine crucial indicators of the environment (such as vegetation cover, water quality, and urban growth patterns). A graph-based clustering approach with Convolutional Neural Networks (CNNs) can be used to extract features and cluster data to identify anomalies in real time to interpret geospatial data on a big scale. The results show that the specified system would be effective in the land cover classification with an accuracy of 93%, in contrast to the accuracy of the conventional remote sensing analytics tools, which is 15 percent higher. In addition, the use of normalized different indexes estimation of water quality led to the R² of 0.87 that indicated the high predictive reliability. The visual analytics dashboard can dynamically interact with spatial-temporal patterns in such a way that the sustainability of the available resources can be managed by the decision-making process.