New problems have been created in ‘aero-elastic stability' in modern Formula 1 ground-effect aerodynamics. Of particular interest is high-frequency oscillations, "porpoising" and structural wing flutter. This paper develops an idea of Multi-Modal Sensor Fusion framework which is applicable to real-time aerodynamic stability monitoring and mapping. This design is distinctly different from existing telemetry designs based on data transfers from individual sources operating with high speed optical deflection monitoring and high frequency chassis vibration monitoring (Inertial Measurement Units). The system uses a deep-learning network detection pipeline to detect the visual deformation of the structure and crosses this with a Z-axis impulse algorithm to calculate the quantity of vertical load fluctuations. These inputs are then added together to create a dynamic Instability Index that may be in one of three states: Critical, Transitional, Stable. Experimental simulations have demonstrated that this two modal concept has been possible with a very high level of accuracy (and a 94% success rate) to predict aerodynamic stalls as well as to reduce the “signal noise” generated by track irregularities or kerb strikes. This output is automatically visualized with an engineer ready prioritized visualization in a Spatial-Temporal Track Dashboard describing the location where an aero-elastic instability is impacting the performance of the vehicle. This can be implemented on a wide scale and becomes an edge computing solution to enhance lap time and vehicle safety in very high downforce.

Keywords : Aero-Elasticity, Formula 1, Sensor Fusion, DL, Ground-effect, RTSM.

Authors : Ramya Thudumu

Title : Multi - Sensor Fusion for Real-Time Aero-Elastic Stability Mapping in Ground-Effect Formula 1 Vehicles

Volume/Issue : 2026;3(2 ( April - June ))

Page No : 23 - 29

The early detection of Alzheimer's Disease (AD) is important to slow the disease's progression and manage patient care. But conventional diagnosis techniques are often expensive, invasive, and not scalable for screening. The proposed solution integrates handwriting recognition and speech recognition for early diagnosis of AD in a non-invasive way. The approach employs a Random Forest model for handwriting features and a Convolutional Neural Network (CNN) for speech signals. The two modalities are integrated by a decision-level fusion scheme. The findings indicate that the developed model has an overall accuracy of 96.8%, surpassing the accuracy of the individual models, and has good robustness for practical screening.

Keywords : Multimodal Learning, Handwriting Analysis, Speech Recognition, Deep Learning, Decision-Level Fusion.

Authors : B. Sree Charan , D. Bharani , P. Vishnuvardhan Reddy , P. Manasa

Title : A Multimodal Approach for Early Alzheimer’s Disease Detection Using Handwriting and Speech Analysis

Volume/Issue : 2026;3(2 ( April - June ))

Page No : 30 - 36

Due to crowd congestion in overcrowded areas, severe conditions such as panic and stampede may occur. These events should be predicted at an earlier stage and thus the crowd will be better managed and safe. In this paper a camera based system is introduced that predicts the congestion by analyzing the motions of the people in conflict areas as well as how their flow can become free-flowing. This system includes video, and it calculates the direction and velocity of every individual motion (optical flow) and clusters the similar motion vectors to discover key flow patterns. When counter flows come together, the system identifies such points as the conflict zones and monitors their dynamics over the passage of time to assess the risks of congestion. It also provides a turbulence index in order to quantify motion field instability. These features are fed in three prediction models, a simple linear model, a Kalman filter, and an LSTM neural network to approximate the congestion time before it happens. It is also a method used in measuring how early it can predict congestion in advance of it taking place. Real crowd video experiments indicate that incorporation of awareness of turbulence can provide reliable early congestion indicator and this method is more effective than purely basic methods.

Keywords : Crowd Analysis, Congestion Prediction, Crowd Turbulence, Optical Flow, Pedestrian Dynamics.

Authors : Sathvik Rangu , Allu Siva Kumar , Sura Manoj

Title : Quantum Intelligence Turbulence-Aware Crowd Flow Conflict Modeling for Early Congestion Prediction

Volume/Issue : 2026;3(2 ( April - June ))

Page No : 23 - 29

The fast implementation of online examination systems has yielded a dire requirement of proctoring solutions that safeguard and maintain privacy. Standard webcam-based surveillance systems improve detection rates and add serious privacy problems, excessive computing needs and reliance on a well-functioning network presence. In order to overcome these shortcomings, this paper suggests a risk-conscious and privacy-conscious online examination framework that gets rid of constant video surveillance by using the behavioural analysis of browsers, identifying anomalies, and recording the logs using blockchain technology. One of the systems identifies suspicious behaviour patterns like tab switching, inactivity, and patterns of abnormal interactions by a weighted risk-scoring model. Moreover, a lightweight anomaly detection system is incorporated to detect the violations of the regularity of behaviours. Critical events and examination results are safely stored in blockchain logging based on SHA-256 in order to guarantee data integrity and non-repudiation. Experimental outcomes show that the proposed system is highly accurate in detecting and has low latency without compromising user privacy. The structure offers an efficient, scalable and technically sound system to the current web-based test setting.

Keywords : Online Examination, Privacy-preserving Proctoring, Behavioural Anomaly Detection, Blockchain, SHA-256.

Authors : V.Chelshiya , S.Charan Reddy , V.Adil Jaiwant , B. Hemanth , G. Sai Prasanna

Title : A Risk-Aware Privacy-Preserving Online Examination System Using Behavioural Anomaly Detection Using Blockchain Technology

Volume/Issue : 2026;3(2 ( April - June ))

Page No : 12 - 22

In this paper, an algorithm is defined as an ordered set of processes or rules designed to achieve specific objectives. In the context of this study, an algorithm refers to the ordered set of computing processes used to enhance image processing. This research focuses on designing an algorithm for image processing, restricted to a new algorithm that employs cutting-edge techniques in both image processing and computing. The algorithms examined in this study will be limited to those suitable for real-time applications and high-resolution images. The study will compare algorithms, excluding others outside this scope. Performance metrics such as speed, efficiency, and reliability will be key parameters, considered without regard for cases beyond those specified during the algorithm's design. Genetic algorithms, differential evolution, and rank-based uniform crossover are the fundamental features of the two algorithms proposed for optimisation of image processing chains; specifically, LCHF, IPCO, and MIPCO.

Keywords : LCHF, IPCO, MIPCO, Networks, Computing , MATLAB,

Authors : B V Sowjanya , J Nageswara Rao , N Shehanaaz

Title : Multi-Algorithm Image Processing Chain Optimization for Efficient and Accurate Image Analysis

Volume/Issue : 2026;3(2 ( April - June ))

Page No : 6 - 11