Developed a novel BMFCNet architecture integrating blended multi-level feature extraction for robust detection of Major Depressive Disorder (MDD) from EEG signals. Introduced a Residual-Inception module to effectively capture both low-level (LL) and high-level (HL) discriminative EEG features, enhancing representational capacity. Developed a Constraint Fusion mechanism for adaptive weighting and fusion of LL and HL features, improving feature integration and classification performance. Addressed subjectivity in MDD diagnosis by providing an automated, EEG-based framework that enhances accuracy, reliability, and clinical applicability. Validated the proposed model on benchmark datasets, demonstrating superior performance compared to 16 state-of-the-art methods in terms of accuracy and efficiency.

• Veracity-aware fusion combines CLIP, SBERT, and NLI to capture image–text incongruity for sarcasm understanding. • Ordinal sarcasm modeling predicts intensity levels (Not, Mild, High) instead of simple binary classification. • Dual-view learning uses original and masked-text memes to reduce template bias and improve generalization. • CLIP + transformer architecture enables effective cross-modal interaction between image and text features. • Template-grouped 5-fold validation prevents data leakage and ensures realistic model evaluation. • Multi-loss training strategy (contrastive, incongruity, ordinal) improves feature alignment and detection performance.

• The system utilises Wi-Fi signal strength mapping to follow a designated operator, employing unique identification to ensure precise tracking in crowded environments. • Equipped with ultrasonic sensors for real-time person tracking and Bluetooth-enabled remote control for manual operation, the trolley maintains optimal distance and adapts to changes in the operator's speed. • Additionally, a load cell system ensures safe load management and alerts the user above the threshold limit of weight.

• Converts solar irradiance into chemical energy to produce hydrogen via water electrolysis, enabling renewable fuel generation. • Employs a hybrid dual-fuel engine (hydrogen–diesel blend) optimized for irrigation pumping, enhancing combustion efficiency and operational reliability. • Significantly decreases reliance on conventional fossil fuels while mitigating greenhouse gas emissions and environmental impact. • Delivers a cost-efficient, low-carbon solution for agricultural irrigation with reduced long-term operating expenses. • Demonstrates high scalability and adaptability across diverse farm sizes, irrigation demands, and geographic conditions. • Achieves improved overall energy efficiency relative to conventional diesel-powered irrigation systems through hybridization.

• Dual-mode operation (Heating + Cooling) in a single portable bottle • Based on thermoelectric (Peltier) effect • Manual DPDT switch-based control (no microcontroller required) • Rechargeable battery-powered system • Integrated heat sink + cooling fan assembly • Compact, portable, and self-contained design • Eliminates dependency on: Refrigerator, Kettle • Low complexity, high reliability system • Suitable for outdoor, travel, medical, and sports use

TinyEyeNet introduces a lightweight CNN for accurate anterior segment eye disease classification Designed for high performance with low computational cost, ideal for real-world deployment Trained and validated on a custom-curated clinical eye image dataset Achieves strong diagnostic accuracy, outperforming conventional deep models Suitable for resource-constrained and portable ophthalmic screening systems Enables faster, scalable, and accessible eye disease detection

• The apparatus leverages sustainable photovoltaic energy, offering a viable alternative to traditional grid-dependent or fossil-fuel-reliant machinery in off-grid rural locations. • The design incorporates a five-tier screen configuration with diverse apertures, allowing for precise sorting and classification of harvests by dimensions and mass. • A specialised rotary transmission unit powers the oscillatory motion of the sieves, replacing the need for arduous manual labour. • Performance evaluations conducted on wheat, garbanzo beans, and mustard seeds confirmed the unit's effectiveness across various grain types and contamination levels. • Technology reached a purification rate exceeding 98%, while simultaneously lowering overhead expenses and physical strain for farmers. • Tests highlighted the machine’s structural durability and its capacity for prolonged use without thermal issues or mechanical failure.

• The study investigates both mechanical and thermal performance of hollow core slabs, focusing on compressive, flexural strength, fire resistance, and heat insulation characteristics. • Results show that hollow core slabs provide better thermal insulation, with approximately 6°C lower temperature and improved heat efficiency compared to conventional slabs. • The U-value of hollow core slab is lower, indicating better insulating performance, and overall heat efficiency is significantly higher (~80%) than normal slabs. • Although flexural strength is slightly lower (˜10% less) than conventional slabs, hollow core slabs are found to be economical, durable, and suitable for hot and humid climates.

• Dual-mode mobility integrating aerial and ground operation in a single platform • In-place transformation without forward motion, unlike conventional hybrid robots • Body-lift mechanism enabling smooth and controlled mode switching • Linear actuator-based transformation avoiding wheel scraping and surface friction • Reduced mechanical wear compared to servo-driven wheel rotation systems • Improved terrain adaptability by maintaining natural wheel orientation during transition • Scalable actuator design with potential for speed optimization and performance enhancement

• Spider robot design enables stable movement on sand and rough desert terrain. • Equipped with sensors to identify and locate water sources accurately. • Allows operators to monitor the robot’s status and control its operations remotely via a secure wireless connection. • Integrates with cloud computing platforms for real-time data transmission, analysis, and visualization.

• FarmGuard AI introduces a deep learning–based system for early plant disease detection using leaf images. • Uses CNN models for accurate and real-time crop health analysis. • Scalable and efficient, ensuring high performance with low computational cost. • Trained on diverse plant datasets for reliable and robust predictions. • Provides actionable insights, including disease identification and preventive measures. • Integrates weather-based alerts to improve decision-making. • Reduces pesticide usage through precise and timely intervention. • Promotes sustainable and precision agriculture practices.

• Introduces the RAIE Transformer, a lightweight, recency-augmented architecture designed specifically for accurate cricket ball trajectory prediction using short input sequences. • Uses a curated dataset of 4,400 frames from 212 cricket deliveries, manually annotated and preprocessed for reliable spatio-temporal modelling. • Incorporates a novel Recency-Augmented Input Embedding (RAIE) that prioritizes recent motion cues while preserving long-term context to improve prediction accuracy. • Outperforms traditional models such as LSTM, RNN, Base Transformer, Informer, TimeMixer, and Graph Transformer—achieving MSE 3.95, ADE 2.35, and FDE 2.56, the best among all tested models. • Provides a fully automated end-to-end pipeline combining YOLO-based detection, interpolation, and autoregressive prediction—eliminating manual annotation requirements. • Demonstrates strong real-time potential, handling non-linear motion (swing, spin, bounce) with high efficiency and low computational cost, suitable for sports analytics and broadcast applications

• Employs a custom puppeteer-core web scraper equipped with a unique "Fallback Selector Algorithm," successfully bypassing modern UI obfuscation and anti-bot measures with a 98% extraction reliability rate. • Utilizes a Long Short-Term Memory (LSTM) neural network via TensorFlow.js, engineered with a 20% recurrent dropout to ignore daily market noise and predict post-sale price recoveries with a robust 60.1% directional accuracy. • Overcomes the strict memory constraints of Firebase Cloud Functions by deploying a "Singleton Pre-Trained" AI architecture, effectively reducing AI inference latency from over two seconds to under 20 milliseconds. • Solves database redundancy by implementing a "Shared Product Architecture" within MongoDB Atlas, allowing thousands of users to track the same item using a single, centralized data node. • Delivers genuine financial advantages by autonomously dispatching targeted email alerts via Nodemailer strictly when a product's live price drops to the user's specific target.

• Proposes an AI-assisted otoscope framework for automated ear disease diagnosis • Enables early and accurate detection of common ear conditions from otoscopic images • Utilizes a deep learning–based image analysis pipeline for robust classification • Reduces dependency on specialist expertise, supporting primary healthcare screening • Designed for real-time and portable diagnostic applications • Demonstrates high classification accuracy on clinically relevant datasets • Enhances accessible, scalable, and cost-effective ear healthcare solutions.

• Predictive AI using a Temporal Fusion Transformer (TFT) model for Remaining Useful Life (RUL) calculation. • Multi-modal sensor array measuring temperature, humidity, vibration (shear forces), and ammonia gas. • Custom private LoRaWAN network infrastructure for secure, long-range communication. • "Store-and-Forward" data logic to prevent data loss during network dead zones. • Hybrid power management system with an internal battery backup. • Multi-level deployment architecture featuring portable end-user units and industrial bulk shipping containers. • Intelligent, context-aware alerting logic designed to prevent "alarm fatigue".