In the evolving landscape of medical imaging, this study introduces a deep learning-based approach for brain tumour detection and classification. In this study, a U-Net architecture was developed for tumour detection and segmentation while an EfficientNet-based model was used for classification. Dataset consisting of MRI scans which has complex brain tumour pattern types was used to train the model. The performance of the developed model was evaluated using Dice coefficient, IoU score, sensitivity, and specificity for detection, and accuracy, precision, recall, and F1-score for classification, which demonstrates the system's effectiveness. The detection model achieves a Dice coefficient of 0.9321 and an IoU score of 0.8729, while the classification model attains an overall accuracy of 0.965, which surpasses the benchmark methods. Additionally, a user-friendly web interface was developed to enhance the system's practicality for clinical use. The results obtained show that the developed interface enables real-time tumour analysis. The proposed system not only improves the accuracy and efficiency of brain tumour analysis but also provides a seamless tool for medical professionals, which will enhance diagnostic workflows and patient outcomes.

Laser micromachining has become an essential tool in precision manufacturing due to its non-contact nature, high spatial resolution, and capability to produce intricate micro-features. However, identifying the optimal combination of process parameters remains challenging because of the nonlinear and interdependent effects of laser power, scanning speed, and pulse frequency on cut quality. In this study, a comparative framework is presented that benchmarks the Taguchi Design of Experiments (DoE) against a Deep Neural Network (DNN) model to predict and optimize the micromachining performance of stainless steel. A unified Cut Quality Index (CQI) was developed by combining three critical responses kerf width, heat-affected zone (HAZ), and edge chipping into a single measure of overall cut integrity. A physics-consistent dataset of 75 samples, comprising 20 literature-based and 55 synthetically generated data points, was constructed to ensure both experimental realism and statistical diversity. The Taguchi analysis using an L18 orthogonal array identified the optimal parameters as 80 W laser power, 250 mm/s scanning speed, and 60 kHz pulse frequency, corresponding to the highest signal-to-noise ratio and thermally balanced operation. The DNN model achieved strong predictive accuracy (R² ≈ 0.92–0.94), effectively capturing nonlinear parameter interactions without overfitting. The results demonstrate that while the Taguchi method efficiently identifies robust process windows with minimal experimentation, the DNN extends predictive capability across continuous, untested regions of the process space. Collectively, these findings establish a physics-informed, data-driven comparative framework for intelligent optimization of laser micromachining, with direct relevance to aerospace, biomedical, and precision micro-engineering applications.   

Micro-Electro-Mechanical Systems (MEMS) have fundamentally transformed technology by combining microelectronics with mechanical systems to create miniature devices capable of diverse functionalities. This review article provides a thorough exploration of MEMS, tracing its evolution from early developments to the latest advancements. It begins by outlining the fundamental principles behind MEMS design and fabrication, detailing processes such as lithography, deposition, and etching. The paper covers a wide array of MEMS devices, including sensors, actuators, resonators, and microfluidic systems, while focusing on essential design considerations, fabrication techniques, and performance parameters. The versatility of MEMS across sectors like healthcare, automotive, aerospace, consumer electronics, and telecommunications is highlighted, illustrating their role in advancing applications such as medical diagnostics, environmental sensing, and autonomous technologies. Unlike previous reviews, this paper provides a unique synthesis linking fabrication mechanisms with device performance metrics, offering an updated comparative analysis across MEMS subcategories (RF MEMS, microfluidics, and optical MEMS). It also integrates the latest market data (2024–2025) and contextualizes how MEMS devices underpin IoT and Industry 5.0 applications. Furthermore, it emphasizes emerging research directions such as energy harvesting MEMS, bio-inspired microsystems, and security-aware MEMS integration in connected environments. These additions make this review both comprehensive and forward-looking, serving as a reference for researchers and practitioners.

Agriculture has continued being one of the economic powerhouses of India, but then the productivity is usually compromised due to the poor utilization of soil and environment data. This paper is a proposal of a new framework named Distribution and Resource Aware Random Forest (DRARF) to be used in smart farming applications. The strategy combines IoT-ready soil data that comprises of moisture, temperatures, humidity, pH, and NPK that are monitored via different sources and used to make crop-specific decisions. The DRARF presents two important novel features to traditional Random Forests: (i) distribution-aware threshold selection, which guarantees statistical meaningful data partition and (ii) resource-aware feature selection, which gives more predictive power without the expense of buying sensors in the IoT. It assessed the framework using soil and environmental data of wheat and rice. The comparative tasks performed using Logistic Regression, Support Vector Machine, Naïve Bayes, and the classical random Forest have shown that DRARF not only provides a better accuracy, precision, recall and F1-scores, but it also minimizes sensor redundancy. Its potential depends on its scalability, efficiency, and reliability as a precision agricultural decision-support system and this, as well as the remaining results, are reflected in the results. The given approach with machine learning and IoT-facilitated sensing source-based solutions can bring the advancements in the sphere of smart farming technologies to help increase the yield of crops and resources and improve long-term food security.

Menstrual discomfort significantly affects women’s productivity and engagement in structured environments such as offices, schools, colleges, and conferences. However, current access control systems fail to address this issue, resulting to inadequate accommodation. To bridge this gap, we propose an AI-driven menstrual detection system that uses closed-circuit (CC) cameras for facial recognition. Leveraging deep learning, our model analyzes facial cues—such as skin texture changes, eye fatigue, and puffiness to detect discomfort non-invasively while preserving privacy. To support this research, we introduce the Menstrual Presence Facial Dataset (MAFD-2024), a curated collection of facial images captured before and during menstruation, annotated for pain indicators. Our hybrid CNN-LSTM model (HCL-MD) enhances detection accuracy by 94.1%, combining spatial (facial features) and temporal (symptom progression) analysis. This system integrates with automated access frameworks, enabling real-time adjustments for affected individuals. Beyond access control, technology can be embedded in telemedicine for remote discomfort assessment or deployed in smart wearables and surveillance systems (e.g., in schools or public transport) to offer timely suggestions. By enabling discreet, real-time support, this AI solution fosters inclusivity and awareness, pioneering the fusion of facial recognition and menstrual health monitoring.