B472515021225

The energy optimisation of buildings relies heavily on an accurate understanding of the thermophysical properties of building materials. However, in many developing countries the characterization of these materials remains limited by the high cost and scarcity of measuring instruments. In this perspective, we first designed, realized and experimentally validated a device for the simultaneous measurement of thermal conductivity and diffusivity of building materials. With these results, the present study introduces the design and simulation of an autonomous electronic thermal data-acquisition device intended to improve the accuracy and autonomy of the initial system. The proposed device is based on an Arduino Due and integrates a temperature sensor (MCP9600), a real-time clock (DS3231), a 20×4 LCD, and an SD Card storage module. The simulation, performed in Proteus, validated the functional consistency of the circuit and the communication between the different modules via the I2C and SPI interfaces. The results demonstrate stable acquisition and reliable real-time temperature recording. This work aligns with efforts to develop affordable, robust, and reproducible measurement instruments tailored to the specific needs of African energy and materials science research laboratories.

A469215011025

The rapid expansion of complex system logs in modern infrastructures has heightened the need for accurate, interpretable, and low-latency risk analysis. These logs contain high-dimensional, context-rich data that is essential for operational reliability, cybersecurity, and compliance. While conventional machine learning models are efficient, they often overlook the nuanced semantic relationships in sequential log data, limiting predictive reliability. Conversely, large language models (LLMs) offer deeper contextual understanding but are computationally intensive, making them unsuitable for real-time, large-scale deployment. This study presents a deployment optimised pipeline that balances semantic depth with computational efficiency for log-based risk prediction. The architecture integrates lightweight MiniLM embeddings with an XGBoost classifier to produce interpretable, high-quality predictions at reduced computational cost. Key optimizations include class balancing to address dataset skew, model quantization to lower memory usage, and batched inference to increase throughput, enabling cost-effective CPU-only execution without GPUs. A structured evaluation examined accuracy, latency, and memory trade-offs across production scenarios. Testing on representative log datasets showed notable gains over a TF-IDF baseline: classification accuracy improved from 21.4% to 57.1%, weighted F1-scores rose accordingly, and inference latency decreased with negligible loss in predictive strength. By combining transformer-based dense embeddings with gradient-boosted decision trees, this approach delivers a practical balance of semantic expressiveness, interpretability, and deployment efficiency. The framework supports scalable, real-time risk prediction for cybersecurity monitoring, compliance auditing, and IT operations, bridging the gap between advanced language modelling and real-world infrastructure constraints.

F469114060825

Preserving Indian folk music in digital repositories poses significant challenges because robust classification systems are lacking to capture its linguistic, instrumental, and acoustic diversity. As a cornerstone of India’s intangible cultural heritage, this music faces the risk of marginalisation and loss unless systematic, scalable methods are employed to identify and preserve it. This research aims to develop an automated, multi-modal framework for regional classification of Indian folk music, thereby enabling structured archiving and improved accessibility. To achieve this, a novel machine learning pipeline was designed, integrating Whisper for speech recognition and regional language identification . Instrument detection was performed using YAMNet, which has proven effective in recognizing traditional instruments . Acoustic features such as MFCCs, chroma, and spectral descriptors were extracted using Librosa [Error! R eference source not found.]. Together, these tools provide a comprehensive understanding of the songs’ linguistic, instrumental, and rhythmic content. The curated dataset includes folk music from linguistically rich regions of India, such as Marathi, Punjabi, Urdu Qawwali, and dialects from Uttar Pradesh and Bihar. Seven supervised learning algorithms were trained and evaluated, including Random Forest, Support Vector Machine, and Gradient Boosting. Simpler classifiers, such as K-Nearest Neighbours, Naive Bayes, and Logistic Regression, were also tested. A hybrid ensemble model combining Random Forest, SVM, and Gradient Boosting through soft voting achieved a classification accuracy of 99%. This result demonstrates the effectiveness of ensemble learning, combined with multimodal features, in handling nuanced differences in regional folk genres. This research addresses the critical gap in scalable and automated tools for preserving folk music. The study highlights the potential of artificial intelligence in safeguarding endangered cultural assets

F468414060825

Poultry and food processing manufacturing units have several automated and monitoring processes of the food that go into packaging. However, at the packaging section, manual human intervention is needed to ensure that the correct amount of food, in terms of count and weight, is placed into every package. This is not without error. Hence, an accurate real-time measurement of sample object counts and weight is critical for optimizing processing efficiency and automating workflows in the production chain. The system identifies the food object, counts it, and weighs it before packaging the batch. In this work, we present a novel approach that integrates an Ultralytics You Only Look Once (YOLO) v10 model, a convolutional neural network (CNN)-based object detection framework, with an automated weighing system and dashboard to optimize quality control.

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