The swift advancement of electric vehicle (EV) technology enhances the focus on sustainable energy storage and underscores the crucial significance of lithium-ion batteries. This research primarily presents the techniques of forecasting the Remaining Useful Life (RUL) of this battery using advanced Machine Learning (ML) methods such as Random Forest (RF) and Support Vector Machine (SVM). This research centres around the thorough preprocessing of a detailed dataset received from the NASA Ames Prognostics Center of Excellence. The One-way ANOVA method is employed to find the optimum set of features. The exhaustive hyperparameter-tuning (HPT) was performed to boost the performance of the ML models. An important component of this study is its pragmatic methodology, which considered real-time variables such as temperature changes and usage cycles to analyse the effect on battery capacity (cap). The proposed system helped to understand the behaviours of battery deterioration trends more comprehensively. The effectiveness of the system is decided based on the R2 score and Mean Squared Error (MSE). The RF model has shown R2 score of 0.83 and MSE of 1.67. The result enhances lithium-ion battery safety and efficiency by establishing new predictive models. Thus, it provides a better battery management system for electric vehicles. As a result, it promotes the development of more sustainable and economical energy solutions.

In this quickly developing world, automatic currency identification and recognition are crucial tasks. Several financial institutions, such as banks and hardware-based devices such as vending machines and slot machines, play an essential role in all monetary unification fields. Accurate coin recognition is essential in various contexts, including vending machines, currency exchange, and archaeological research. However, the distinctive visual characteristics of Brazilian coins, including variations in size, color, and design, pose significant challenges for automated classification. Most of the existing currency recognition systems are based on the physical properties of the currencies, such as length, breadth, and mass. At the same time, image-based methods rely on other properties like color, shape, and edge. This paper presents a novel deep-learning framework tailored to classify Brazilian coins. Our proposed deep learning framework leverages state-of-the-art convolutional neural networks (CNNs) to address these challenges. We introduce a Repetitive Feature Extractor Convolution Neural Network (RFE-CNN) model to recognize the currency faster and accurately. Our framework employs a multi-stage approach for coin classification. First, a pre-processing module handles coin localization and image enhancement to mitigate variations in lighting and background. Next, an RFE-CNN-based feature extractor extracts discriminative features from the coin images. We explore transfer learning from pre-trained models to enhance the model’s generalization capability, given limited data availability. We used a comprehensive dataset of Brazilian coins, comprising various denominations, minting years, and conditions, to facilitate model training and evaluation. The dataset includes high-resolution images captured under diverse lighting and environmental conditions, ensuring robust model performance in real-world scenarios. In conclusion, our proposed deep learning framework offers a powerful and efficient solution for classifying Brazilian coins. The framework’s adaptability makes it a valuable tool for recognizing coins from other regions with similar visual diversity and variability challenges. The proposed model has achieved a classification accuracy of 98.34% for the classification of Brazilian coins.

The new 3-D proposed structure has a stop band at 2.45 GHz. This stop band is the operating frequency for common microwave heating devices. The structure is periodic. The infrared emissions are passed through a large enough opening inside the microwave cavity in order to allow the temperature of the object inside to be read by the thermopile installed outside the chamber. Using the FE Eigen analysis it was possible to design the unit cell of the structure in order to locate the stop bands. After locating the stop bands the structure was analyzed deterministically using an FE solver, then three unit cells was fabricated. Through the three unit cells fabrication and measurements satisfactory results have been achieved when the proposed structure was tested in a commercial microwave acid digestion system.

Timely identification of seizure-related EEG states can support clinical assessment and motivate future monitoring tools. This study investigates a comparative machine-learning framework for EEG-based epileptic seizure state classification using sub-band analysis of the Bonn University EEG dataset. The signals are decomposed into delta, theta, alpha, beta, and gamma bands with bandpass filtering, and a set of lightweight statistical descriptors is extracted from each band. Support Vector Machines, Random Forest, XGBoost, CatBoost, Multi-Layer Perceptron, k-Nearest Neighbours, and Logistic Regression are then evaluated using multi-class ROC analysis, confusion matrices, precision, recall, F1-score, and accuracy. The results show that tree-based models, particularly XGBoost, CatBoost, and Random Forest, provide the strongest overall performance on this benchmark. The manuscript is now framed as a three-state EEG classification study rather than prospective seizure forecasting, and the discussion highlights the limited size of the Bonn dataset, the need for validation on larger datasets such as CHB-MIT and Siena Scalp EEG, and the requirements for future real-time deployment.

Economic diversification across the Gulf region, including Kuwait Vision 2035, positions smart manufacturing as a key enabler of sustainable growth. Yet, industrial datasets in the region are typically small, heterogeneous, and incomplete, limiting the performance and trustworthiness of conventional AI models. This paper introduces a Scalable Random Forest (SRF) framework enhanced with a Decision Path Search (DPS) mechanism to address these challenges through both technical robustness and practical interpretability. The SRF pipeline incorporates leakage-safe preprocessing, mixed-type imputation, and small-data augmentation to improve prediction stability under real industrial constraints, while DPS transforms model internals into actionable operational causal knowledge identifying optimal and avoidance parameter ranges. Case studies, including investment casting, demonstrate that SRF + DPS not only outperforms established baselines such as Random Forest (RF), XGBoost, LightGBM, and CatBoost but also deliver transparent insights that engineers can directly apply to reduce defects and enhance process control. The findings highlight how interpretable AI frameworks can accelerate industrial modernization, strengthen regional manufacturing competitiveness, and support national economic diversification strategies.