Mohammad Alkhatib

Sana Kordoghli, Abdelhakim Settar, Oumayma Belaati et al.

This work contributes to advancing sustainable energy and waste management strategies by investigating the thermochemical conversion of food-based biomass through pyrolysis, highlighting the role of artificial intelligence (AI) in enhancing process modelling accuracy and optimization efficiency. The main objective is to explore the potential of underutilized biomass resources, such as spent coffee grounds (SCG) and date seeds (DS), for sustainable hydrogen production. Specifically, it aims to optimize the pyrolysis process while evaluating the performance of these resources both individually and as blends. Proximate, ultimate, fibre, TGA/DTG, kinetic, thermodynamic, and Py-Micro GC analyses were conducted for pure DS, SCG, and blends (75% DS - 25% SCG, 50% DS - 50% SCG, 25% DS - 75% SCG). Blend 3 offered superior hydrogen yield potential but had the highest activation energy (Ea: 313.24 kJ/mol), while Blend 1 exhibited the best activation energy value (Ea: 161.75 kJ/mol). The kinetic modelling based on isoconversional methods (KAS, FWO, Friedman) identified KAS as the most accurate. These approaches provide a detailed understanding of the pyrolysis process, with particular emphasis on the integration of artificial intelligence. An LSTM model trained with lignocellulosic data predicted TGA curves with exceptional accuracy (R^2: 0.9996-0.9998).

Mohammad Alkhatib, Adel Hafiane

Texture is an important cue for different computer vision tasks and applications. Local Binary Pattern (LBP) is considered one of the best yet efficient texture descriptors. However, LBP has some notable limitations, mostly the sensitivity to noise. In this paper, we address these criteria by introducing a novel texture descriptor, Robust Adaptive Median Binary Pattern (RAMBP). RAMBP based on classification process of noisy pixels, adaptive analysis window, scale analysis and image regions median comparison. The proposed method handles images with high noisy textures, and increases the discriminative properties by capturing microstructure and macrostructure texture information. The proposed method has been evaluated on popular texture datasets for classification and retrieval tasks, and under different high noise conditions. Without any train or prior knowledge of noise type, RAMBP achieved the best classification compared to state-of-the-art techniques. It scored more than $90\%$ under $50\%$ impulse noise densities, more than $95\%$ under Gaussian noised textures with standard deviation $σ= 5$, and more than $99\%$ under Gaussian blurred textures with standard deviation $σ= 1.25$. The proposed method yielded competitive results and high performance as one of the best descriptors in noise-free texture classification. Furthermore, RAMBP showed also high performance for the problem of noisy texture retrieval providing high scores of recall and precision measures for textures with high levels of noise.