احمد ناصر حسين الزرفي

The paper addresses the urgent problem of reconciling environmental sustainability with operational efficiency in current logistics by presenting a new AI-based framework that directly considers the complexities of consumer behavior among Gen Z/Millennial Chinese consumers. Since environmental concern increases the demand for green logistics, classical optimization models do not tend to reflect the peculiarities of consumer preferences and the new confidence in AI-based solutions. This methodology is distinguished by a perfect combination of Structural Equation Modelling (SEM) to empirically derive the findings of consumer behavior, and a Genetic Algorithm (GA)-based optimization model designed to meet the needs of logistics. This analysis is based on the primary consumer data collected by WeChat from students and young professionals in Chinese universities. The integrated model also takes into account real-life constraints like vehicle capacity, demand variability, and time windows. The SEM analysis indicated that the perceived utility (β =0.64, p < 0.01) and environmental awareness (β =0.76, p < 0.01) have the greatest influence on the adoption of green transportation, with younger customers (ages 18–24, 88.34%) being more supportive of it. However, building trust in AI-driven logistics is still a challenge (β =0.39, p < 0.05). These behavioral observations (as developed using SEM) are incorporated systematically as explicit constraints and elements of the objective function (Behavioral Penalty term) in the GA-based optimization framework. This model reduces emissions and consumer-preference penalties while achieving a small reduction in total generalized cost, although pure operational cost increases. Our results reveal the disruptive potential of AI in terms of efficiency and sustainability in supply chains and support policy incentives, and further research on advanced behavioral analytics that can contribute to consumer trust and facilitate the widespread adoption of these technologies.

Remote sensing data of medium resolution are commonly used to classify land cover, and machine learning (ML) models have taken on a central aspect in the necessary data analysis. Ordinarily, land cover is coded on a pixel basis on the basis of Digital Number (DN) values, which in turn are computed across several spectral bands. This paper is concerned with land cover mapping in Mosul, Iraq, based on satellite images captured by Sentinel-2. Two platforms featuring unsupervised classification algorithms were used, Google Earth Engine and ArcMap, making it possible to use K-means and X-means in Google Earth Engine and ISO Cluster in ArcMap. The comparative analysis of these algorithms allowed development of four significant land cover classes: built-up areas, vegetation, water bodies, and bare land. The results of the classification for the Mosul study area also indicated that using X-means gave an accuracy of 76.34, while using ISO Cluster gave 73.61, and K-means gave 80.14. The highest classification accuracy was achieved by the use of K-means, suggesting that it is the best algorithm to use in this region.

The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors’ prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

Glioma grading is vital for therapeutic planning where the higher level of glioma is associated with high mortality. It is a challenging task as different glioma grades have mixed morphological characteristics of brain tumour. A computeraided diagnosis (CAD) system based on three-dimensional textural grey level co-occurrence matrix (GLCM) and machine learning is proposed for glioma grading. The purpose of this paper is to assess the usefulness of the 3D textural analysis in establishing a malignancy prediction model for glioma grades. Furthermore, this paper aims to find the best classification model based on textural analysis for glioma grading. The classification system was evaluated using leave-one-out cross-validation technique. The experimental design includes feature extraction, feature selection, and finally the classification that includes single and ensemble classification models in a comparative study. Experimental results illustrate that single and ensemble classification models, can achieve efficient prediction performance based on 3D textural analysis and the classification accuracy result has significantly improved after using feature selection methods. In this paper, we compare the proficiency of applying different angles of 3D textural analysis and different classification models to determine the malignant level of glioma. The obtained sensitivity, accuracy and specificity are 100%, 96.6%, 90% respectively. The prediction system presents an effective approach to assess the malignancy level of glioma with a non-invasive, reproducible and accurate CAD system for glioma grading.

The preoperative diagnosis of brain Glioma grades is crucial for therapeutic planning as it impacts on the tumour's prognosis. The development of machine learning methods that can accurately evaluate Glioma grades is of great interest since it is a repeatable and reliable diagnosis procedure. Moreover, the classification accuracy of a single classifier can be further improved by using the ensemble of different classifiers. In this paper, a new strategy has been developed, which uses a deep neural network incorporating an extensive iteration matrix based on the combination of eleven different machine learning algorithms. The classification system is evaluated using a cross-validation technique, to add more generalization to the results of the classification system's reliability in unseen cases. Experimental results indicate that, when compared to both the single classification model, and the majority vote scheme, the grading accuracy has significantly improved using our proposed approach. The obtained sensitivity, specificity and accuracy are 100%, 90% and 93.3% respectively. The proposed approach has improved upon the highest accuracy of the single classification model by 13.3%. The proposed classification system presents an efficient method to evaluate the malignancy level of Glioma with more reliable and accurate clinical outcomes.

Abstract: Image identification of plant leaves based on human vision is difficult task as well as plant identification based on keywords retrieval. It requires the domain knowledge in the botanist field. This work proposes the image texture analysis using Discrete Wavelet Transformation (DWT) and combined with an entropy measurement to identify a query image to one of seven classes that consists of 280 plant leaves images. The experimental results show that the proposed method yields higher correctness retrieval accuracy rate which reaches up to 92% compared to the Gray Level Co-occurrence Matrix (GLCM) that gives 49.28%.