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Byblos, designated as a UNESCO World Heritage site, stands as one of Lebanon's most ancient urban centers, known for its expansive green spaces. However, ongoing urbanization threatens these valuable areas. This study uses a multi-faceted approach to evaluate the structure and landscape attributes of Byblos' urban and peri-urban forests (UPFs). Landscape canopy cover, diversity indices, forest structure, and a silhouette perceptual test were assessed across 24 streets in the city center, residential zones, and areas with heavy vehicular traffic. Findings reveal that 28% of Byblos' canopy cover is concentrated mostly in the northeastern region. Native tree species account for 30% of the total, and a notable variation in tree diversity exists among different land-use types (Shannon diversity index (H) was 1.02 for the city center, 1.35 for residential streets, and 0.64 for vehicular areas). Additionally, a normal J-shaped distribution of tree diameters was identified across all street types. This study highlights a correlation between tree silhouettes and visual preferences, with densely spreading canopies being favored. Residential trees demonstrate the highest structural diversity and varied blossoming seasons. This research represents the first investigation into the current state of urban forestry in Byblos and offers recommendations for sustainable management and planning strategies. [ABSTRACT FROM AUTHOR]

Warts are benign tumors infected by the Human Papillomavirus. Physicians and medical practitioners are endeavoring to identify the best wart treatment method. The present study finds the response of well-known wart treatment methods, namely immunotherapy and cryotherapy, towards the removal of predominant wart types such as plantar and common warts. The present study utilized the optimal feature space generated by the measure of Fisher score, information gain, and univariate statistical test. In addition, the proposed method finds the customized cost in terms of class weighted and non-class weighted to reduce the miss-classified instances for the positive class sample. The class-weighted and non-class-weighted approaches explicitly incorporated with the well-known classification algorithm extreme gradient boosting approach, which provides a maximum measure of true positive rate, true negative rate, positive predicted value, F-measure, and area under receiver operating characteristic curve of 100.00, 100.00, 100.00, 80.00, and 82.00% respectively on immunotherapy dataset, 100.00, 100.00, 100.00, 100.00, 92.00% respectively on cryotherapy dataset. While validating the performance on the benchmark dataset with the state-of-the-art approach, the proposed model gives an improvement of 6.40% to a maximum of 43.00% in terms of specificity on the immunotherapy dataset. However, the proposed model improves 3.33 - 30%, 5.70 -30%, and 0 - 31% in terms of accuracy, sensitivity, and specificity, respectively on cryotherapy dataset. Also, the proposed framework achieved a maximum sensitivity of 91.30%, which dominates the existing state-of-the-art approaches by a margin of 1.87% and 10.82%, respectively on the merged dataset. [ABSTRACT FROM AUTHOR]

A homogeneous distribution of students in a class is accepted as a key factor for overall success in primary education. A class of students with similar attributes normally increases academic success. It is also a fact that general academic success might be lower in some classes where students have different intelligence and academic levels. In this study, a class distribution model is proposed by using some data science algorithms over a small number of students' dataset. With unsupervised and semi-supervised learning methods in machine learning and data mining, a group of students is equally distributed to classes, taking into account some criteria. This model divides a group of students into clusters by the considering students' different qualitative and quantitative characteristics. A draft study is carried out by predicting the effectiveness and efficiency of the presented approaches. In addition, some process elements such as quantitative and qualitative characteristics of a student, data acquisition style, digitalization of attributes, and creating a future prediction are also included in this study. Satisfactory and promising experimental results are received using a set of algorithms over collected datasets for classroom scenarios. As expected, a clear and concrete evaluation between balanced and unbalanced class distributions cannot be performed since these two scenarios for the class distributions cannot be applicable at the same time. [ABSTRACT FROM AUTHOR]

Plant diseases may cause large yield losses, endangering both the stability of the economy and the supply of food. Convolutional Neural Networks (CNNs), in particular, are deep neural networks that have shown remarkable effectiveness in completing image categorization tasks, often outperforming human ability. It has numerous applications in voice processing, picture and video processing, and natural language processing (NLP). It has also grown into a centre for research on plant protection in agriculture, including the assessment of pest ranges and the diagnosis of plant diseases. In two plant phenotyping tasks, the function of a CNN (Convolutional Neural Networks) structure based on Residual Networks (ResNet) is investigated in this study. The majority of current studies on Species Recognition (SR) and plant infection detection have used balanced datasets for accuracy and experimentation as the evaluation criteria. This study, however, made use of an unbalanced dataset with an uneven number of pictures, organised the data into several test cases and classes, conducted data augmentation to improve accuracy, and-- most importantly--used multiclass classifier assessment settings that were helpful for an asymmetric class distribution. Furthermore with all these frequent issues, the paper addresses selecting the size of the data collection, classifier depth, necessary training time, and assessing the efficacy of the classifier when using various test scenarios. The Species Recognising (SR) and Identifying of Health and Infection Leaves (IHIL) tasks in this study have shown substantial improvement in performance for the ResNet 20 (V2) architecture, with Precision of 91.84% & 84.00%, Recall of 91.67% and 83.14%, and F1 scores of 91.49% & 83.19%, respectively. [ABSTRACT FROM AUTHOR]

Byblos, designated as a UNESCO World Heritage site, stands as one of Lebanon’s most ancient urban centers, known for its expansive green spaces. However, ongoing urbanization threatens these valuable areas. This study uses a multi-faceted approach to evaluate the structure and landscape attributes of Byblos’ urban and peri-urban forests (UPFs). Landscape canopy cover, diversity indices, forest structure, and a silhouette perceptual test were assessed across 24 streets in the city center, residential zones, and areas with heavy vehicular traffic. Findings reveal that 28% of Byblos’ canopy cover is concentrated mostly in the northeastern region. Native tree species account for 30% of the total, and a notable variation in tree diversity exists among different land-use types (Shannon diversity index (H) was 1.02 for the city center, 1.35 for residential streets, and 0.64 for vehicular areas). Additionally, a normal J-shaped distribution of tree diameters was identified across all street types. This study highlights a correlation between tree silhouettes and visual preferences, with densely spreading canopies being favored. Residential trees demonstrate the highest structural diversity and varied blossoming seasons. This research represents the first investigation into the current state of urban forestry in Byblos and offers recommendations for sustainable management and planning strategies.

Data selection is commonly used to reduce costs and energy usage by training on a subset of available data. However, determining the appropriate subset size requires extensive dataset knowledge and experimentation, limiting transferability. Varying the validation set also produces unstable results and wastes computational resources. In this paper, we propose a data selection method for dynamically determining subset ratios based on model performance using only a training set. The data search space is narrowed through weighted sampling, leveraging statistical selection patterns. Parallel analysis of class distributions identifies the most representative samples with high selection potential. Extensive experiments validate our approach and demonstrate improved training efficiency. Our method speeds up various subset ratios by up to 2.2x on CIFAR-10, 1.9x on CIFAR-100, 2.0x on TinyImageNet, and 2.3x on ImageNet with negligible accuracy drops. [Display omitted] Main flowchart of Dyna m ic Data S u bset S election for I mage C lassification (Music) to speed up neural network training, where reinforced data selection is performed every L epochs. Music is compatible with any data selection algorithm, and model parameters are updated using selected data subsets. The block diagram shows interactions between modules in the reinforced selection component. • Propose a data selection method to dynamically determine the subset ratio. • Study the role of intelligent data subset ratios. • Orthogonalize existing subset selection methods. • Two novel modules are used to narrow the data search. • Enable more efficient and sustainable AI development. [ABSTRACT FROM AUTHOR]

Learning convolutional networks on graphs have been a popular topic for machine learning on graph-structured data and achieved state-of-the-art results on various practical tasks. However, most existing works ignore the impact of per-class distribution, therefore their performance may be limited due to the diversity of various categories. In this paper, we propose a novel class-aware progressive self-training (CPS) algorithm for training graph convolutional networks (GCNs). Compared to other self-training algorithms for GCNs' learning, the proposed CPS algorithm leverages the class distribution to update the original graph structure in each self-training loop, including: (a) find these high-confident unlabeled nodes in the graph for each category to add pseudo labels, in order to enlarge the current set of labeled nodes; (b) delete these noisy edges between different classes for graph sparsification. Then, the optimized graph is used for next self-training loops in hopes of enhancing the classification performance. We evaluate the proposed CPS on several datasets commonly used for GCNs' learning, and the experimental results show that the proposed CPS algorithm outperforms other baselines. [ABSTRACT FROM AUTHOR]