Hani M. Ibrahim

The increasing global discovery of plant species presents both opportunities and challenges, particularly in distinguishing between beneficial and poisonous varieties. While computer vision techniques show promise for classifying plant species and predicting toxicity, the lack of comprehensive datasets including images, scientific names, descriptions, local names, and poisonous status complicates these predictions. In this paper, we propose an Explainable Deep Inherent Learning approach that leverages advanced computer vision techniques for effective plant species classification and poisonous status prediction. The proposed Deep Inherent Learning method was validated using different explanation techniques, and Explainable AI (XAI) was employed to clarify decision-making processes at both the local and global levels. Additionally, we provide visual information to enhance trust in the proposed method. To validate the efficacy of our approach, we present a case study involving 2500 images of 50 different plant species from the Arabian Peninsula, enriched with essential metadata. This research aims to reduce the incidence of poisoning from harmful plants, thereby benefiting individuals and society. Our experimental results demonstrate strong performance, with the XAI model achieving accuracy, Precision, Recall, and F1-Score of 0.94, 0.96, 0.96 and 0.97, respectively. By enhancing interpretability, our study fosters greater trust in AI-driven plant classification systems.

Myeloproliferative neoplasms, particularly the Philadelphia chromosome-negative (Ph-negative) subtypes such as essential thrombocythemia, polycythemia vera, and primary myelofibrosis, present diagnostic challenges due to overlapping morphological features and clinical heterogeneity. Traditional diagnostic approaches, including imaging and histopathological analysis, are often limited by interobserver variability, delayed diagnosis, and subjective interpretations. To address these limitations, we propose a novel framework that integrates handcrafted and automatic feature extraction techniques for improved classification of Ph-negative myeloproliferative neoplasms. Handcrafted features capture interpretable morphological and textural characteristics. In contrast, automatic features utilize deep learning models to identify complex patterns in histopathological images. The extracted features were used to train machine learning models, with hyperparameter optimization performed using Optuna. Our framework achieved high performance across multiple metrics, including precision, recall, F1 score, accuracy, specificity, and weighted average. The concatenated probabilities, which combine both feature types, demonstrated the highest mean weighted average of 0.9969, surpassing the individual performances of handcrafted (0.9765) and embedded features (0.9686). Statistical analysis confirmed the robustness and reliability of the results. However, challenges remain in assuming normal distributions for certain feature types. This study highlights the potential of combining domain-specific knowledge with data-driven approaches to enhance diagnostic accuracy and support clinical decision-making.

Crowd behavior presents significant challenges due to intricate interactions. This research proposes an approach that combines the power of 3D Convolutional Neural Networks (ConvNet) and Multi-Support Vector Machines (Multi-SVM) to study and analyze crowd behavior in highly dense crowd videos. The proposed approach effectively utilizes the temporal information captured by the 3D ConvNet, which accounts for the spatiotemporal characteristics of crowd movement. By incorporating the third dimension as a temporal stack of images forming a clip, the network can learn and comprehend the dynamics and patterns of crowd behavior over time. In addition, the learned features from the 3D ConvNet are classified and interpreted using Multi-SVM, enabling a comprehensive analysis of crowd behavior. This methodology facilitates the identification and categorization of various crowd dynamics, including merging, diverging, and dense flows. To evaluate the effectiveness of the approach, experiments are conducted on the Crowd-11 dataset, which comprises over 6000 video sequences with an average length of 100 frames per sequence. The dataset defines a total of 11 crowd motion patterns. The experimental results demonstrate promising recognition rates and achieve an accuracy of 89.8%. These findings provide valuable insights into the complex dynamics of crowd behavior, with potential applications in crowd management.

Automatic key gesture detection and recognition are difficult tasks in Human–Computer Interaction due to the need to spot the start and the end points of the gesture of interest. By integrating Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs), the present research provides an autonomous technique that carries out hand gesture spotting and prediction simultaneously with no time delay. An HMM can be used to extract features, spot the meaning of gestures using a forward spotting mechanism with varying sliding window sizes, and then employ Deep Neural Networks to perform the recognition process. Therefore, a stochastic strategy for creating a non-gesture model using HMMs with no training data is suggested to accurately spot meaningful number gestures (0–9). The non-gesture model provides a confidence measure, which is utilized as an adaptive threshold to determine where meaningful gestures begin and stop in the input video stream. Furthermore, DNNs are extremely efficient and perform exceptionally well when it comes to real-time object detection. According to experimental results, the proposed method can successfully spot and predict significant motions with a reliability of 94.70%.

In the area of network analysis, centrality metrics play an important role in defining the “most important” actors in a social network. However, nowadays, most types of networks are dynamic, meaning their topology changes over time. The connection weights and the strengths of social links between nodes are an important concept in a social network. The new centrality measures are proposed for weighted networks, which relies on a time-ordered weighted graph model, generalized temporal degree and closeness centrality. Furthermore, two measures—Temporal Degree-Degree and Temporal Closeness-Closeness—are employed to better understand the significance of nodes in weighted dynamic networks. Our study is caried out according to real dynamic weighted networks dataset of a university-based karate club. Through extensive experiments and discussions of the proposed metrics, our analysis proves that there is an effectiveness on the impact of each node throughout social networks.