Aliaa A. A. Youssif

Accurate segmentation of histopathological images is essential to support early diagnosis and effective treatment planning in cancer care. This study presents CancerSeg-XA, a deep learning-based histopathology segmentation system designed to deliver robust performance across diverse tissue types and imaging sources. Built upon the DeepLabV3+ framework, CancerSeg-XA incorporates architectural enhancements to strengthen feature representation and improve model stability. The system was evaluated on three widely recognized datasets—BCSS, PanNuke, and PUMA—each presenting distinct structural and clinical challenges. Across all datasets, CancerSeg-XA consistently outperformed the baseline DeepLabV3+ in terms of segmentation accuracy, recall, and F1-score. Specifically, it achieved accuracy improvements of 4.78%, 4.31%, and 3.22% on BCSS, PanNuke, and PUMA, respectively, along with substantial gains in FwIoU. These results highlight the model’s ability to generalize effectively across varied histopathological contexts, positioning CancerSeg-XA as a promising solution for clinical integration and future research in digital pathology.

Breast cancer remains a formidable health challenge requiring advanced computational tools for accurate diagnosis and treatment planning. This study hypothesizes that modifica-tions to the DeepLabV3+ architecture, such as incorporating an attention layer and replacing the ResNet50 backbone with Xcep-tion, can significantly enhance segmentation accuracy and model stability for breast cancer histopathological images. To test this hy-pothesis, we evaluated the performance of the original DeepLabV3+ and three modified versions for semantic segmenta-tion using the “Breast Cancer Semantic Segmentation” (BCSS) da-taset, which provides pixel-wise annotations of breast cancer tis-sues. The proposed modifications include integrating an attention layer between the encoder and decoder (Model 1), replacing the ResNet50 backbone with an Xception backbone up to 'block5' (Model 2), and combining the Xception backbone with the atten-tion layer (CancerSeg-XA). The models were implemented and trained in the Kaggle Notebook environment, and their perfor-mance was assessed based on training and validation accuracy. The results show that Model 1 improved the model stability and accuracy compared to DeepLabV3+, whereas Model 2 and Can-cerSeg-XA achieved significant accuracy improvements of 91.47% and 91.57%, respectively, over the baseline DeepLabV3+ accuracy of 85.7%. CancerSeg-XA demonstrated enhanced training stabil-ity, making it a promising approach for clinical application in breast cancer diagnosis and treatment.

Egypt faces a series of challenges and opportunities in developing its supply chain management (SCM) and port operations. These developing include technological infrastructure expansion, digital transformation, regional integration and social responsibility. Artificial Intelligence (AI) is emerging as a solution for (SCM) to meet this challenges and achieve optimal performance. This study suggests that the integration of Artificial Intelligence (AI) in SCM aims to improve port performance in Egypt, based on the results of the questionnaires. However, this integration remains insufficient as it faces several obstacles, including the variability of AI maturity among supply chain actors, data security concerns, and the need to develop specialized skills in AI. Specifically, the findings of the study were analyzed in relation to the existing literature and their relevance to the research objectives and questions. The purpose of this research paper is to provide a summary of the findings and conclusions, as well as the limitations and potential contributions of the research.

A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development during the past few years. Due to the volatile nature of the crypto-market, cryptocurrency trading involves a high level of risk. In this paper, a new normalized decomposition-based, multi-objective particle swarm optimization (N-MOPSO/D) algorithm is presented for cryptocurrency algorithmic trading. The aim of this algorithm is to help traders find the best Litecoin trading strategies that improve their outcomes. The proposed algorithm is used to manage the trade-offs among three objectives: the return on investment, the Sortino ratio, and the number of trades. A hybrid weight assignment mechanism has also been proposed. It was compared against the trading rules with their standard parameters, MOPSO/D, using normalized weighted Tchebycheff scalarization, and MOEA/D. The proposed algorithm could outperform the counterpart algorithms for benchmark and real-world problems. Results showed that the proposed algorithm is very promising and stable under different market conditions. It could maintain the best returns and risk during both training and testing with a moderate number of trades.

The advent of quantum computers could enable the resolution of complex computational problems that conventional cryptographic protocols find challenging. As a result, the formidable computing capabilities of quantum computers may render all present-day cryptographic schemes that rely on computational complexity ineffectual. Inspired by these possibilities, the primary purpose of this paper is to suggest a quantum image encryption scheme based on quantum cellular automata with mixed multi-chaos hybrid maps and a hyperchaotic system with quantum operations. To achieve desirable encryption outcomes, we designed an encryption scheme involving two main operations: (1) pixel-level diffusion and (2) pixel-level permutation. Initially, the secret keys generated using the hyperchaotic system were closely tied to the original image. During the first phase, the establishment of correlations among the image pixels, in addition to the three chaotic sequences obtained from the hyperchaotic system, was achieved with the application of a quantum-state superposition and measurement principle, wherein the color information of a pixel is described using a single qubit. Therefore, the three channels of the plain image were subjected to quantum operations, which involve Hadamard transformation and the quantum-controlled NOT gate, before the diffusion of each color channel with the hyperchaotic system. Subsequently, a quantum ternary Toffoli gate was used to perform the diffusion operation. Next, the appropriate measurement was performed on the three diffused channels. To attain the confusion phase, a blend of mixed multi-chaos hybrid maps and a two-dimensional quantum cellular automaton was used to produce random and chaotic sequence keys. Subsequently, the circular shift was utilized to additionally shuffle the rows and columns of the three diffused components, in order to alter the positions of their pixel values, which significantly contributes to the permutation process. Lastly, the three encoding channels, R, G, and B, were merged to acquire the encrypted image. The experimental findings and security analyses established that the designed quantum image encryption scheme possesses excellent encryption efficiency, a high degree of security, and the ability to effectively withstand a diverse variety of statistical attacks.

The study of human behaviors aims to gain a deeper perception of stimuli that control decision making. To describe, explain, predict, and control behavior, human behavior can be classified as either non-aggressive or anomalous behavior. Anomalous behavior is any unusual activity; impulsive aggressive, or violent behaviors are the most harmful. The detection of such behaviors at the initial spark is critical for guiding public safety decisions and a key to its security. This paper proposes an automatic aggressive-event recognition method based on effective feature representation and analysis. The proposed approach depends on a spatiotemporal discriminative feature that combines histograms of oriented gradients and dense optical flow features. In addition, the principal component analysis (PCA) and linear discriminant analysis (LDA) techniques are used for complexity reduction. The performance of the proposed approach is analyzed on three datasets: Hockey-Fight (HF), Stony Brook University (SBU)-Kinect, and Movie-Fight (MF), with accuracy rates of 96.5%, 97.8%, and 99.6%, respectively. Also, this paper assesses and contrasts the feature engineering and learned features for impulsive aggressive event recognition. Experiments show promising results of the proposed method compared to the state of the art. The implementation of the proposed work is available here.

In recent decades, the crime rate has significantly increased. As a result, the automatic video monitoring system has become increasingly important for researchers in computer vision. A person’s baggage classification is essential in knowing who has abandoned baggage. This paper proposes a model for classifying humans carrying baggage. Two approaches are used for comparison using a deep learning technique. The first approach is based on categorizing human-containing image regions as either with or without baggage. The second approach classifies human-containing image regions based on the human position direction attribute. The proposed model is based on the pretrained DenseNet-161 architecture. It uses a "fit-one-cycle policy" strategy to reduce the training time and achieve better accuracy. The Fastai framework is used for implementation due to its super computational ability, simple workflow, and unique data cleansing functionalities. Our proposed model was experimentally validated, and the results show that the process is sufficiently precise, faster, and outperforms the existing methods. We achieved an accuracy of between 96% and 98.75% for the binary classification and 96.67% and 98.33% for the multi-class classification. For multi-class classification, the datasets, such as PETA, INRIA, ILIDS, and MSMT17, are re-annotated with one’s direction information about one’s stance to test the suggested approach’s efficacy.