Mokhaled N. A. Al-Hamadani

Domain shift between microscopy imaging domains poses a significant challenge for deploying deep learning-based cell detection models across different experimental setups. Manual annotation of new microscopy datasets remains resource-intensive and time-consuming. This study presents a detection-oriented Cycle-Consistent Generative Adversarial Network (CycleGAN)-based annotation transfer framework for adapting a labeled B16BL6 source domain to a HeLa target domain. Annotated B16BL6 melanoma microscopy images are translated into the visual appearance of HeLa microscopy data while retaining their original bounding-box annotations, enabling YOLOv8x detector training without full manual annotation of the target domain. Three YOLOv8x configurations were compared: a source-only bright B16BL6 baseline, an intensity-inverted dark B16BL6 baseline, and the proposed CycleGAN-translated B16BL6 → HeLa configuration. Performance was evaluated using standard object detection metrics on a manually annotated 100-frame HeLa target-domain subset, together with complementary unsupervised proxy metrics on the full unlabeled HeLa dataset. The CycleGAN-trained detector achieved the highest supervised target-domain performance, with a precision of 0.244, recall of 0.353, F1-score of 0.288, and mAP@0.50 of 0.186, compared with mAP@0.50 values of 0.027 and 0.009 for the bright and dark baselines, respectively. It also achieved the highest exploratory composite reliability score on the full HeLa sequence. These findings demonstrate that source-to-target image translation improves detector generalization under the investigated B16BL6 → HeLa phase-contrast microscopy domain shift, thereby reducing the need for extensive manual target-domain annotation.

Accurate and automated detection and tracking of cells in microscopy images is a persistent challenge in biotechnology and biomedical research. Effective detection and tracking are crucial for understanding biological processes and extracting meaningful data for subsequent simulations. In this study, we present an integrated pipeline that leverages a fine-tuned YOLOv8x model for detecting cells and cell divisions across microscopy image series. While YOLOv8x exhibits strong detection capabilities, it occasionally misses certain cells, leading to gaps in data. To mitigate this, we incorporate the DeepSORT tracking algorithm, which enhances data association and reduces the cells’ identity (ID) switches by utilizing a pre-trained convolutional network for robust multi-object tracking. This combination ensures continuous detection and compensates for missed detections, thereby improving overall recall. Our approach achieves a recall of 93.21% with the enhanced DeepSORT algorithm, compared to the 53.47% recall obtained by the original YOLOv8x model. The proposed pipeline effectively extracts detailed information from structured image datasets, providing a reliable approximation of cellular processes in culture environments.

Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields.

Introduction/purpose: The utilization of machine learning methods has become indispensable in analyzing large-scale, complex data in contemporary data-driven environments, with a diverse range of applications from optimizing business operations to advancing scientific research. Despite the potential for insight and innovation presented by these voluminous datasets, they pose significant challenges in areas such as data quality and structure, necessitating the implementation of effective management strategies. Machine learning techniques have emerged as essential tools in identifying and mitigating these challenges and developing viable solutions to address them. The MNIST dataset represents a prominent example of a widely-used dataset in this field, renowned for its expansive collection of handwritten numerical digits, and frequently employed in tasks such as classification and analysis, as demonstrated in the present study. Methods: This study employed the MNIST dataset to investigate various statistical techniques, including the Principal Components Analysis (PCA) algorithm implemented using the Python programming language. Additionally, Support Vector Machine (SVM) models were applied to both linear and non-linear classification problems to assess the accuracy of the model. Results: The results of the present study indicate that while the PCA technique is effective for dimensionality reduction, it may not be as effective for visualization purposes. Moreover, the findings demonstrate that both linear and non-linear SVM models were capable of effectively classifying the dataset. Conclusion: The findings of the study demonstrate that SVM can serve as an efficacious technique for addressing classification problems.

A terrifying spread of COVID-19 (which is also known as severe acute respiratory syndrome coronavirus 2 or SARS-COV-2) led scientists to conduct tremendous efforts to reduce the pandemic effects. COVID-19 has been announced pandemic discovered in 2019 and affected millions of people. Infected people may experience headache, body pain, and sometimes difficulty in breathing. For older people, the symptoms can get worse. Also, it can cause death because of the huge effect on some parts of the human body, particularly for those who have chronic diseases like diabetes. Machine learning algorithms are applied to patients diagnosed with Corona Virus to estimate the severity of the disease depending on their chronic diseases at an early stage. Chronic diseases could raise the severity of COVID-19 and that is what has been proved in this paper. This paper applies different machine learning techniques such as random forest, decision tree, linear regression, binary search, and k-nearest neighbor on Mexican patients’ dataset to find out the impact of lifelong illnesses on increasing the symptoms of the virus in the human body. Besides, the paper demonstrates that in some cases, especially for older people, the virus can cause inevitable death.

In many countries, particularly, third world countries. The common issue is saving energy. That`s why smart systems considered now primary for life requirements. This work aims to solve the energy saving problem. We prepared a street model that contains several lampposts on both sides of the street; we placed three IR sensors between the lampposts alongside the street. The IR sensors are connected to the controller (in this work we used Arduino UNO). The controller takes the signal from the IR sensor, and then it sends the command to the lamppost to turn on or off. Depending on the number of cars passed, (we took a sample of a number of cars that passed on an actual street) and through formulas we calculated the power consumed by the lampposts in two cases, the first case is when the lights is always on. The second case is when the smart system applied. We also applied fuzzy logic to the system to take the intensity of the ambient light (the sun light) under consideration. The results showed that the proposed smart lighting system is efficient and reliable in saving energy. The energy saved for both (smart and fuzzy) systems was enormous.

The network is a massive part of life today. It participates not only on one side of life but in nearly every station, especially in educational organizations. The key aim of education is to share data and knowledge, making the network important for education. In particular, it is essential to ensure the exchange of information; thus, no one can corrupt it. To safe and trustworthy transfers between users, integrity and reliability are crucial questions in all data transfer problems. Therefore, we have developed a secure campus network (SCN) for sending and receiving information among high-security end-users. We created a topology for a campus of multi networks and virtual local area networks (VLANs’) using cisco packet tracer. We also introduced the most critical security configurations, the networking used in our architecture. We used a large number of protocols to protect and accommodate the users of the SCN scheme.

To implement electronic exams and material resources sharing (which is usually limited), an operating base is must be available for trading information and correspondence management. In this paper, self-managed computer networks, no server was designed. This network aims to share information and correspondence management between the laboratory`s computers. A specific software called Packet tracer used for designing and simulating the network, also for choosing the right medium and install the IP address so that a maximum of data flowing with minimum time and no data loss can be achieved. A class-C IP address was selected since it is the famous class and it doesn`t need any special equipment. Also it has a wide range of of computers in case of expanding the network. We didn`t use a wireless medium to protect the network from hacking. The network was based on an Ethernet medium and a star-connection between the computers with an average signal distributer. An excellent result was achieved after testing the network with a low error percentage, as shown in the result section of this paper.