Samy S. Abu-Naser

Cardiovascular diseases remain the leading cause of global mortality, responsible for an estimated 17.9 million deaths annually. Early and interpretable diagnosis is critical to improving patient outcomes. This study bridges the gap between black-box machine learning models and clinical interpretability by developing a hybrid expert system that derives transparent, evidence-based diagnostic rules directly from patient data. We trained a constrained Decision Tree model (maximum depth = 3) on a publicly available dataset of 918 patients to ensure inherent explainability. The tree structure was systematically parsed and translated into a set of eight mutually exclusive IF-THEN clinicalrules, forming the knowledge base of a prototype rule-based expert system. Each rule is accompanied by its confidence score, support, and a clinically meaningful interpretation. The system achieved an accuracy of 82.1% (95% CI: 76.5 – 87.6%), a precision of 87.1%, and a recall of 79.4% on an independent test set. Five-fold cross-validation confirmed robustness (mean accuracy: 81.5% ± 6.1%), and the area under the ROC curve (AUC) was 0.886, indicating excellent discriminative ability. Crucially, for each case, the system outputs the specific diagnostic rule applied, a risk-stratified recommendation (e.g., urgent cardiology consultation, routine screening), and a confidence score. and a clinical interpretation, moving beyond a mere prediction to an auditable diagnostic aid. This transforms the model from a predictive endpoint into a fully auditable and interpretable clinical decision support tool. This work provides a reproducible and scalable framework for building accurate, transparent, and clinically deployable diagnostic systems, facilitating greater trust and adoption in real-world healthcare settings.

Bone fracture detection is a critical task in orthopedic radiology, where timely and accurate diagnosis significantly impacts patient recovery. Manual interpretation of X-ray images can be challenging due to the subtle nature of certain fractures and the high workload of clinicians. This study explores the efficacy of Deep Learning models in automating the detection of bone fractures. We implemented and compared two prominent architectures: ResNet50 and a proposed optimized MobileNetV2. To address the challenges of class imbalance and limited clinical data, we utilized advanced Data Augmentation techniques and Dropout regularization. The models were evaluated on a dataset of 425 unseen test images. The results demonstrate that the proposed MobileNetV2 model significantly outperformed the baseline, achieving a remarkable 99% accuracy, a 1.00 Recall for fracture detection, and a Macro F1-score of 0.95. In contrast, while ResNet50 achieved 94% accuracy, it exhibited severe bias and failed to generalize to normal cases. Furthermore, we employed Grad-CAM (Gradient-weighted Class Activation Mapping) to provide visual interpretability, confirming that the model’s "attention" aligns with actual anatomical fracture sites. These findings suggest that the lightweight MobileNetV2 architecture is not only highly accurate but also suitable for integration into portable medical devices and real-time clinical decision support systems, offering a reliable tool for enhancing diagnostic precision in emergency settings.

Background: Coronavirus is a family of viruses that can cause disease such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In 2019, Coronavirus was identified as the cause of the outbreak in China. Therefore, in this paper we will review what the Coronavirus is and explore the symptoms associated with it. Objectives: The main objective of this expert system is to obtain appropriate diagnosis of the disease. Methods: In this paper, the expert system is designed for the ability of doctors to detect and diagnose symptoms of coronavirus, a common symptom of this disease are fever, cough, pneumonia in both lungs and shortness of breath. This system presents the disease symptoms, which day will be recognizing symptoms, survival and spread, favorable conditions and image of this symptoms. Clips and Delphi expert system languages are used for designing and implementing the proposed expert system. Result: The expert system in the diagnosis of Coronavirus disease was assessed by doctors and they were satisfied and accepted with its quality of performance. Conclusions: The expert system is easy for doctors and people have interested in the coronavirus to detect and diagnosis the symptoms that may face this disease.

Background: Safflower is a highly branched, herbaceous, thistle-like annual plant. Any section of the safflower plant sections can suffering from a disease that weakens the ability to grow and eliminates its production. Therefore, in this paper will identify the pests and diseases present in safflower culture and detect the symptoms in each disease. Also images are showing the symptom form in this disease. Objectives: The main objective of this expert system is to obtain appropriate diagnosis of the disease. Methods: In this paper, the expert system is designed for the ability of agricultural engineers to detect and diagnose disease of safflower like as: alternaria blight, cercospora leaf spot, powdery mildew, head rot and wilt, mosaic, ramularia leaf spot, rust, wilt, and root rot. This system displays the disease symptoms, survival and spread, favorable conditions and image for each disease. Clips and Delphi expert system languages are used for designing and implementing the proposed expert system. Results: The expert system in the diagnosis of safflower diseases was evaluated by farmers and agricultural engineers and they were satisfied and accepted with its quality of performance. Conclusions: The expert system is easy for farmers and people have experience in the plant of safflower to detect and diagnosis the symptoms that may face this plant from several disease.

Background: The castor bean is a large grassy or semi-wooden shrub or small tree. Any part of the castor plant parts can suffering from a disease that weakens the ability to grow and eliminates its production. Therefore, in this paper will identify the pests and diseases present in castor culture and detect the symptoms in each disease. Also images is showing the symptom form in this disease. Objectives: The main objective of this expert system is to obtain appropriate diagnosis of the disease. Methods: In this paper, the expert system is designed for the ability of agricultural engineers to detect and diagnose disease of castor like as: seeding blight, alternaria blight, cercospora leaf spot, powdery mildew and wilt. This system presents the disease symptoms, survival and spread, favorable conditions and image for each disease. Clips and Delphi expert system languages are used for designing and implementing the proposed expert system. Results: The expert system in the diagnosis of castor diseases was assessed by farmers and agricultural engineers and they were satisfied and accepted with its quality of performance. Conclusions: The expert system is easy for farmers and people have experience in the plant of castor to detect and diagnosis the symptoms that may face this plant from several disease.

Background: Thyroid disease is a group of disorders that affects the thyroid gland. The thyroid is a small, butterflyshaped gland in the front of your neck that makes thyroid hormones. Thyroid hormones control how your body uses energy, so they affect the way nearly every organ in your body works—even the way your heart beats. Therefore, in this paper will identify what is the thyroid and diseases present in thyroid and detect the symptoms in each disease. Objectives: The main objective of this expert system is to obtain appropriate diagnosis of this disease. Methods: In this paper, the expert system is designed for the ability of doctors to detect and diagnose disease of thyroid. The proposed expert system presents an overview about thyroid diseases are given, the cause of diseases are outlined and the treatment of disease whenever possible is given out. SL5 Object Expert System language was used for designing and implementing the proposed expert system. Results: The expert system in the diagnosis of thyroid diseases was assessed by Medical student and doctors they were satisfied and accepted with its quality of performance. Conclusions: The expert system is easy for podiatric physician, patients and people have experience to detect and diagnosis the symptoms that may face this disease.

The global cultivation of grapes reaches approximately 77.8 million tons annually, according to the International Organization of Vine and Wine. While grapes remain a vital agricultural commodity and dietary staple worldwide, their production faces serious threats from common diseases like black rot, Esca, and leaf blight. Current disease detection methods in modern vineyards primarily depend on manual visual inspection, a practice that often delays diagnosis and leads to reduced yields and compromised fruit quality. The integration of automated detection methods, particularly those based on machine learning, is crucial for promoting sustainable viticulture. This study leverages the power of deep learning, a subfield of machine learning particularly adept at interpreting image data. Specifically, we implemented Convolutional Neural Networks (CNNs) to classify images of grapevine leaves as either healthy or diseased. The models employed included a custom-built baseline CNN and a range of advanced transfer learning models: DenseNet121, EfficientNetB7, MobileNetV2, ResNet50, and VGG16. While we initially hypothesized that ResNet50 would yield the highest accuracy due to its deep architecture, experimental results revealed that EfficientNetB7 outperformed all other models. To further enhance classification performance, we constructed a max-voting ensemble using the top three performing models. This ensemble approach outmatched the performance of individual models. The final model was deployed via a web-based interface, enabling vineyard professionals and growers to detect black rot, Esca, leaf blight, or confirm leaf health by uploading images captured in real-world vineyard environments.

Abstract: Low vision is a significant global health issue that affects millions of individuals, limiting their daily activities and reducing their quality of life. Accurate and early diagnosis is essential for effective management and treatment, yet existing diagnostic methods often fall short due to various limitations in precision and accessibility. This paper proposes an innovative and accurate system for diagnosing low vision, leveraging advanced technologies such as artificial intelligence and expert systems. The proposed system integrates patient history, vision test results, and imaging data to provide precise diagnoses and tailored recommendations. Designed with a user-friendly interface, the system is intended to assist ophthalmologists, optometrists, and general practitioners in diagnosing a wide range of low vision conditions efficiently. Initial evaluations suggest the system’s potential to significantly enhance diagnostic accuracy and patient outcomes, while also reducing the burden on healthcare professionals. This work represents a step forward in bridging the gap between technology and accessible eye care for patients worldwide.

Abstract: Sorting algorithms have always been the primary focus of data organization and have been the same since they were discovered. They play a vital role in reducing work and maximizing efficiency as well as accuracy. This paper aims at comparing and examining traditional sorting algorithms, usually applied by programmers as opposed to AI-based methods like Decision Trees and Neural Networks. The performance of these sorting techniques would be evaluated on time over such data executions considering memory requirement and accuracy on different datasets. Most traditional methods could sort structured data even of a smaller dataset, but they cannot work well when it comes to scaling and unstructured data [4]. AI-based algorithms, however, seem to perform better in terms of accuracy, which is superior in its implementation to complex and unstructured datasets, at a great cost of and accuracy; thus, making possible insights into the practical implementations. These findings show the potentials of hybrid methods regarding modern data sorting application challenges and pave the way for further future optimization and real-world implementation [8].

Digital communities drive the increasing demand for computing and energy [1], Over the past fifty years, the digital age has heavily relied on hardware improvements to meet this demand, and as microchips approach their physical limits, optimizing the code they run on becomes critical, especially for algorithms that execute trillions of times each day. Sorting algorithms are fundamental tools used by computers around the world continuously [2]. By improving these algorithms with artificial intelligence (AI), the efficiency and accuracy of sorting vast amounts of data can be enhanced, offering better scalability. This paper explores the role of AI in improving sorting techniques by leveraging AI, to achieve faster and more efficient data management, and how AI techniques can address the limitations of traditional sorting.

Abstract: Sorting is a fundamental operation in computer science, critical for data organization, retrieval, and analysis. Traditional algorithms like QuickSort and MergeSort excel in smaller datasets but struggle in big data contexts, prompting the need for innovative solutions. Artificial Intelligence (AI) has emerged as a transformative force, enabling adaptive, intelligent sorting mechanisms that significantly outperform conventional methods in efficiency and scalability. This paper examines the integration of AI into sorting algorithms, highlighting innovative approaches, comparative analyses, and implications for big data. By exploring machine learning, neural networks, and genetic algorithms, this research reveals the potential of AI-driven sorting to enhance performance in diverse environments. The findings emphasize the importance of hybrid AI-traditional models for addressing computational costs and scalability challenges, underscoring the need for continued research.

The protracted conflict in Gaza has significantly intensified humanitarian crises, posing severe challenges to relief operations due to resource scarcity, logistical obstacles, and the complexities imposed by the blockade. This study proposes the development of an intelligent sorting system utilizing advanced artificial intelligence (AI) methodologies to prioritize, classify, and manage critical humanitarian data efficiently in real-time. Designed to meet the specific demands of crisis response, this AI-driven system organizes and categorizes essential data streams—including urgent aid requests, resource allocation data, and public health information—while addressing logistical and infrastructural barriers typical of conflict zones. Leveraging machine learning algorithms, the system identifies and ranks high priority needs dynamically, supporting accurate resource distribution and prompt emergency interventions. Findings from this study indicate improvements in response times and overall resource management efficiency, highlighting the transformative potential of AI in humanitarian data handling. Furthermore, the study presents a scalable framework adaptable to other crisis-affected regions worldwide, enhancing the management of complex data demands under extreme conditions. The system’s scalability underscores its relevance as an innovative tool for advancing humanitarian logistics and emergency response in volatile, high-risk environments."

Abstract: Sorting algorithms are at the core of data management processes, enabling efficient organization and retrieval of information. However, the fast expansion of Big Data introduces challenges that traditional sorting algorithms need help to address, such as scalability, adaptability, and processing speed. This research delves into AI-driven sorting algorithms, exploring their ability to revolutionize data processing through intelligent decision-making, real-time adaptability, and enhanced scalability. Innovations such as neural network-guided sorting, reinforcement learning-based optimization, and hybrid AI-traditional models are examined in-depth. Applications of these technologies are highlighted in industries such as healthcare, finance, logistics, and social media analytics. The findings underscore AI's potential to transform sorting methodologies and adapt to the ever-growing demands of the digital era, providing a roadmap for future research and applications.

Abstract: Sorting is a fundamental operation in data processing, serving as the backbone for efficient searching, analytics, and data management. This paper investigates two well-known sorting algorithms, QuickSort and MergeSort, with an emphasis on their computational complexities and real-world applications. In light of recent advancements in artificial intelligence, AI-driven enhancements are explored to optimize sorting performance. Python implementations of both algorithms are presented to compare their efficiency and highlight potential performance improvements through AI-based optimizations.

Abstract: This research paper examines AI-driven sorting algorithms tailored for big data, exploring the integration of traditional and advanced methods to enhance data processing efficiency. The focus is on the performance of QuickSort, MergeSort, and AI-augmented algorithms such as deep neural networks (DNNs) and reinforcement learning (RL) models across various data types and sizes. The proposed methodology emphasizes the use of diverse real-world and synthetic datasets to assess execution time, memory usage, and computational efficiency. The findings demonstrate that AI techniques offer significant advantages in processing large-scale data, particularly in dynamic and multimodal scenarios.

The advent of big data has ushered in an era of unprecedented data volumes, necessitating efficient and scalable sorting algorithms. Traditional sorting algorithms, while effective for smaller datasets, struggle to handle the massive data loads generated by modern applications. To address this challenge, AI-driven sorting algorithms have emerged as a promising solution, leveraging the power of machine learning and artificial intelligence to optimize sorting processes. This research paper provides a comprehensive overview of AI-driven sorting algorithms, exploring their underlying principles, methodologies, and applications in big data scenarios. I delve into the key techniques, including machine learning-based approaches. Additionally, I discuss the potential benefits and challenges associated with AI-driven sorting algorithms, highlighting their potential to significantly improve sorting efficiency, accuracy, and scalability in big data contexts.

As artificial intelligence (AI) technologies rapidly evolve and permeate various aspects of society, the need for effective regulation and governance has become increasingly critical. This paper explores the current landscape of AI regulation, examining existing frameworks and their efficacy in addressing the unique challenges posed by AI. Key issues such as ensuring compliance, mitigating biases, and maintaining transparency are analyzed. The paper also delves into ethical considerations surrounding AI governance, emphasizing the importance of fairness and accountability. Through case studies of different regulatory approaches and their outcomes, the paper highlights both successes and areas for improvement. Finally, it offers insights into future directions for AI regulation, advocating for innovative policies and enhanced international cooperation to ensure that AI technologies are developed and deployed responsibly. This comprehensive analysis aims to contribute to the ongoing discourse on creating robust regulatory mechanisms that can keep pace with the rapid advancement of AI.

Abstract: The integration of artificial intelligence (AI) into mental health care has the potential to revolutionize the field by enhancing diagnostic accuracy, personalizing treatment, and improving access to care. This paper explores the advancements in AI technologies applied to mental health, including machine learning algorithms for diagnosis, natural language processing for therapeutic applications, and predictive analytics for personalized care. It also examines the ethical and practical challenges associated with these technologies, such as privacy concerns, algorithmic bias, and the need for regulatory frameworks. By analyzing current applications and emerging trends, this paper aims to provide a comprehensive overview of how AI can transform mental health care while addressing the associated challenges and opportunities.

The integration of artificial intelligence (AI) into drug discovery is revolutionizing the pharmaceutical industry by accelerating the development of novel therapeutics. AI-powered tools enable researchers to process vast datasets, identify drug candidates, and predict their efficacy and safety with unprecedented speed and accuracy. This paper explores the transformative impact of AI on drug discovery, highlighting key advancements in machine learning algorithms, deep learning, and predictive modeling. Additionally, it addresses the challenges associated with AI implementation, including data quality, regulatory hurdles, and ethical considerations. By analyzing case studies of AI-driven pharmaceutical breakthroughs, this paper underscores AI's potential to streamline drug development, reduce costs, and address unmet medical needs. The future of drug discovery is poised to shift dramatically as AI continues to advance, offering promising solutions for improving human health.

Abstract: As the frequency and sophistication of cyberattacks continue to rise, organizations face increasing challenges in safeguarding their digital infrastructures. Traditional cybersecurity measures often struggle to keep pace with rapidly evolving threats, creating a pressing need for more adaptive and proactive solutions. Artificial Intelligence (AI) has emerged as a transformative force in this domain, offering enhanced capabilities for detecting, analyzing, and preventing cyberattacks in real- time. This paper explores the pivotal role of AI in strengthening cybersecurity defenses by leveraging machine learning algorithms, predictive analytics, and automation to anticipate and mitigate potential threats before they manifest. Furthermore, it examines AI's ability to evolve with emerging attack vectors, providing a dynamic response to an ever-changing threat landscape. The paper also addresses the limitations and ethical considerations surrounding AI-driven cybersecurity, advocating for a balanced approach to its deployment. Through this exploration, the research underscores how AI is redefining the future of cyber defense by shifting the focus from reactive to proactive strategies.

Climate change presents a critical challenge that demands advanced analytical tools to predict and mitigate its impacts. This paper explores the role of artificial intelligence (AI) in enhancing climate modeling, emphasizing how AI-driven methods are revolutionizing our understanding and response to climate change. By integrating machine learning algorithms with diverse data sources such as satellite imagery, historical climate records, and real-time sensor data, AI improves the accuracy, efficiency, and granularity of climate predictions. The paper reviews key AI techniques, including neural networks and ensemble models, and their applications in simulating extreme weather events and forecasting long-term climate trends. Additionally, it examines the impact of AI-enhanced climate models on policy-making and strategic planning. Despite significant advancements, challenges such as data quality, computational demands, and model interpretability remain. This study underscores the potential of AI to transform climate modeling and highlights areas for future research and development to harness these technologies for a sustainable future.

Abstract: The convergence of nanotechnology and artificial intelligence (AI) represents a transformative frontier in modern science, with the potential to revolutionize multiple industries, particularly healthcare. Nanotechnology enables the manipulation of matter at the atomic and molecular scale, while AI offers sophisticated data analysis, pattern recognition, and decision-making capabilities. This paper explores the synergies between these two fields, focusing on their impact on medical diagnostics, targeted drug delivery, and personalized treatments. By leveraging AI's predictive power and nanotechnology's precision, healthcare can achieve unprecedented levels of accuracy in disease detection and treatment, leading to improved patient outcomes. Beyond healthcare, this convergence extends to environmental monitoring, smart materials, and manufacturing, offering solutions to some of the world’s most pressing challenges. The paper also discusses the ethical considerations and potential risks of integrating AI with nanotechnology, emphasizing the need for responsible innovation. Together, AI and nanotechnology hold the promise of reshaping industries and improving quality of life on a global

Abstract: Generative AI is transforming the creative industries by redefining how art, music, and media are produced and experienced. This paper explores the profound impact of generative AI technologies, such as deep learning models and neural networks, on creative processes. By enabling artists, musicians, and content creators to collaborate with AI, these systems enhance creativity, speed up production, and generate novel forms of expression. The paper also addresses ethical considerations, including intellectual property rights, the role of human creativity in AI-assisted production, and the potential risks of AI-generated content. As the convergence of AI and creativity deepens, this study highlights the opportunities and challenges posed by generative AI in revolutionizing the landscape of creative industries.

As artificial intelligence (AI) technologies become increasingly integrated into various facets of society, their impact on human rights has garnered significant attention. This paper examines the intersection of AI and human rights, focusing on key issues such as privacy, bias, surveillance, access, and accountability. AI systems, while offering remarkable advancements in efficiency and capability, also pose risks to individual privacy and can perpetuate existing biases, leading to potential discrimination. The use of AI in surveillance raises ethical concerns about the balance between security and civil liberties. Additionally, disparities in access to AI technologies can exacerbate inequalities, affecting human rights across different populations. This paper explores these challenges and proposes frameworks for ensuring that AI development and deployment adhere to human rights principles. By analyzing current practices and policies, the study aims to provide actionable recommendations for creating a more equitable and responsible AI landscape.

Abstract: In an era of rapid technological advancements, artificial intelligence (AI) has transformed surveillance systems, enhancing security capabilities across the globe. However, the deployment of AI-driven surveillance raises significant ethical concerns, particularly in balancing the need for security with the protection of individual privacy. This paper explores the ethical challenges posed by AI surveillance, focusing on issues such as data privacy, consent, algorithmic bias, and the potential for mass surveillance. Through a critical analysis of the tension between security and privacy, this paper examines the implications of AI technologies on civil liberties and human rights. It also highlights the importance of establishing clear regulatory frameworks and ethical guidelines to ensure that AI surveillance systems operate within boundaries that respect individual freedoms. In navigating this delicate balance, we propose solutions that prioritize transparency, accountability, and the protection of privacy in the digital age.

Abstract: The rapid advancement of artificial intelligence (AI) has catalyzed significant developments in autonomous systems, which are increasingly shaping diverse sectors including transportation, robotics, and industrial automation. This paper explores the evolution of AI technologies that underpin these autonomous systems, focusing on their capabilities, applications, and the challenges they present. Key areas of discussion include the technological innovations driving autonomy, such as machine learning algorithms and sensor integration, and the practical implementations observed in autonomous vehicles, drones, and robotic systems. Additionally, the paper addresses critical challenges including safety, ethical concerns, and regulatory issues that influence the deployment and acceptance of autonomous technologies. By examining current trends and future prospects, this research aims to provide a comprehensive overview of how AI is transforming the landscape of autonomous systems and to identify key areas for future research and development.

Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine learning algorithms are improving imaging analysis and reducing diagnostic errors. Furthermore, the paper discusses advancements in genetic screening, liquid biopsies, and biomarkers, emphasizing their potential in identifying high-risk individuals and supporting personalized treatment plans. The paper provides a forward-looking perspective on the future of breast cancer detection, addressing both the opportunities and challenges associated with the adoption of these cutting-edge technologies to improve patient outcomes.

Abstract: The integration of Artificial Intelligence (AI) into medical imaging marks a transformative advancement in healthcare, significantly enhancing diagnostic accuracy, efficiency, and patient outcomes. This paper delves into the application of AI technologies in medical image analysis, with a particular focus on techniques such as convolutional neural networks (CNNs) and deep learning models. We examine how these technologies are employed across various imaging modalities, including X-rays, MRIs, and CT scans, to improve disease detection, image segmentation, and diagnostic support. Furthermore, the paper discusses the challenges associated with AI-driven medical imaging, such as data quality, model interpretability, and ethical considerations. By analyzing recent advancements and real-world case studies, this paper offers insights into the current landscape of AI in medical imaging and explores its potential future directions. The findings underscore the ongoing evolution of AI technologies and their pivotal role in advancing medical diagnostics and treatment strategies.

Abstract: Artificial Intelligence (AI) is transforming organizational dynamics by reshaping both structures and processes. This paper examines how AI-driven innovations are redefining organizational frameworks, ranging from shifts in hierarchical models to the adoption of decentralized decision-making. It explores AI's impact on key processes, including workflow automation, data analysis, and decision support systems. Through case studies and empirical research, the paper illustrates the advantages of AI in enhancing efficiency, driving innovation, and fostering agility within organizations. Additionally, it addresses the challenges posed by AI integration, such as resistance to change, ethical concerns, and data security issues. The paper concludes with insights into future trends and implications for organizations navigating the evolving landscape of AI-driven change.