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In the rapidly evolving landscape of cybersecurity, the human element remains one of the most critical and complex factors to manage. Insider threats, whether originating from malicious intent or inadvertent actions, pose significant risks to organizational security. This paper explores the multifaceted nature of insider threats, examining the motivations and behaviors that drive individuals to compromise systems. By analyzing case studies and current research, we identify key vulnerabilities and the role of organizational culture in mitigating these risks. Furthermore, we propose comprehensive strategies for detecting, preventing, and responding to insider threats, emphasizing the importance of continuous education, robust access controls, and advanced monitoring technologies. This paper aims to provide a holistic understanding of the human factor in cybersecurity and offers practical solutions to address the pervasive challenge of insider threats.

The emergence of deep learning has transformed numerous fields, particularly image processing, where it has substantially enhanced image quality. This paper provides a structured overview of the objectives, methods, results, and conclusions of deep learning techniques for image enhancement. It examines deep learning methodologies and their applications in improving image quality across diverse domains. The discussion includes state-of-the-art algorithms such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Autoencoders, highlighting their applications in medical imaging, photography, and remote sensing. These methods have demonstrated notable impacts, including noise reduction, resolution enhancement, and contrast improvement. Despite its significant promise, deep learning faces challenges such as computational complexity and the need for large annotated datasets. outlines future research directions to overcome these limitations and further advance deep learning's potential in image enhancement.

This paper presents a deep learning-based system for predicting gold prices using historical data. The system leverages Long Short-Term Memory (LSTM), a specialized recurrent neural network architecture, to capture temporal dependencies and patterns in the time series data of gold prices. A comprehensive dataset of historical gold prices is used, and the model is trained on a sequence of past data points to predict future prices. The data is preprocessed using normalization techniques to improve the performance of the model. Experimental results demonstrate the effectiveness of the proposed model in providing accurate price predictions, offering potential utility in financial forecasting and decision-making processes. The system's performance is evaluated through visualization and statistical metrics, illustrating its capacity to track gold price trends and predict future market movements. This work contributes to the growing field of time series forecasting by applying deep learning techniques to financial markets.

In today's rapidly evolving technological landscape, the successful implementation of IT projects is increasingly contingent upon effective organizational change management (OCM). This research paper explores the intersection of OCM and IT project delivery, proposing a comprehensive framework that integrates these two critical domains. Through a review of existing literature and analysis of case studies, we identify key challenges and best practices for synchronizing OCM strategies with IT project management processes. Our findings reveal that the alignment of OCM with IT project delivery not only enhances project success rates but also promotes sustainable organizational transformation. This integrated approach ensures that technological advancements are supported by a well-prepared workforce, thereby minimizing resistance and maximizing adoption. The paper concludes with practical recommendations for practitioners aiming to bridge the gap between OCM and IT project delivery, ultimately fostering a more agile and resilient organizational environment.

This systematic review paper delves into the evolving landscape of cloud architectures for distributed edge computing, with a particular focus on decentralized and collaborative computing models. The aim of this systematic review is to synthesize recent advancements in decentralization techniques, collaborative scheduling, federated learning, and blockchain integration for edge computing. As edge computing becomes increasingly vital for supporting the Internet of Things (IoT) and other distributed systems, innovative strategies are needed to address challenges related to latency, resource management, and data security.The key findings highlight the benefits of latency-aware task management, autonomous serverless frameworks, and the collaborative sharing of computational resources. Additionally, the integration of federated learning and blockchain technologies offers promising solutions for enhancing data privacy and resource allocation. The versatility of edge computing is showcased through its applications in diverse domains, including healthcare and smart cities. Future research directions emphasize the need for optimized resource management, improved security protocols, standardization efforts, and application-specific innovations. By providing a comprehensive review of these developments, this paper underscores the critical role of decentralized and collaborative models in advancing the capabilities and efficiency of edge computing systems.

The synergy of distributed frameworks with Artificial Intelligence (AI) is pivotal for advancing applications in cloud computing. This review focuses on AI-powered applications in distributed systems, conducting a thorough examination. Analyzing foundational studies and real-world applications, it extracts insights into the dynamic interplay between AI and distributed frameworks. Quantitative measures allow a nuanced comparison, revealing diverse contributions. The survey provides a broad overview of the state-of-the-art, spanning applications like performance optimization, security, and IoT integration. The ensuing discussion synthesizes comparative measures, significantly enhancing our understanding. Concluding with recommendations for future research and collaborations, it serves as a concise guide for professionals and researchers navigating the challenging landscape of AI-powered applications in distributed cloud computing platforms.

Background: Comprehensive exploration of e-commerce landscape; Insights drawn from diverse scholarly sources across disciplines. Objective: Examine key themes, including COVID-19 impact, consumer behavior, business models, regulatory challenges, security, privacy, opportunities, and future trends in digital commerce. Method: The methodology involved a comprehensive literature review spanning various disciplines to explore e-commerce. Scholarly sources were gathered from academic databases and journals, focusing on key themes like the COVID-19 impact, consumer behavior, business models, regulatory challenges, security, privacy, opportunities, and future trends. Data analysis identified patterns and trends, with findings organized into distinct sections. Synthesizing and interpreting the results within the e-commerce context, along with peer feedback, ensured the study's rigor and credibility. Results: Uncover sustained shift in consumer preferences influenced by the pandemic; Provide insights into strategic approaches adopted by businesses in the digital marketplace. Conclusion: Anticipate future e-commerce trajectory, discussing emerging trends like metaverse integration, AI, augmented reality shopping, voice commerce, and online-offline convergence; Serve as a guide for businesses navigating challenges, seizing opportunities, and aligning with emerging trends; Offer a comprehensive understanding of the dynamic nature of e-commerce in the digital age. Keywords: Artificial Intelligence; Business; E-Commerce; Marketing.

Background: Choosing a banner supplier is a significant challenge for digital printing companies due to the various advantages offered by each supplier, often leading to selections based on subjective aspects such as price and quality. Objective: This research aims to develop a system that determines the best banner supplier to minimize production inefficiencies and maximize profits by comparing two calculation methods, Profile Matching and TOPSIS. Methods: A quantitative study was conducted using transaction data from the last six months. The parameter criteria used in this system include price, quality, delivery, availability, and payment terms. The study compares the effectiveness of Profile Matching and TOPSIS methods in identifying the best supplier. Results: The study results show that the TOPSIS method is superior, yielding 100% accuracy, 84% recall, and a 92% F1-score, outperforming the Profile Matching method. This demonstrates that the correct method and algorithm effectively provide the best alternative recommendations. Conclusion: The results indicate that using the TOPSIS method leads to more accurate and objective decisions based on predetermined criteria. The findings suggest that further research should focus on refining these methods to enhance decision-making in supplier selection.

Clustering algorithms play a pivotal role in the field of engineering, offering valuable insights into complex datasets. This review paper explores the landscape of developed clustering algorithms with a focus on their applications in engineering. The introduction provides context for the significance of clustering algorithms, setting the stage for an in-depth exploration. The overview section delineates fundamental clustering concepts and elucidates the workings of these algorithms. Categorization of clustering algorithms into partitional, hierarchical, and density-based forms lay the groundwork for a comprehensive discussion. The core of the paper delves into an extensive review of clustering algorithms tailored for engineering applications. Each algorithm is scrutinized in dedicated subsections, unraveling their specific contributions, applications, and advantages. A comparative analysis assesses the performance of these algorithms, delineating their strengths and limitations. Trends and advancements in the realm of clustering algorithms for engineering applications are thoroughly examined. The review concludes with a reflection on the challenges faced by existing clustering algorithms and proposes avenues for future research. This paper aims to provide a valuable resource for researchers, engineers, and practitioners, guiding them in the selection and application of clustering algorithms for diverse engineering scenarios.

When the world economy suffered a new financial economic crisis, it uncovered to the world that there were major failures within the Industrial financial system and it was caused by a number of issues including the practice of interest and the terrible effect it has on the financial crisis events. Issues that will be discussed in this paper are Riba, Gambling, Uncertainty, Derivatives, Saucerization, Sell of debt, Creation of Money, and the Private “Personal” interests. However, the paper will focus on the Islamic financial system and the financial instruments which are based and designed in compliance with Shariah Rules and Regulations, whether if practicing Islamic banking would cause such a crisis to accrue. The Sukuk, of the Islamic mortgage-Backed securities (MBS), and Islamic future contract will be discussed in order to prove why using the Islamic Financial system is better to assure that this kind of global crisis doesn’t come to pass again.