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Strategic Approaches to Safeguarding the Digital Future: Insights into Next-Generation Cybersecurity
Full text linkThis research investigates various strategic approaches to protecting the digital future and offers insights into the practices of the next generation of cybersecurity. The study\u27s primary objectives are to identify key challenges and trends in the current cybersecurity landscape, evaluate the effectiveness of traditional cybersecurity approaches, investigate emerging technologies for enhancing cybersecurity resilience, examine human factors in cybersecurity resilience, and develop recommendations for future cyber defense. All of these objectives will be accomplished through the course of the study. To analyze strategic approaches to cybersecurity, the study uses a methodology based on a survey of secondary data and synthesizes the current literature from recognized sources. Among the most significant discoveries are the significance of incorporating new technologies, addressing human aspects, encouraging collaboration, and emphasizing risk management. Policy implications include promoting cooperation and the exchange of information, investing in emerging technologies, raising awareness about cybersecurity, building regulatory frameworks, and boosting international cooperation through the promotion of international cooperation. According to the findings of this study, organizations, policymakers, and other stakeholders looking to improve their cybersecurity resilience and effectively protect the digital future can benefit from the insightful insights and practical advice provided
Neural Network Architectures for Real-Time Image and Video Processing Applications
Full text linkThis research optimizes neural network topologies for real-time image and video processing to achieve high-speed, accurate performance in dynamic contexts. The project aims to find efficient optimization methodologies, track neural network model progress, and highlight visual media applications. A secondary data review synthesizes peer-reviewed literature, technical reports, neural network design, and optimization advances. The research found that lightweight neural network architectures like MobileNet and Transformer-based Vision Transformers (ViTs) boost the computing economy without losing accuracy. Real-time applications need model pruning, quantization, knowledge distillation, and hardware-aware design. From real-time object identification in surveillance and autonomous driving to medical imaging and creative media creation, neural networks have transformed many applications. Despite these advances, balancing accuracy and economy, addressing hardware variability, and assuring ethical usage in face recognition remain issues. The report emphasizes the need for privacy-friendly and egalitarian AI rules. These results may help future research improve real-time visual processing systems and legislators control their responsible use in real-world applications
Opportunities and Challenges of Data Migration in Cloud
Full text linkCloud data migration is the process of moving data, localhost applications, services, and data to the distributed cloud processing framework. The success of this data migration measure is relying upon a few viewpoints like planning and impact analysis of existing enterprise systems. Quite possibly the most widely recognized process is moving locally stored data in a public cloud computing environment. Cloud migration comes along with both challenges and advantages, so there are different academic research and technical applications on data migration to the cloud that will be discussed throughout this paper. By breaking down the research achievement and application status, we divide the existing migration techniques into three strategies as indicated by the cloud service models essentially. Various processes should be considered for different migration techniques, and various tasks will be included accordingly. The similarities and differences between the migration strategies are examined, and the challenges and future work about data migration to the cloud are proposed. This paper, through a research survey, recognizes the key benefits and challenges of migrating data into the cloud. There are different cloud migration procedures and models recommended to assess the presentation, identifying security requirements, choosing a cloud provider, calculating the expense, and making any essential organizational changes. The results of this research paper can give a roadmap for data migration and can help decision-makers towards a secure and productive migration to a cloud computing environment
Significant of Gradient Boosting Algorithm in Data Management System
Full text linkGradient boosting machines, the learning process successively fits fresh prototypes to offer a more precise approximation of the response parameter. The principle notion associated with this algorithm is that a fresh base-learner construct to be extremely correlated with the “negative gradient of the loss function” related to the entire ensemble. The loss function\u27s usefulness can be random, nonetheless, for a clearer understanding of this subject, if the “error function is the model squared-error loss”, then the learning process would end up in sequential error-fitting. This study is aimed at delineating the significance of the gradient boosting algorithm in data management systems. The article will dwell much the significance of gradient boosting algorithm in text classification as well as the limitations of this model. The basic methodology as well as the basic-learning algorithm of the gradient boosting algorithms originally formulated by Friedman, is presented in this study. This may serve as an introduction to gradient boosting algorithms. This article has displayed the approach of gradient boosting algorithms. Both the hypothetical system and the plan choices were depicted and outlined. We have examined all the basic stages of planning a specific demonstration for one’s experimental needs. Elucidation issues have been tended to and displayed as a basic portion of the investigation. The capabilities of the gradient boosting algorithms were examined on a set of real-world down-to-earth applications such as text classification
Wave Structures for Nonlinear Schrodinger Types Fractional Partial Differential Equations Arise in Physical Sciences
Full text linkNonlinear partial differential equations are mostly renowned for depicting the underlying behavior of nonlinear phenomena relating to the nature of the real world. In this paper, we discuss analytic solutions of fractional-order nonlinear Schrodinger types equations such as the space-time fractional nonlinear Schrodinger equation and the (2+1)-dimensional time-fractional Schrodinger equation. The considered equations are converted into ordinary differential equations with the help of wave variable transformation and then the recently established rational ( )-expansion method is employed to construct the exact solutions. The obtained solutions have appeared in the forms of a trigonometric function, hyperbolic function, and rational function which are compared with those of literature and claimed to be different. The graphical representations of the solutions are finally brought out for their physical appearances. The applied method is seemed to be efficient, concise, and productive which might be used for further research.
Mathematics Subject Classifications: 35C08, 35R1
Strategic Growth of Everything-as-a-Service (XaaS) Business Model Transformation
Full text linkThe study presents the XaaS architecture and explores how this business model has strategically developed and how it has revolutionized the whole business industry. The study indicates how this particular business model allows companies to enhance the range of advantages of Internet Cloud model applications, particularly when it deals with process of media processing and customization of users. Its huge benefits with negligible disadvantages are mentioned as part of the presented research. This study will help to understand the services of XaaS model, challenges experiencing by it and its future opportunities. This will also assists new scholars of field to build a better understanding and better prospects regarding XaaS in future
A Note on the Tower of Hanoi Problem with Evildoers
Full text linkRecent literature considers the variant of the classical Tower of Hanoi problem with n (³ 1) discs, where r (1 £ r < n) discs are evildoers, each of which can be placed directly on top of a smaller disc any number of times. Letting E(n, r) be the minimum number of moves required to solve the new variant, an explicit form of E(n, r) is available which depends on a positive integer constant N. This study investigates the properties of N.
 
Hierarchical Structuring of Fibrous Materials: Toward Tailored Properties for High-Performance Textile Products
Full text linkThis study aims to determine whether fiber materials with hierarchical organization can improve the qualities of textile goods for high-performance uses. The primary goals are to examine functionalization strategies, identify future possibilities in textile engineering, investigate hierarchical structuring methodologies, and analyze hierarchically structured textiles\u27 mechanical and thermal properties. An extensive examination of extant literature and secondary data sources is carried out to examine the approaches, uses, and difficulties related to hierarchical organization. Important discoveries emphasize how hierarchically organized fabrics have improved mechanical strength, thermal behavior, and functional qualities. The discussion of policy implications follows, focusing on the role of industry cooperation, workforce development, sustainability programs, research funding, and industrial collaboration in improving hierarchically structured textiles. The study\u27s findings highlight the revolutionary potential of fibrous materials\u27 hierarchical organization for promoting sustainability and innovation in the textile sector
Quantum Computing in High Frequency Trading and Fraud Detection
Full text link‘Quantum Computing in high-frequency trading and fraud detection is an analysis of quantum computing and how it can be used by the different industries especially finance. It is an evolution of computing from the traditional computing method. Quantum computing is a process that is concentrated on creating systems and technology based on quantum theory rules. Quantum theory describes the energy on atomic and subatomic levels. Quantum computing uses quantum bits (qubits) which are more advanced than the traditional bits used by traditional computers. This article focuses on deploying quantum computers in solving problems that cannot be efficiently solved using traditional computers. In the finance sector, such as banking, insurance, and high-frequency trading, quantum computers can help optimize service by providing targeting and predictive analytics to reduce risk, provide personalized customer service, and provide the needed security framework against fraud
Network Security Framework, Techniques, and Design for Hybrid Cloud
Full text linkNetwork security is a framework that deals with issuing procedures and policies that will be used to establish and maintain security protocols in a particular organization. The functions related to the network security framework are oriented toward insulating the specific organization from external cyber threats and adversaries. On the other hand, a hybrid cloud is a type of cloud whose function is to allow the running and operating of different applications in various and different environments. The primary technique associated with developing hybrid clouds is the conjunctions between private and public clouds that will allow application portability and management for better and more efficient working of the clouds.