Spectrum of engineering sciences

A Study on the Detection and Prevention of Cyber Attacks using Machine Learning Algorithms

2024-12-22T17:13:22+02:00

This study explores the use of machine learning algorithms to detect and prevent cyber attacks. The research focuses on several widely used models, including Decision Trees, Support Vector Machines (SVM), Random Forests, and Neural Networks, evaluating their performance on datasets related to network traffic, intrusion detection, and malware classification. Preprocessing techniques such as data cleaning, feature selection, and balancing were applied to optimize the datasets for model training. The results show that Neural Networks outperformed the other algorithms in terms of accuracy, precision, recall, and F1-score, followed by Random Forests. This study highlights the importance of machine learning in cyber security, demonstrating its potential to detect complex attack patterns and improve real-time threat detection systems.

Keywords: Machine, learning, algorithms, cyber, attacks, Decision Trees, Support Vector Machines (SVM), Random Forests, Neural Networks.

Comparative Analysis of State-Space Least Mean Square Technique with Variable Step-Size Algorithms for Noise and Disturbance Resilience

2024-12-16T06:45:07+02:00

The state-space least mean square technique with variable step-size algorithms. SSLMS assimilates time-varying behavior because of the disruptions from the surrounding environment. Consequently, in comparison to conventional LMS, SSLMS presents an enhanced performance compared entirely dependent on external noise, varying behavior of measured signals and transient disturbances. The step size plays a prominent role to minimize the error. Due to the absence of uncertainties, it is pretty typical to propose a feasible step-size parameter. To overcome the challenge, we applied three different variable step size algorithms resulting in modified step size and provides minimum mean square error compared to fixed step size SSLMS.

Keywords: State-Space Least Mean Square (SSLMS), Variable Step-Size Algorithms, Transient Disturbances, Mean Square Error Minimization, Time-Varying Behavior

Analyzing Deep Reinforcement Learning for Robotics Control

2024-12-22T18:03:43+02:00

This research analyzes the application of Deep Reinforcement Learning (DRL) for robotics control, focusing on its potential to enhance the autonomy and efficiency of robotic systems. DRL, a powerful machine learning technique combining reinforcement learning with deep neural networks, allows robots to learn optimal control policies through interaction with their environment. This study aims to evaluate the effectiveness of DRL in various robotic control tasks, such as manipulation, navigation, and task execution. The research methodology involves developing and testing DRL algorithms on simulated robotic environments, using widely recognized frameworks such as OpenAI Gym and RoboSumo. The robots are trained to perform tasks by receiving feedback from their actions, which reinforces learning based on rewards and penalties. Data analysis involves comparing the performance of DRL models with traditional control methods, evaluating metrics such as task completion time, energy efficiency, and adaptability to dynamic environments. Results show that DRL-based systems significantly outperform conventional methods in complex, high-dimensional tasks, though challenges such as computational cost, reward shaping, and sample inefficiency remain. The study concludes that DRL has the potential to revolutionize robotics control, although further refinement of algorithms and resources is necessary to ensure their practical deployment in real-world applications.

Keywords: Deep Reinforcement Learning, robotics control, autonomous systems, machine learning, reinforcement learning, task execution, algorithm performance, robotic manipulation.

Systematic Literature Review On Swarms Of Uavs

2024-12-19T07:42:03+02:00

The study on Unmanned Aerial Vehicle (UAV) swarms, including scholarly studies, conference proceedings, and industry reports, is thoroughly examined in this survey of the literature. Swarm coordination algorithms, communication protocols, and applications in fields like firefighting and disaster management are important areas of interest. The review emphasizes new trends, identifies implementation-related issues, and makes suggestions for further study. The usefulness, constraints, and public perception of UAV swarms which come in a variety of configurations and operating capabilities are investigated. Academic populations' experimental surveys highlight the significance and promise of these systems. Additionally, the paper suggests a swarm design that uses cellular mobile networks to improve dependability and autonomy. More autonomous systems with reliable UAV-to-UAV coordination and communication are needed to advance UAV swarms. Notwithstanding notable advancements, there are still unanswered questions in certain areas that need further research to fully realize the promise of UAV swarm technology.

Keywords: Unmanned Aerial Vehicles (UAVs), Swarms of UAVs, Multi-UAV System, Applications of UAVs

Retrofitting Strategies for Enhancing Wind Resilience of Minor Structures: A Numerical Investigation

2024-12-13T09:10:36+02:00

The collapse of a tensile fabric parking shelter with an arched roof under severe wind loads is numerically analyzed in this study. Under simulated wind conditions, the project assesses the structural reactions and pinpoints the crucial failure sites using LS-DYNA's sophisticated finite element analysis capabilities. To create an accurate simulation model, the inquiry starts with a thorough site inspection, during which material samples and structural dimensions are gathered. The work effectively compares a comprehensive finite element model to the actual failure using LS-DYNA, which captures the intricate behaviors of the structural components under wind loads. Using this analysis, we pinpoint essential design flaws in the parking structure and suggest two repair plans to strengthen structural resistance against comparable wind-driven collapses. Additionally, the project creatively compares uplift on several canopy designs using wind tunnel testing, recommending the best design to reduce wind-induced damages. By improving our knowledge of wind dynamics and structural reaction, the investigation's conclusions not only shed light on the parking shed's particular collapse process but also advance the civil engineering discipline.

Keywords: LS-DYNA, Arched Roof, material model validation, benchmarking, retrofits, uplift, wind tunnel

Acoustic Investigation of Sindhi Consonants using Waveform and Spectrogram Techniques

2024-12-25T20:20:13+02:00

Sindhi language has many accents and also contains a large variety of vowels, consonants and phonemes which can be extended at any place. The description and analysis of prosodic features are compulsory for various Speech Processing (SP) Applications. The accuracy of SP applications is not accurate due to the indistinct uniformity in consonants, which make similar sounds. In this paper, two of the prospective parameters of prosody; duration and pitch are used for extraction of features from the recorded sounds of both male and female speakers. To study these phonemes, twelve most frequently used consonant-phons are selected and distributed into six groups of experiments. The recorded sounds are subjected to a methodology that is also described and then applied in this work. The PRAAT speech analyzer tool is used to obtain the results of sound duration and pitch parameters. This study revealed that the sound duration and pitch of the female speakers are higher than the male speakers. The discussed pitch values proved that some of the identified consonants produce similar sounds and that others are quite different. The outcomes of this study will be of great benefit in the advancement of Sindhi SP applications

Combined Navigation Aids In Single User Virtual Environments: A Performance Study

2024-12-11T10:25:41+02:00

Single User Virtual Environments (SUVEs) employ a range of interaction techniques tailored to specific tasks and applications. The variability in user needs and system requirements has prevented the emergence of a universally effective solution. Effective work in SUVEs often relies on communication channels that enable users to share task goals, manage task decomposition, and monitor progress. In this paper, we evaluate the impact of six combinations of navigation aids: 3DML + Arrows-Casting, 3DML + Audio, Audio + Arrows-Casting, Textual + Arrows-Casting, Audio + textual and 3DML + Textual. The assembly task in SUVE is guided by combined navigation aids and using the shortest path selection algorithm (Dijkstra's). Experiments were carried out on university students to evaluate their effectiveness on task performance in assembly activities of using six navigational aids. Our experiment involved 20 individuals. Findings show that the combination of audio + arrows-casting resulted in the most efficient task completion, while 3DML and textual aids support led to slower navigation performance. This study contributes to understanding the effectiveness of different navigation aids for enhanced user performance in SUVE in assembly tasks accomplishment.

Keywords: 3D virtual environments (VEs), combined navigational aids, Virtual reality (VR), Single User virtual environments (SUVEs), Navigational aids, Three dimensional map with liner (3DML) ·

5G and Advance Network Architecture Considering Challenges, Solutions and Future Perspectives: A Survey

2024-12-11T10:00:21+02:00

In recent decades, the two most crucial and intelligent breakthroughs of IT in recent decades have been the categories represented by mobile and wireless communications, plus the Internet. These technologies are not only grown up fast but also make a revolution in human life. The near future targets of the goal of the higher capacity and high data rate Services beyond 4G, lower transmission delay, and better service performance. To accomplish such network objectives above, radical architectural changes of cellular networks are required the aim is to shed some light on what 5G is about: what are the building blocks of core 5G system concept, what the main challenges are and how to tackle them. This paper presents findings from a comprehensive study on fifth-generation (5G) cellular network architecture and emerging technologies that can enhance it to meet user demands. The primary focus of this extensive review is on 5G cellular network design, massive multiple input multiple output technologies, and device-to-device communication (D2D).

Keywords: 5G Architecture, Mobile and Wireless Communications, Cellular Network Design, Massive MIMO (Multiple Input Multiple Output), Device-to-Device Communication (D2D), Ultra-Dense Networks, Full-Duplex Radios, Millimeter Wave Solutions, Cloud Technologies, Software-Defined Networks (SDN), 5G Radio Access Networks (RAN), Internet of Things (IoT), Network Clouds, Enhanced Capacity, Reduced Latency, High Data Rates

An Enhanced Approach of Exploring Digital Economy Using Modern Computer Networks

2024-12-11T10:13:35+02:00

With globalization driving complex between state exchange labor and products, challenges have arisen around burdening Worldwide Undertakings (MNEs) in an undeniably computerized economy. This report inspects current two-sided charge arrangements between South Center Part States and countries where most computerized MNEs are settled, zeroing in on MNEs with yearly incomes surpassing EUR 750 million. The investigation gives key information on the source burdening freedoms of South Center Part States, investigating the effect on settlement exchanges and suggesting the reception of Article 12B from the Assembled Countries (UN) Model Assessment Show to help successful advanced tax collection. Also, the review recognizes settlements with restricted source burdening freedoms that might require exhaustive renegotiation past Article 12B's consideration. The report likewise surveys how "PC Programming" is treated in these arrangements, finishing with proposals for future activity.

Keywords: Digital technologies; digital transformation; digital literacy; digitalization; big data; data analytics; artificial intelligence; blockchain; cybersecurity; challenges.

Criticality and Security Evaluation of Events: Insights for Luxury Hotel Management

2024-12-11T06:39:26+02:00

In this research paper, we present a comprehensive Business Intelligence (BI) framework tailored specifically for luxury hotels to optimize their digital marketing strategies through sentiment analysis of customer reviews. By employing advanced Machine Learning (ML) and Deep Learning (DL) methods, we aim to provide valuable insights into customer sentiments to accurately classify sentiments into positive and negative polarities. We utilize Support Vector Machine (SVM), Random Forests (RF), Naive Bayes classifier (NB), and long short-term memory networks (LSTM) networks to effectively classify insights derived from hotel reviews. To begin, we initially perform data acquisition, followed by the identification of implicit and explicit features, and finally, sentiment classification. To evaluate the performance of our approach, we measure precision, recall, True Positive Rate (TPR), False Positive Rate (FPR), loss, and validation accuracy. Substantially, we conduct a comprehensive comparison of different regularization and optimization methods. Our proposed framework demonstrates exceptional accuracy, particularly on the LSTM network, when compared to SVM, RF, and NB classifiers. This outstanding accuracy establishes the superiority of our approach in effectively categorizing hotel review sentiments.

Index Terms: Sentiment Analysis, security evaluation Review Classification, Luxury hotel Reviews, Smart marketing, Business intelligence, Machine Learning.

Exploring the Impact of Advance 5G Connectivity Revolutionizing Technology: A Survey

2024-12-09T05:42:55+02:00

The arrival of 5G technology represents a major advancement in connectivity, revolutionizing how people and businesses engage with digital platforms. This paper delves into the various impacts of 5G, highlighting its exceptional speed, minimal latency, and extensive device connectivity. Through its contributions to areas such as autonomous vehicles, smart cities, healthcare, and the Internet of Things (IoT), 5G acts as a catalyst for fostering innovation and enhancing efficiency. The research emphasizes the transformative impact of 5G on communication, the promotion of economic growth, and the reshaping of global digital environments. The examination also covers challenges like infrastructure deployment, cybersecurity risks, and policy implications. In conclusion, this paper highlights the potential of 5G to reshape technological advancement and its crucial role in shaping a connected future.

Keywords- Exploring the Impact of Advance 5G Connectivity Revolutionizing Technology: A Survey

A Survey of Software-Defined Networks Based on Advance Machine Learning Based Techniques

2024-12-09T05:53:02+02:00

Presently, networks have opportunities to build systems with improved and intelligent solutions which can be easily tailored for different users. In conventional networking, the software defined networking (SDN) work separately for its control plane and data plane, which makes it better in managing, more secure and affordable. Since ML is the most fundamental branch of AI when amalgamated with SDN it will provide an efficiency and effectiveness in the management of resources such as bandwidth mapping, flow control, error control and security on the network. In this paper you will find the use of network alongside ML implemented on SDN concepts in two ways. First it describes how appropriate ML algorithms are integrated with SDN based networks following assessment. On the other hand, it provides reasonable recommendations for various network applications based on SDN. And toward the end, it discusses the extra development needed for Machine Learning (ML) algorithms and SDN concepts. The common point of AI, Big data, computer networking and similar fields is discussed in this paper. Researchers from different professions and ages have their findings with regards to AI for various uses since it is a young and intricate area. Thus, this paper will assist these researchers to point out these main faults more accurately.

Keywords: Artificial intelligence, machine learning, network management, software-defined networking.

A Survey on Tor’s Multi Layer Architecture and Web Implications in Dark Web

2024-12-09T06:00:40+02:00

The Tor network is a fundamental element of the dark web that provides users with secure and shadowy access to data and communication. Originally tor was developed for privacy and protection purposes but later it turned into a platform where both legal and illegal activities are done. The study looks into the architectural components of Tor, onion routing, encryption layer, and node structure that facilitate obscurity. Moreover, it explores both the positive and negative sides of the network, examining how its architecture works and supports illegal activities. Law enforcement investigates the activities, highlighting the challenges in balancing the benefits of the network against the need to tackle security risks. By reviewing recent research, technical advancement, and structural study, this paper presents a deep understanding of the Tor network in enhancing privacy, security, and unidentified communication. Also highlighting the related risks, challenges, and ethical considerations

Keywords: Tor network, Dark web, onion routing, privacy protection, law enforcement, ethical considerations

A Detailed Review of latest Trends, Technologies Applications of Artificial Intelligence in Modern System Network

2024-12-09T06:08:50+02:00

The application of artificial intelligence (AI), a cutting-edge technology that emulates human intelligence and learning abilities, holds immense potential for advancing computer networks. This article aims to explore AI-based applications in computer networks, emphasizing their role in improving network security and performance. The study highlights the innovations and advancements brought by AI to network technology by examining its applications in areas such as network operations and maintenance, intelligent security systems, intrusion detection, and performance optimization. Additionally, the article provides key recommendations to promote the seamless integration of AI into computer networks, introducing new ideas and strategies for developing secure and efficient network infrastructures.

Keywords- Review of latest Trends, Technologies Applications, Artificial Intelligence & Modern System Network

Research Trends In Deep Learning and Machine Learning for Cloud Computing Security

2024-12-09T06:55:39+02:00

Meanwhile, security concerns of cloud computing services have grown more diverse and pressurizing with evolution of existing cloud services known to overtake traditional security solutions. Consequently, this paper aims at identifying the contribution of the two most crucial advanced technologies known as machine learning and deep learning in strengthening cloud security. These are techniques that use Artificial Intelligence to detect threats and respond to, or prevent them, automating the identification of abnormalities in cloud traffic. This study focuses on discussing how ML and DL work and their uses, such as fraud detection, real-time authentication in the Zero Trust models, and building security into an application at the software development stage. In addition, it covers important aspects that were previously omitted in other surveys of DL, including privacy, and describes important methods for DL privacy, including federated learning and homomorphic encryption. The paper also explores the issue of model interpretability before highlighting the need for explainable AI frameworks in order to confidence the security administrators. Besides, it explores the threat of adversarial attacks against ML models and also presents a guide to improving model resilience. In conclusion, it is recommended that continuous research should be conducted, and cooperation between researches, practitioners, and policy makers establish to come up with effective and dynamic secure cloud solutions.

Keywords: Machine Learning, Deep Learning, Cloud Computing, Software Development, AI framework, Cloud traffic, Cloud security

Enhancing Supplier Capability Through Waste Minimization: A Quality Matrix Perspective

2024-12-06T14:43:04+02:00

In the packaging sector, even minor deviations in product specifications can lead to increased waste, operational inefficiencies, and compromised product quality. This research focuses on addressing critical manufacturing challenges faced by Company A in maintaining ovality standards for polymer cans exceeding the acceptable threshold (≤3%) or 1.2mm which is pivotal for packaging integrity. Using a combination of advanced tools like the Hoshin Kanri X-Matrix methodology for strategic alignment and integrates tools like root cause analysis (Ishikawa), Pareto analysis, and experimental validation to identify key factors influencing deformation, including load, packaging, and temperature variations. The samples were analyzed, controlled experiments and correlation analyses confirmed that load management significantly reduces deformation, while temperature has minimal impact. By implementing improved packaging designs, including the use of 5-ply separators, floor rejections were reduced from 5% to 1.1%, reduced man-hours by 50%, yielding financial savings of 8.76 MN PKR annually and enhancing operational efficiency.

Keywords: Continuous improvement, Defect reduction, Lean manufacturing, Load management, Packaging quality, Supplier capability, Waste minimization.

SD Network based on Machine Learning: An Overview of Applications and Solutions

2024-12-04T16:04:38+02:00

The integration of Software-Defined Networking (SDN) and Machine Learning (ML) provides a promising framework for creating adaptive, secure, and responsive networks. This method allows for resource allocation, traffic routing, and security optimization by fusing the centralized control structure of SDN with the data-driven insights of machine learning. This review assesses important studies in SDN-ML applications, emphasizing both important contributions and noteworthy drawbacks, such as limited experimental validation, scalability, and problems with data quality. Future research should investigate sophisticated machine learning techniques, provide scalable frameworks, and improve dataset quality in order to tackle these issues. This study demonstrates how SDN-ML integration may be used to build network environments that are secure, intelligent, and responsive.

Keywords- SD Network based, Machine Learning, Applications and Solutions

A Survey on Enhanced Approaches for Cyber Security Challenges Based on Deep Fake Technology in Computing Networks

2024-12-04T16:13:32+02:00

With the help of artificial intelligence methods like as generative adversarial networks (GANs), deepfake technology has developed to produce incredibly lifelike but fraudulent audio, video, and image content. This technology presents significant cybersecurity risks, including as identity theft, social engineering, and public opinion manipulation, even while it has uses in creative media and entertainment. This study explores public awareness and views of deepfake concerns, focusing on people's attitudes toward potential remedies and their level of knowledge regarding potential misuse. An online poll was included in the mixed-methods approach to collect quantitative information on public awareness, perceived risks, and views on technical or regulatory actions. The findings indicate that respondents are generally aware of deepfakes and are particularly concerned about the threats to their privacy, their confidence in digital information, and the possibility of malevolent usage in corporate or political contexts. Most participants support proactive measures to lessen these risks, such as public awareness campaigns, improved regulatory frameworks, and the creation of detecting technology. Findings show that in order to defend against deepfake dangers, lawmakers, software developers, and cybersecurity experts must act quickly to coordinate their reaction. This study adds to the expanding corpus of research on the effects of deepfakes by highlighting the significance of multi-stakeholder cooperation and educated public engagement in addressing this changing cybersecurity threat.

Keywords: Cybersecurity, Deepfake Technology, Generative Adversarial Networks (GANs), Artificial Intelligence (AI), Digital Media, Identity Theft, Social Engineering, National Security, Misinformation, Detection Algorithms, Public Awareness, Mitigation Strategies, Fraud Political.

Investigating the Most Effective AI/ML-Based Strategies for Predictive Network Maintenance to Minimize Downtime and Enhance Service Reliability

2024-12-04T16:22:45+02:00

The increasing complexity of modern network infrastructures presents significant challenges in maintaining performance and service reliability. Traditional reactive maintenance approaches, which rely on manual troubleshooting and scheduled checks, are often insufficient to prevent unplanned downtime, resulting in financial losses and decreased customer satisfaction. This research proposal explores the most effective AI/ML-based strategies for predictive network maintenance to minimize downtime and enhance service reliability. By leveraging advanced data-driven models, AI and ML technologies can forecast network failures, detect anomalies, and optimize resource allocation, enabling proactive management of network operations. The primary objectives of this study are to identify the most effective AI/ML algorithms, develop predictive models capable of real-time failure forecasting, and assess the impact of these strategies on network performance. The research will evaluate various algorithms, including time-series forecasting (LSTM, ARIMA), supervised learning (Random Forest, SVM), and unsupervised learning models for anomaly detection. By combining historical network data analysis with simulations, this study aims to build a scalable framework for predictive network maintenance. The findings are expected to provide actionable insights, guiding organizations in adopting AI-driven network automation solutions to enhance operational efficiency, reduce costs, and improve network resilience, ultimately supporting the growing demand for reliable digital connectivity across multiple sectors.

Keywords: Predictive Network Maintenance, Artificial Intelligence (AI), Machine Learning (ML), Network Downtime, Service Reliability, Network Automation, Anomaly Detection, Time-Series Forecasting, AI/ML Algorithms, Proactive Network Management, Network Resilience, Data-Driven Insights, Network Optimization, Operational Efficiency

A Survey on Latest Trends and Technologies of Computer Systems Network

2024-12-04T16:35:32+02:00

Zero trust (ZT) represents a set of evolving cybersecurity principles that shift defense strategies from fixed, network-centered perimeters to a focus on users, assets, and resources. A zero trust architecture (ZTA) applies these principles to design industrial and enterprise infrastructure and workflows. Zero trust operates on the idea that no implicit trust is granted to any asset or user account based solely on physical or network location (e.g., a local network versus the internet) or asset ownership (whether enterprise-owned or personal). Both authentication and authorization (of the user and device) are separate steps that must be completed before access to an enterprise resource is allowed. Zero trust is a response to modern network trends such as remote work, bring your own device (BYOD) practices, and the use of cloud-based resources outside an organization’s direct network boundary. Rather than focusing on network segments, zero trust prioritizes securing resources—like assets, services, workflows, and user accounts—because network location alone is no longer considered a main factor in assessing the security posture of a resource. This document provides an outline of zero trust architecture (ZTA), along with general deployment models and use cases where zero trust can enhance an organization’s overall IT security posture.

Keywords: Architecture; Cybersecurity; Enterprise; Network Security; Zero Trust.

Internet of Things (IoT's) in Agriculture: Unexplored Opportunities in Cross – Platform

2024-11-30T06:51:49+02:00

Through precision agriculture, IoT has taken it to the real-time observation and optimization of resources; moreover, the improved productivity within that particular area. Although with numerous developments in the respective areas, most of the agricultural IoT systems lack integration from cross-platform, giving a fragmentation effect on the data and very limited inter- device interoperability between them of different manufacturers. Cross-platform IoT integration in the area of agriculture is also examined here to discuss what seamless data exchange, strong decision-support systems, and even improved automation can gain in agriculture. This study shall review case studies and analyses of technical details to study the existing challenges in areas of standardization, security, and scalability. Its findings show that a singular platform for IoT devices in agriculture can transform agricultural systems, including better resource utilizations, automation, and transparency in the supply chain. Therefore, most probably, cross- platform IoT could overcome such integration barriers and create a more sustainable, connected, and intelligent agricultural ecosystem.

Keywords: Internet of Things (IoT), Precision agriculture, Cross-platform integration, Data interoperability, Agricultural automation, IoT standardization, Sustainable farming, Decision support systems (DSS), Smart farming technology, Agricultural data security

Strategic Planning for Sustainable Energy Towards Greener Future: Geospatial Analysis of Hydroelectric Power Generation Sites in Pakistan

2024-12-01T04:31:24+02:00

The unexploited possibilities of hydropower energy in Khyber Pakhtunkhwa (KPK) province, Pakistan, pose a considerable challenge to advancing sustainable energy solutions. This study performs an initial feasibility evaluation to determine appropriate sites for runoff river hydropower initiatives in KPK. The study uses sophisticated geospatial tools like Geographic Information Systems (GIS), remote sensing, and satellite imagery data to pinpoint locations where adequate head and discharge are present. The findings aid in pinpointing potential locations for hydropower development, offering essential insights into sustainable energy alternatives for the region's advancement.

Keywords: Hydroelectric power generation, runoff river hydropower projects, GIS, remote sensing, satellite imagery, geospatial analysis.

An Enhanced Cost Effective and Scalable Network Architecture for Data Centers

2024-11-30T07:05:13+02:00

Large-scale data centers, comprises tens of thousands of computers, require significant total of bandwidth. The network infrastructure typically adopts a hierarchical tree configuration of routing and switching components, with higher levels featuring increasingly specialized and expensive equipment. Despite the implementation of advanced IP switches and routers, the resulting setups often utilize only 50% of the total bandwidth available at the network edge, while still incurring significant costs. Its modular, scalable design allows expansion with minimal disruption or cost, supporting big data, AI, and other demanding applications. In this architecture balances efficiency, scalability, and compatibility, making it a future-ready, low-cost choice for data centers modernizing for the digital era and expanding data-driven services.

Keywords: Data Center, Network, Network Architecture, Scalable Network Architecture, Data, Cost Effective.