Spectrum of engineering sciences

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

Shah Khalid — 2024-12-11

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

Hasheem Ahmed — 2024-11-28

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

Ghufran Ali — 2024-11-28

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

Asadullah Kehar — 2024-11-29

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

Ayesha Urooj — 2024-12-09

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

Fazeel Mahmood — 2024-12-09

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

Noman Aqeel — 2024-12-09

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

Saud Ismaeel — 2024-11-12

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

Shoaib — 2024-11-20

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

Zainab Bibi — 2024-12-06

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

Abdul Rafay — 2024-11-16

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

Jawad Ahmad — 2024-12-04

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

Muhammad Waleed Khawar — 2024-12-04

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

Shehreyar Nawaz — 2024-11-14

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

Muhammad Abdullah — 2024-11-13

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

Saad Zaheer1 — 2024-11-11

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

Hafiz M. Saqlain Khan — 2024-11-09

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.