New Review of Information Networking

JiaXing, He; YiYan, Wang; XiaoNing, Lu; WenYuan, Yang; Kie-Su, Kim

doi: 10.1080/13614576.2025.2515051pmid: N/A

With the rapid advancement of digital information technology, the methods of digital preservation of cultural heritage have become increasingly varied, exhibiting more diverse application models and technical approaches. Based on literature analysis from the Web of Science core database, this study employs CiteSpace and VOSviewer to conduct a systematic knowledge graph analysis of the evolution of design technology in the field of “digital preservation of cultural heritage,” aiming to uncover the core themes, research trends and technological development trajectories in this domain. By drawing the knowledge graph of this field, this study endeavors to achieve a balance between technological progress and heritage conservation to ensure the sustainable development of cultural heritage, and offers empirical data and development recommendations to support innovation and application in this domain.

Rahman, Ziaur; Dinda, Goutam; Rahaman, Safiqur; Nausheen, Sabahat

doi: 10.1080/13614576.2025.2554576pmid: N/A

Artificial Intelligence (AI) has emerged as a transformative force in addressing developmental challenges, particularly in data mining. Despite its growing relevance, comprehensive studies analyzing AI’s contribution to data mining remain limited. This study bridges the gap by examining bibliometric trends and conceptual evolution of AI applications in data mining from 2005 to 2023. Using R (Biblioshiny), VOSviewer, and CiteSpace, 6,843 documents (including 6,552 articles) from 1,379 sources in the Web of Science database were analyzed. Keywords such as AI, data mining, machine learning, and big data guided the analysis. The study revealed 14,613 authored works, with 24.1% involving international collaboration. English-language publications dominated (6,723 total publications, 104,184 total citations). Articles accounted for the majority, with an h-index of 119. The most-cited article, from Expert Systems with Applications, garnered 7,669 citations. Professor Lin JCW of Shenzhen University was the most prolific author (81 publications, 1,994 citations), while Iran’s Islamic Azad University led with 120 publications. China ranked first regionally (1,891 publications), followed by India (878). The findings highlight AI’s significant role in advancing data mining, emphasizing global collaboration, key contributors, and evolving research trends shaping the field.

Deshpande, Padmaja M; Sharma, Raghvendra; Sinha, Swati; Kulkarni, Dattatraya Vasant

doi: 10.1080/13614576.2025.2554577pmid: N/A

Cellular service providers collect real-time location data to maintain connectivity and optimize network performance. However, this data collection raises serious privacy concerns, as users are often unaware of how extensively their movements are tracked. Therefore, the major intention of this model is to develop a Deep Kronecker Residual Forward Harmonic Network(DKResFHNet) for preserving the privacy of user location from cellular service providers. Here, three major steps are involved in location privacy preservation using a Peer-to-Peer system (P2P). Firstly, the registration process for the user is carried out. Followed by this, the anonymization process is conducted, where a dummy location is generated. Then, the level of anonymization is determined through DKResFHNet. Thereafter, the result of the tasks is returned to the Location-Based Services (LBS) server. Therefore, the location anonymization is efficiently accomplished by employing the proposed DKResFHNet, where DKResFHNet is developed by the integration of Deep Kronecker Network (DKN) and Wide Residual Network (WRN). The evaluation metrics of DKResFHNet are Anonymous Entropy, Location Privacy, and Location Preservation attained superior values of 7.174, 0.958, and 0.967, respectively. These results confirm the model’s robustness and effectiveness in safeguarding user location data while maintaining service utility.

Kadam, Vivek S.; Deore, Bhushan S.; Garg, Bhawana; Gadge, Poonam

doi: 10.1080/13614576.2025.2554583pmid: N/A

Network Intrusion Detection System (NIDS) is a security solution used to monitor and analyze the network traffic to detect the unauthorized access or malicious activity. NIDS in Internet of Things (IoT) environments face challenges due to the high dimensional traffic data, evolving attack patterns, and limitations in detecting adversarial threats. To address this problem a novel intrusion detection technique is introduced utilizing an Xception convolutional Generative Adversarial Network (Xcov-GAN) model, which integrates Xcov-Net architecture with Generative Adversarial Network (GAN). First, an adversarial attack is performed against a Kitsune-based NIDS model, within an IoT context. Following data acquisition, traffic records are extracted from the database and subjected to median normalization for consistency. Key features are identified and selected through the Jeffreys and Jensen-Shannon (JJ-Shannon) similarity metric, which combines Jeffrey’s and Jensen-Shannon measures. Finally, intrusion detection is applied utilizing the developed Xcov-GAN model. The novel Xcov-GAN demonstrates strong performance by acquiring 94.877% true positive rate (TPR), 93.777% accuracy and 92.877% true negative rate (TNR). The high detection accuracy, TPR and TPR of Xcov-GAN imply that it is practically applicable for deployment in trustworthy systems, like intelligent home environments, healthcare equipments, industrial IoT, and other connected systems which require reliable real-time threat detection.

G, Madhumala; R, Dr Suma

doi: 10.1080/13614576.2025.2554585pmid: N/A

Cloud services are adapted to several problems of real-time computing. Users access diverse cloud services to execute numerous tasks. Additionally, it is essential to consider diverse quality of service (QoS) measures to enhance service performance. The impact of blockchain approaches helps to improve QoS metrics. In this work, the blockchain-based deep pyramidal residual fractional maxout network (Blockchain_DPRFMN) is devised for workflow scheduling in cloud computing. First, a cloud with a blockchain system model is simulated and then workflow scheduling is performed based upon multi-objective parameters such as virtual machine (VM) parameters and task parameters. Central processing unit (CPU) and bandwidth utilization are the VM parameters; whereas, task parameters include task priority, earliest finish time, task length, earliest start time, actual task running time, predicted energy, trust, makespan equivalent of total cost, memory capacity, reliability, and resource utilization. The deep feedforward network (DFFN) is utilized to predict energy. Finally, task scheduling is accomplished by Blockchain_DPRFMN, which is designed by integrating deep pyramidal residual network (PyramidNet) and deep maxout network (DMN) with fractional calculus (FC). Moreover, Blockchain_DPRFMN has attained a maximal residual energy of 0.769J, a minimal makespan of 0.580, resource utilization of 0.627, and latency of 0.485 sec.

Schellnack-Kelly, Isabel

doi: 10.1080/13614576.2025.2570662pmid: N/A

The increasing complexity, volume and diversity of information handled by contemporary organizations pose ongoing challenges for efficient, compliant records management. Central to this is the design and maintenance of robust file plans that are structured classification schemes reflecting organizational functions, supporting information retrieval and ensuring regulatory compliance. In South Africa’s public sector, file plan development remains largely manual, labor-intensive and inconsistently applied. This results in inefficiencies, misalignment with business processes and heightened risks of noncompliance with legislation such as Promotion of Access to Information Act, Protection of Personal Information Act and the National Archives and Records Service Act. This paper examines how artificial intelligence (AI) can enhance the creation and upkeep of dynamic, context-sensitive and functionally aligned file plans. It explores the potential of machine learning, natural language processing and automated classification to analyze organizational structures, identify record categories, detect retention requirements and generate adaptable file plan hierarchies. The study also considers how AI can integrate with existing electronic document and records management systems (EDRMS) to improve consistency, reduce administrative burdens and strengthen records lifecycle management. Taking the South African context into account, the paper discusses the constraints posed by legacy systems, skills gaps and resource limitations, alongside opportunities for innovation. It contrasts the compliance-heavy environment of the public sector with the more flexible but varied practices of the private sector. The paper proposes a practical framework for piloting AI-assisted file plans. Recommendations include functional mapping, training domain-specific models and maintaining human oversight to ensure compliance and accountability. Ultimately, the paper argues that, if implemented responsibly, AI offers a viable path to modernizing records management in South Africa, by transforming static file plans into dynamic tools that adapt to organizational change and digital transformation.

Bok, Stephen; Shum, James; Lee, Maria

doi: 10.1080/13614576.2025.2575260pmid: N/A

Polychronicity is the preference in performing multiple tasks at a given time (e.g. texting while listening). Polychronicity is a belief that drives a behavioral practice of multitasking. Unfortunately, phone use while socializing with others can undermine relationships. Conservation of resource (COR) theory explains individuals multitask to accumulate resources for later use. While upfront effort may be greater, in the long-run time, effort, and social resources are saved. Path analysis (N = 809) showed polychronicity is associated to increased phubbing others (i.e. phone snubbing). Polychronicity is associated to decreased personal wellbeing (mediator). Personal wellbeing is associated to decreased phubbing others. Counterintuitively, high technological adaptivity (moderator) and polychronicity are associated to increased personal wellbeing. Despite common use of mobile devices in the digital age that can negatively impact in-person interactions, a polychronic individual can improve personal wellbeing with a career-oriented use of technology. This can reduce the desire to use technology in the presence of others because of satisfactory grounding in their career, health, relationships, and safety needs. Interactions with people are a time when these individuals choose not to multitask with digital devices.

Vats, Gaurav; Tanwar, Sarvesh; Sharma, Pankaj Kumar

doi: 10.1080/13614576.2025.2582130pmid: N/A

Internet of Things (IoT) is among one of the most promising and interactive technologies in today’s era. it refers to a network of physical electronic tools, devices, and other objects that are embedded with sensors, software, and network connectivity, allowing them to collect and exchange information. IoT-enabled systems are employed in different area such as transports, healthcare energy, and smart homes. All these sectors are for the benefit and comfort of society. However, the increasing adoption of IoT leads to exposure of basic infrastructure to the Internet, resulting in cybersecurity risk. Various algorithms and frameworks have been proposed to cope with this. This research investigates the frameworks and techniques designed to detect and avoid attacks in IoT-enabled systems. It also covers a comprehensive review of the existing literature, focusing on various security vulnerabilities and attacks that have emerged in the context of IoT environments. In that context the study finds there is a clear trade off in framework based on machine learning (ML). ML has powerful anomaly detection capability but it also requires a large labeled dataset. Conversely, solutions based on blockchain technology provide trust and improves security and integrity with its decentralized property; however, it also has problems such as latency, a high energy requirement, and scalability issues. In the same context, signature/anomaly-based systems remain effective only for known threats; it needs frequent updates and struggles with zero-day attacks. Case studies show that these frameworks and tools are not completely sufficient to prevent cyber-attacks such as recent Trojans and Distributed Denial of Service (DDOS). The study finds necessity for adaptive, domain specific frameworks that can provide security with balance accuracy, resource constraints, and timely response. The novelty of this work lies in presenting a deep study of ML-based, blockchain-enabled, and signature/anomaly detection frameworks for IoT security, which is an approach that goes beyond single technique studies, along with bibliometric analysis of IoT security.

G, Narendrababu Reddy; P, Aswani; S, Phani Kumar

doi: 10.1080/13614576.2025.2582784pmid: N/A

Cloud computing enables users to run applications, manage data, and perform computations online without relying on local software. Key challenges in load balancing include scalability, traffic management, fault tolerance, latency reduction, and efficient resource allocation. This work introduces a novel strategy, Spider Wasp Fossa Optimization (SWFO), to enhance Virtual Machine (VM) migration and improve load balancing in cloud environments. Initially, task allocation across VMs is handled using a Round-Robin scheduling mechanism. To anticipate potential overloads, the system employs a Dilated Convolutional Neural Network (Dilated CNN) to forecast the resource usage of each Physical Machine (PM). When the predicted load surpasses a predefined threshold, the system initiates VM migration relocating VMs from heavily burdened PMs to those with available capacity. This decision-making process takes into account multiple factors including load, energy consumption, make span, CPU utilization, capacity, and migration cost. The SWFO algorithm orchestrates this migration process, ensuring optimal resource distribution and improved operational performance. SWFO is obtained by integrating Spider Wasp Optimizer (SWO) and Fossa Optimization Algorithm (FOA). Additionally, SWFO is evaluated, and it has achieved superior values for energy consumption, load prediction, and migration cost as 0.071 J, 0.024, 0.119, respectively.

AlGhannama, Bareeq; Alsaber, Ahmad; Alkandari, Anwaar; Anbar, Amal

doi: 10.1080/13614576.2025.2588758pmid: N/A

The rapid pace of digitization has increased the need for a comprehensive understanding of digital literacy, encompassing the skills required to navigate, critically evaluate, and use digital technology, such as software systems and applications, including artificial intelligence. However, existing tools lack the cultural adaptability in assessing both operational and critical digital skills, particularly in nonnative English-speaking educational contexts. This quantitative study addresses this gap by developing the culturally responsive Comprehensive Digital Literacy Scale (CDLS), designed to measure the skills necessary for employing digital technologies in education. The tool is a five-point Likert-scale questionnaire with 15 items administered in English. The sample consisted of 267 students from various private tertiary institutions in Kuwait, where English was dominant language of teaching. Evaluation of the tool followed a rigorous psychometric process, with a Cronbach’s alpha of 0.927. Principal Component Analysis (PCA) was employed to uncover the underlying structure of the digital literacy construct and identify two distinct dimensions: critical digital literacy and basic operational digital skills. Furthermore, CDLS was validated through Confirmatory Factor Analysis (CFA), with model fit indices indicating a satisfactory representation of the data. By integrating both critical and functional dimensions of digital literacy within a culturally responsive framework, the CDLS advances existing measures and provides a validated tool applicable across diverse educational contexts. Practically, the CDLS offers educators, policymakers, and software designers a reliable tool for evaluating and enhancing digital literacy to promote effective digital engagement.