Communication Technologies Research Articles

Underground Cable Fault Detection

Underground cables play a crucial role in delivering electricity, ensuring a dependable power supply in both urban and industrial regions. However, detecting and resolving faults in these cables can be difficult due to their hidden placement. This project aims to develop an effective underground cable fault detection system that accurately identifies fault locations, reducing downtime. The system will combine electrical principles with modern sensing technologies to detect faults such as short circuits, open circuits, and insulation issues. By injecting a small current into the cable and analyzing the resulting voltage drop, the system will calculate the distance to the fault from the measurement point. Using a microcontroller along with sensors and signal processing units, it will determine and display the fault location in kilometers. Real-time fault detection will significantly decrease maintenance time and costs by quickly locating problem areas, enabling faster repairs and reducing power outages. This project aims to improve the efficiency and reliability of power transmission systems. Additionally, the system can be integrated with communication technologies for remote monitoring, allowing utility companies to detect and manage faults from centralized control centers.

A two-tier database architecture framework for combating cyber threats

The 21st century has witnessed a lot of advancements in science and technology. One may not be wrong to assert that the field of Information and Communications Technology (ICT) and its allied disciplines like Artificial Intelligence (AI) and Nanotechnology have taken the lead as we witness new developments in these areas year by year. The products and services we utilize and enjoy are evidence of the usefulness of this advancement in science and technology. It is worthy of note that the interest of many (both good and bad in the discipline of ICT), is being ignited by the fact that ICT finds its applicability virtually in every area of human endeavor. In this study, we propose a two tier database architecture that employs an Object-Oriented Analysis Design Methodology (OOADM) using the Unified Modelling Language (UML). The choice of this methodology is because we need to present every stage of the study from analysis to the design phases. With the help of the chosen methodology, we analyzed the existing frameworks, identified the weaknesses, and as a result, we further surveyed algorithms used for reliable data security. From the results obtained from the survey, we employed the SHA-256 algorithm because of its strength in data security, and the Argon2 hashing algorithm which is one of the best algorithms for securing database credentials. In the end, we provide an enhanced framework made up of the chosen algorithms and a two-dimensional database (2-DDb), which could be implemented to effectively combat attacks on important databases.

Exploring the mediating role of brand trust in the impact of digital marketing and online reviews on purchase decisions for beauty products: A case study on students in solo

The rapid development of information and communication technology has significantly altered the way consumers interact with brands. In the digital era, digital marketing has emerged as a primary strategy for companies to reach consumers and promote their products. The beauty industry, in particular, has undergone a major transformation, with digital marketing and online reviews playing crucial roles in influencing consumer purchasing decisions. This study focuses on exploring the mediating role of brand trust in the relationship between digital marketing, online reviews, and beauty product purchasing decisions among university students in Solo, Indonesia. The research aims to provide insights into how brand trust, as influenced by digital marketing and online reviews, impacts consumer behavior. The findings are expected to help beauty brands in enhancing their digital marketing strategies and improving brand trust among young consumers.

Gen Z and Generative AI: Shaping the Future of Learning and Creativity

Thus, the appearance of the Generative Artificial Intelligence opened up a great turn in many areas, including education and creative industries. This paper seeks to understand the deep impact that Generative AI is going to have on learning and creating processes for the social context of Generation Z (Gen Z) students – born in digital culture. The work looks into the possibilities and challenges that Gen Z in collaboration with Generative AI leads to the future of learning and creativity. This paper is relevant as it offers some understanding of the ongoing changes in the education and creativity together with the escalating growth in technology. The nature of the association between the members of Generation Z and the Generative AI needs to be known by the educational stakeholders, policymakers, and business executives to leverage value from the existing and upcoming technologies together with dealing with possible negative impacts. The purpose of this study was to explore the nature and uses of Generative AI, and its effects on the learning and creativity of Gen Z, in addition to identifying the advantages, disadvantages, opportunities, and risks/partities’ concerns that are commensurate with the integration of this technology in teaching/learning and creative processes. To achieve the objectives of the study the following research methodology was used: The research used both a literature review and documentary research. The materials used included academic publications, Industry reports, books and other credible internet sources on Generative AI and its impact on the education and creativity of the Gen Z. The document analysis included policy papers, educational technology reports, case studies and white papers from academic and professional bodies as well as other industries that involve Generative AI. Several insights show that using Generative AI can positively impact learners’ experiences, engagement, and creativity. However, there was some controversy about the excessive usage of AI and claimed that because of it people may get worse at critical thinking. The following were noted to be major concerns; Ethical Issues: they included issues to do with bias in the algorithms as well as the right to privacy of data. Thus, the findings of this research point to a three-way settlement with respect to the use of Generative AI in education and creative industries. It underlines the guideline of how human creativity and critical thinking ought to be sustained, while using AI tools. Proposals include, the need to teach critical thinking alongside AI use, fostering ethical AI consciousness, surged AI education, appropriate non-ethnical AI data set, strong AI policies and pro positive AI inspires and creative constructive use. The research implications for future studies include studying the changes in the achievement of learning outcomes over a period of time, wherein Generative AI has been incorporated and understanding how this technology influences different learning styles and needs, the issues of ethical and privacy concern, the requirement of professional development to educators in relation to Generative AI and finally, the comparison information and communication technology for learning between different cultures. Related to that, further studies on the effectiveness of AI in approaches like collaborative learning, its potential on preparing learners for employment, and on the psychology of students would be helpful in informing the future advancement of Generative AI in school and particular creative areas.

ВЛИЯНИЕ ЦИФРОВЫХ ТЕХНОЛОГИЙ НА ОБУЧЕНИЕ В МЕДИЦИНСКИХ ВУЗАХ: СОЦИОЛОГИЧЕСКИЕ АСПЕКТЫ И ВЛИЯНИЕ НА ЗДРАВООХРАНЕНИЕ

This article is dedicated to the study of the impact of digital technologies on the educational process in medical universities, with a focus on sociological aspects and their significance for the healthcare system. It examines how the use of digital tools and platforms changes students' approaches to studying medicine and shapes their perception of the role of technology in improving the quality of medical services and ensuring public health. The informational and educational environment transforms traditional teaching methods, providing new opportunities for updating curricula and creating conditions for the rapid exchange of information between students and educators. Digitalization of the educational process implies a change in the roles of all participants in the educational process. Students become researchers actively using information and communication technologies to acquire knowledge and practical skills. At the same time, educators assume the role of mentors, supporting students in their learning and possessing the necessary competencies in digital technologies. The article analyzes the most effective tools for facilitating interaction in the educational environment, such as virtual boards like Google Jamboard, Miro, and Padlet, which contribute to the development of critical thinking and presentation skills among students. Additionally, the article addresses the challenges faced by medical universities in the process of integrating digital technologies. Effective implementation of digitalization requires strategic planning and a comprehensive approach to updating educational programs, which will create a sustainable platform for the future professional growth of students. In conclusion, the authors emphasize that digital technologies, along with innovative teaching methods, significantly enhance the quality of education in medical universities. This, in turn, contributes to the training of highly qualified specialists capable of effectively working in modern healthcare conditions, ultimately having a positive impact on public health and the organization of healthcare as a whole.

Influence of Teachers’ Experience in Information Communication Technology and Students’ Academic Performance in Mathematics in Public Secondary Schools of Rwanda: A Case of Kayonza District

The study evaluated the impact of teachers' ICT experience on students' math performance in public secondary schools in Kayonza District, Rwanda. Involving 259 participants (223 teachers and 36 head teachers), data were gathered through questionnaires, interviews, and observations, and analyzed using SPSS Version 20.0. Findings revealed that 83.5% of respondents strongly agreed that teachers skilled in MS Excel, 85.9% in PowerPoint, and 83.9% in preparing teaching materials with ICT tools positively influenced students' performance. Additionally, 82.3% noted that teachers trained in exam preparation with ICT tools demonstrated their ICT experience. Improved student outcomes were reported, with 79.4% noting better numeracy, 80.2% improved grades, and 78.3% enhanced skills in experimenting with MS Excel. A significant positive correlation was found between teachers' ICT expertise and students' math performance. The study suggests that proficiency in ICT tools enhances students' academic performance. The Ministry of Education should support ICT integration to boost academic achievement, with superintendents and head teachers optimizing ICT training and lab usage. Teachers should share ICT skills to improve instruction and student outcomes. Future research should investigate the broader effects of ICT in education on academic achievement in mathematics.

Optimization Analysis of Low-Noise Amplifier

This paper mainly analyzes the important component of a wireless receiver – low-noise amplifiers (LNA). Low-noise amplifiers can reduce the effect of noise on the received signal and amplify signals. For the better development of wireless communication technology, remote control, and other related fields, it is necessary to optimize the design of low-noise amplifiers, so that low-noise operational amplifiers can finally obtain high gain, low noise figure, and other advantages. This paper analyzes the optimization design of LNA in the field of Global Navigation Satellite Systems (GNSS). This paper analyzes a low-noise amplifier design with the operation of concurrent dual-band for GNSS receivers with a single channel. The optimized design of a low-noise amplifier in this field uses a low-cost 0.18µm CMOS tube and adds load capacitances and a feedforward path to reduce the noise figure (NF) of the system. Noise figures in 1.2 GHz and 1.57 GHz bands are reduced by about 0.9 dB. In addition, the high gain, high linearity, and other excellent properties are shown in 1.2 GHz and 1.57GHz. In the application of 5G/6G network technology, the optimized design of Gallium Arsenide (GaAs) PHEMT technology with a gate length of 0.3µmm and inductively degraded common source topology phase structure is adopted. From this optimization, the noise figure is optimized to 1.3-1.4, and the gain of the system is 20-22 dB. At the same time, good linearity is also shown. Then, the prospects of semiconductor materials such as Gallium Nitride (GaN) and Indium Phosphide (InP), which have the advantages of high electron mobility and low-cost design, in optimizing low noise operational amplifiers. To a certain extent, the in-depth study of the optimal design of LNA can improve the technical level of wireless communication, aerospace, and other fields.

BRIDGING THE DIGITAL GAP: ANALYSING THE IMPACT OF ICT DIFFUSION ON INCOME INEQUALITY IN INDONESIA

The development of information and communication technology (ICT) has significantly transformed the socio-economic structure of society, particularly in terms of income distribution. This study aims to analyse the impact of ICT development on income inequality across 34 provinces in Indonesia from 2012 to 2020, employing econometric methods based on panel data through a fixed effects model. Empirical findings reveal that ICT readiness and ICT skills tend to exacerbate income inequality, while ICT use and its quadratic form contribute to reducing it. These results suggest that although ICT infrastructure and skills are crucial, their uneven distribution and utilisation can widen income disparities. Consequently, this study highlights the importance of ensuring equitable access to ICT across Indonesia to mitigate economic inequality. The study's recommendations stress the pivotal role of the government in expanding ICT infrastructure, fostering a research and innovation climate conducive to digital transformation, and formulating policies that enhance ICT skills nationwide. These measures would improve digital literacy, upgrade workforce capabilities, and promote inclusive digital economic growth. Ultimately, comprehensive and equitable ICT development can play a critical role in reducing income inequality across Indonesia.

The effectiveness and barrier of reverse cascade screening for paediatric familial hypercholesterolaemia

Abstract Background Familial Hypercholesterolaemia (FH) is an autosomal dominant genetic disorder that is prevalent in one out of every 300 individuals in the general population. Recognized as an actionable condition, early diagnosis of FH in children is crucial to prevent the onset of atherosclerosis, and equally important is the identification of parents before the development of coronary artery disease events. Therefore, the significance of universal FH screening during childhood combined with reverse cascade screening has been increasingly acknowledged. To achieve these objectives, in our region, universal lipid screening is conducted in elementary schools for children aged 9 or 10 years, annually screening approximately 8,000 individuals, with those identified being referred through a three-step process to four major healthcare facilities within the prefecture. However, the effectiveness of reverse-cascade screening remains unclear. Purpose Our study aimed to assess the effectiveness of and identify barriers to implementing reverse cascade screening in conjunction with universal lipid screening. Methods We conducted a single-center, retrospective observational study from January 2018 to July 2023 and evaluated the utility of reverse cascade screening for children genetically diagnosed with FH following universal screening. This study analysed prevalent barriers and identified families with suspected FH through reverse cascade screening, counting those with Potential FH among second-degree relatives. Beyond successful identification, we categorized the frequently encountered barriers into five distinct categories: bereavement, prior cardiovascular events, dyslipidaemia with unspecified LDL-C levels, incomplete family history information, and inadequate disease awareness of FH. Reverse FH diagnosis adhered to current guidelines. Results and Discussion Of 151 paediatric patients referred to our facility, 67 were genotypically confirmed to have FH. Excluding 8 cases of divorce and 4 cases of tracking challenges, 55 patients were finally analysed. 62 patients were diagnosed with reverse FH, and appropriate treatment was initiated. Notably, 158 cases of Potential FH were identified. (25 first-degree and 133 second-degree relatives). While a higher number of reverse FH diagnoses were observed in first-degree relatives (55 cases), the diagnostic yield was lower among second-degree relatives (seven cases). Common barriers for second-degree relatives included dyslipidaemia with unspecified LDL-C levels (42 cases) and incomplete family history information (66 cases). Conclusion Integrating universal screening with reverse cascade screening facilitates the identification of new FH cases. A comprehensive strategy for diagnosing reverse FH is paramount, raising disease awareness, genetic testing, the development of a family-based care plan, and enhancing access to information and communication technologies.

A Strategy for Predicting the Performance of Supervised and Unsupervised Tabular Data Classifiers

AbstractMachine Learning algorithms that perform classification are increasingly been adopted in Information and Communication Technology (ICT) systems and infrastructures due to their capability to profile their expected behavior and detect anomalies due to ongoing errors or intrusions. Deploying a classifier for a given system requires conducting comparison and sensitivity analyses that are time-consuming, require domain expertise, and may even not achieve satisfactory classification performance, resulting in a waste of money and time for practitioners and stakeholders. This paper predicts the expected performance of classifiers without needing to select, craft, exercise, or compare them, requiring minimal expertise and machinery. Should classification performance be predicted worse than expectations, the users could focus on improving data quality and monitoring systems instead of wasting time in exercising classifiers, saving key time and money. The prediction strategy uses scores of feature rankers, which are processed by regressors to predict metrics such as Matthews Correlation Coefficient (MCC) and Area Under ROC-Curve (AUC) for quantifying classification performance. We validate our prediction strategy through a massive experimental analysis using up to 12 feature rankers that process features from 23 public datasets, creating additional variants in the process and exercising supervised and unsupervised classifiers. Our findings show that it is possible to predict the value of performance metrics for supervised or unsupervised classifiers with a mean average error (MAE) of residuals lower than 0.1 for many classification tasks. The predictors are publicly available in a Python library whose usage is straightforward and does not require domain-specific skill or expertise.

Recent Advances in Organic Photodetectors

Organic photodetectors (OPDs) have garnered significant attention in fields such as image sensing, health monitoring, and wearable devices due to their exceptional performance. This review summarizes recent research advancements in materials, structures, performance, and applications of narrowband organic photodetectors, hybrid organic–inorganic perovskite photodetectors, flexible organic photodetectors (FOPDs), and photomultiplication type organic photodetectors (PM-OPDs). Organic semiconductors offer substantial potential in optoelectronic devices owing to their low cost, ease of processing, and tunable spectral response. Hybrid perovskite materials extend the spectral response range, FOPDs meet the demands of wearable devices, and PM-OPDs enhance sensitivity, allowing for the detection of weak light signals. Through innovations in materials, structural optimization, and improvements in manufacturing processes, the performance of OPDs has seen significant enhancement. This article also explores the application prospects of these technologies in medical monitoring, optical communications, and image sensing.

How much digital public health is taught in German public health programs? A qualitative analysis

Abstract Background Digital public health (DiPH) professionals are crucial for a successful digital transformation in social and health care. However, the extent to which academic professionals are taught DiPH-related content in their public health studies in Germany remains unclear. Methods We used a systematic module handbook analysis to assess accredited full-time public health-oriented degree programs at German Universities for DiPH-related modules. Programs were identified through the Hochschulkompass platform and among the German Public Health Association member programs. We used MAXQDA for the content analysis of the included module handbooks. Results We identified 79 public health programs. Of these, 10 Bachelor’s and 6 Master’s offered at least one DiPH module, all being highly heterogeneous in their overall addressed public health subfields. Regarding DiPH, 6 programs lectured on the ethics of digitalizing health and data protection, 5 programs each offered modules on DiPH in health policy, on data science in healthcare and software used for eHealth, or on the digital determinants of health. 4 programs each included modules on digital health promotion and alternative evaluation methods. 3 programs each addressed digital science communication and health technology assessment. Finally, 2 programs had modules on digital epidemiology. We found differences between the common understanding of DiPH in academia and the content in the module handbooks. The content identified in the analysis focused mainly on technical areas. Social and health science content was only marginally present. Conclusions The diverse DiPH study programs allow academic public health specialists in Germany to develop specific profiles. There is a need for further development of modules with relevance to the respective degree program to achieve comprehensive competencies in DiPH. We encourage the uptake of our analysis to develop a core curriculum in DiPH among European public health programs. Key messages • Digitalization affects all areas of life, including healthcare systems and public health. Preparing the future workforce already during their training is crucial to enable them to lead the change. • German public health programs are not fit to prepare students for their professions. Developing DiPH curricula with pre-defined and updated competencies will strengthen programs in Germany and beyond.

Preliminary Investigation of Context-Aware AAC with Automated Just-in-Time Cloze Phrase Response Options for Social Participation from Children on the Autism Spectrum.

Social participation for emerging symbolic communicators on the autism spectrum is often restricted. This is due in part to the time and effort required for both children and partners to use traditional augmentative and alternative communication (AAC) technologies during fast-paced social routines. Innovations in artificial intelligence provide the potential for context-aware AAC technology that can provide just-in-time (JIT) communication options based on linguistic input from partners to minimize the time and effort needed to use AAC technologies for social participation. This preliminary study used an alternating treatment design to compare the effects of a context-aware AAC prototype with automated cloze phrase response options to traditional AAC for supporting three young children who were emerging symbolic communicators on the autism spectrum in participating within a social routine. Visual analysis and effect size estimates suggest the context-aware AAC condition resulted in increases in linguistic participation, vocal approximations, and visual attention for all three children. While this study was only an initial exploration and results are preliminary, context-aware AAC technologies have the potential to enhance participation and communication outcomes for young emerging symbolic communicators on the autism spectrum and more research is needed.

Online Marketing and Advertising in Nigeria: Prospects and Challenges

The study was guided by three research objectives and underpinned by two theories: Technological Determinism and Social Presence Theories. The study was tilted toward qualitative methodology while library research design was adopted in studying secondary data obtained from books, journals, and newspapers which were analysed. Among other findings, the study ascertained that the Internet and other communication technology channels are generally changing many aspects of consumers’ buying behaviour all over the world, including Nigeria, to new goods and services and that the Internet and the World Wide Web have transformed how goods are bought and the way businesses are conducted. The study also found that despite the prospect of online marketing and advertising, unpredictable fixed line networks in Nigeria and unstable electricity supply, pose serious challenges to online marketing and advertising in Nigeria. The study concluded that these situations pose challenges to sustainable marketing communication in Nigeria. Given these findings, the study recommended among others that, the government and all the relevant stakeholders should improve on internet infrastructure in Nigeria, constant electricity supply, and make data subscription affordable and that online advertising messages need to be compelling and engaging to be able to capture and retain audience attention, to achieve the desired result.

Insights from Smart City Initiatives for Urban Sustainability and Contemporary Urbanism

Urbanization growth poses various challenges, such as congestion, pollution, and resource consumption, prompting city planners and governments to adopt smart systems to manage these issues more efficiently. Despite widespread adoption, there is no consensus on the defining attributes of smart cities, particularly regarding their role in urban sustainability and contemporary urbanism. This paper provides a literature review to understand the implications of smart city initiatives for sustainable urban planning, focusing on practices in Singapore, Helsinki, Barcelona, and Medellin. Based on 71 publications surveyed from Scopus and Web of Science, this paper evaluates smart, sustainable initiatives undertaken in these four cities across six smart domains: mobility, governance, environment, people, living, and economy. This review shows that most studies focus on Barcelona and Singapore, particularly in the domains of smart environment and governance. Despite differing urban contexts, the notion of “smart” is closely tied to using information and communication technologies to drive urban operations. This analysis identifies a lack of assessment studies on the benefits of smart cities in terms of urban sustainability and a lack of holistic approaches to address the complex challenges cities face in achieving sustainable development.

Participation rate and yield of indirect cascade genetic screening for familial hypercholesterolemia using a web-based communication platform (CATCH): a randomized multicenter implementation trial

Abstract Introduction Cascade genetic screening for familial hypercholesterolemia (FH) is poorly performed when direct contact with relatives by health professionals is not allowed. We aimed to compare the effectiveness of implementing a web-based communication platform in a large-scale cascade genetic screening program for FH, while maintaining the confidentiality of indirect family contacts. Methods The CATCH study is a prospective randomized open-label multicenter implementation trial that promotes family cascade genetic screening in Swiss adults older than 16 years with a newly diagnosed pathogenic variant for FH. Each family was randomly assigned to a multigenerational indirect cascade genetic screening (1:1) either via the possibility to use of a web-based platform designed to send pre-prepared text messages or emails, and allowing to connect to a specialized clinical center, or a cascade genetic screening via the local recommended procedure. The primary outcome was the 6-month participation rate of relatives, defined as the number of genetic tests performed within 6 months after referral, over the number of eligible relatives for screening, i.e. living first-degree relatives aged 5 years or older. The secondary outcome was the 6-month yield of positive genetic tests, defined as the number of positive tests over the number of eligible relatives. Both outcome estimates were obtained after accounting for family clusters. Results Between 11/20 and 12/23, we enrolled 221 adults with a phenotype of FH for genetic testing, with 87 of them having confirmed genetic FH, corresponding to a total of 401 eligible relatives for cascade screening, with 102 relatives eventually undergoing genetic testing. Among the 87 index cases with genetic FH, 44 were randomly assigned to the web-based platform screening, and 43 were assigned to the local procedure screening. The 6-month participation rate of relatives was 71 of the 218 eligible relatives (29% [95% CI 21–38]) for the web-based platform screening, and 31 of the 183 eligible relatives (14% [95% CI 8–23]) for the local procedure screening (odds ratio (OR) 2.46 [95% CI 1.22–4.98]), p=0.012) (Figure). Similarly, the yield of detection of positive relatives was higher for the web-based platform screening, (16% [95% CI 11–23]) compared to the local procedure screening (7% [95% CI 4–13]), with OR of 2.43 [95% CI 1.11–5.30], p=0.026. Among the 55 positive relatives identified with genetic FH within 6-months, 13.0% were younger than 18 years, 25.5% were current smokers, and 56.0% were not using any lipid-lowering drugs. Conclusions The effectiveness of cascade genetic screening for FH was approximately doubled by integrating a web-based platform that facilitates family communication and access to specialized clinical centers. Modern communication technology is well suited to be integrated in large-scale indirect genetic screening programs to increase the early identification of patients at high cardiovascular risk.

Efficient and Compressed Deep Learning Model for Brain Tumour Classification With Explainable AI for Smart Healthcare and Information Communication Systems

ABSTRACTThe detection of brain tumours presents a significant challenge in the medical domain, where prompt and precise diagnosis is crucial as patient outcomes depend on it. Conventional deep neural networks perform well in carrying out various imaging tasks within the healthcare sector; however, their effectiveness often falls short of expectations in practical applications due to the substantial computational resources required and issues with reliability. In this research, an optimised and effective deep learning model founded on the DenseNet‐169 architecture is introduced for the classification of magnetic resonance imaging brain tumours, which is particularly advantageous for smart healthcare systems and information and communication technology (ICT) settings with limited computational capabilities. The model compression methodologies, including pruning and quantization, have been employed to significantly diminish the dimensions and intricacy of the model while achieving a classification accuracy of 97.07%. Furthermore, this endeavour necessitates the enhancement of the model's interpretability through the utilisation of explainable artificial intelligence methodologies such as Gradient‐weighted Class Activation Mapping (Grad‐CAM) and SHapley Additive exPlanations (SHAP), which will aid clinicians in highlighting crucial areas of the images and validating feature importance concerning the decisions rendered by the model. A comparative performance evaluation is conducted against DenseNet‐169, ResNet‐50 and various other models to delineate the superior efficacy of our model, rendering it exceptionally adept for knowledge‐driven, real‐time brain tumour diagnosis within smart healthcare and ICT systems where resources are constrained.

Navigating the Social Shift: A Preliminary Study of Consumer Behavior on Malaysia's Social Commerce Platforms

The proliferation of online businesses coupled with the advancement of information and communication technologies (ICT) has led to the emergence of social commerce. The integration of social media platforms into e-commerce offers businesses a convenient avenue for engaging with consumers and a novel method for capturing additional sales volume. This seamless integration of social media platforms into current e-commerce practices has significantly increased its success. Research on the factors that affect purchase intentions in social commerce is still sparse, especially in the context of Malaysian consumers. In general, the causal model of purchase intentions on social commerce in this study aims to investigate the impact of the constructs of visibility and metavoicing on purchase intentions among Malaysian millennial consumers. Henceforth, this initial study specifically aims to evaluate the reliability and validity of the IT affordance constructs of visibility and metavoicing through rigorous assessments. At the items-construct level, the internal consistency assessment was conducted using McDonald’s Omega, which is known for its robustness in measuring the reliability of multi-item constructs. At the construct level, discriminant validity and the strength of linear relationships among variables were assessed using Pearson correlation analysis. The pilot dataset gathered was analyzed with SPSS 29, and the results have validated that the predictive model achieved high levels of reliability and validity at both the item and construct levels. Furthermore, the analysis revealed that the independent variables, metavoicing and visibility, have a significant association with purchase intentions, underscoring their importance in understanding consumer behavior.

A Hybrid Machine Learning‐Based Data‐Centric Cybersecurity Detection in the 5G‐Enabled IoT

ABSTRACTBy increasing smart objects as high‐potential computing devices and 5G technology, the Internet of Things (IoT) has emerging technology to provide a data‐centric infrastructure for detecting safe device‐to‐device communications by supporting security and privacy issues. As a 5G‐enabled communication technology, vehicle‐to‐vehicle (V2V) communication provides a wireless exchange information connection between smart vehicles, intelligent devices, and a cloud‐edge computing environment. This connection should be established with a safe and secured run‐time protection system to avoid many critical anomalies and misbehavior problems. Detecting run‐time malicious transformations with data‐centric misbehaving reactions is a main challenge for autonomous vehicle communications with 5G‐enabled communication technology. This paper provides a hybrid genetic algorithm‐based ensemble bagged trees (GA‐EBT) algorithm for a data‐centric misbehavior detection approach to support the V2V communications against malicious and misbehavior transactions. In this hybrid algorithm, GA provides a beneficial performance on the training accuracy factor based on a single‐objective fitness function and EBT supports a high‐degree prediction and training process using multiple decision tree models with bootstrapped samples of the training data. For the evaluation of the proposed algorithm, four real test cases are applied for messaging injection attacks in the V2V environments in comparison to the state‐of‐the‐art machine learning algorithms. The experimental results show that the proposed hybrid approach can achieve an optimal high rate accuracy factor of 99.999, precision and recall factors of 100%, and an F1‐Score factor of 100% to detect unexpected cyber‐attacks for the V2V communications in the IoT environment.

Cloud-Based Automatic Configuration and Disaster Recovery of Communication Systems Applied in Engineering Training

Network management and troubleshooting require not only a grasp of advanced concepts but also the development of analytical and problem-solving skills. To bridge this gap, this paper introduces a novel network administration system, DRACSC (Spanish acronym for device for automatic recovery and configuration of communication systems), designed for the automatic configuration and disaster recovery of communication equipment. This system transcends the limitations of current hardware and software solutions by combining their advantages, boasting portability, automated functions, and a cloud-based repository as its main features. The DRACSC system, undergoing a comprehensive large-scale evaluation involving diverse user groups across multiple institutions, was tested with 89 users, including students and teachers at educational centers and ICT (Information and Communication Technology) professionals. The benefits of the system were evaluated through a training program based on simulated real-world ICT environments, focusing on both quantitative results on the reduction in time to complete user tasks, as well as qualitative results on the interface and usability of the system. Statistical analysis, including Welch’s t-test on opinion surveys, indicated a significant increase in knowledge and understanding, demonstrating the system’s potential to enhance education and practice. Moreover, the evaluation shed light on the user experience, with positive impacts observed for learning and teaching implications. As a result, the study has verified that the system has the potential to significantly influence network management practices, enhancing both learning and professional application through improved efficiency and usability.