63 research outputs found
Review on Network Function Virtualization in Information-Centric Networking
Get PDFNetwork function virtualization (NFV / VNF) and information-centric networking (ICN) are two trending technologies that have attracted expert's attention. NFV is a technique in which network functions (NF) are decoupling from commodity hardware to run on to create virtual communication services. The virtualized class nodes can bring several advantages such as reduce Operating Expenses (OPEX) and Capital Expenses (CAPEX). On the other hand, ICN is a technique that breaks the host-centric paradigm and shifts the focus to 'named information' or content-centric. ICN provides highly efficient content retrieval network architecture where popular contents are cached to minimize duplicate transmissions and allow mobile users to access popular contents from caches of network gateways. This paper investigates the implementation of NFV in ICN. Besides, reviewing and discussing the weaknesses and strengths of each architecture in a critical analysis manner of both network architectures. Eventually, highlighted the current issues and future challenges of both architectures. © 2021 IEEE
Models of adopting cloud computing in the E-Government context: a review
No full textGovernments and public sector agencies are continuously looking for ways to improve their services. Therefore, there is a need for restructuring processes and effectively using technology to improve efficiency and effectiveness of the business operations. Cloud computing is one of recent technological trends that support these efforts. It is a new type of sourcing model in which computing services are provided as a utility over the Internet. This paper analyzes the benefits and challenges of cloud computing over e-government systems. It reviews the existing literature on the proposed models of cloud computing adoption in the context of e-government. Further, this paper critically analyzes and classifies these models to different categories
Value Innovation in the Malaysian Telecommunications Service Industry: Case Study
Get PDFWireless telecommunications industry has been widely known for its high levels of innovativeness and competitively dynamic business environment. Globalization, privatization, liberalization and consolidation are the terms mostly associated with this sector. The Malaysian telecommunications industry is of no exception for it has exhibited high levels of competitiveness and dynamic change leading to rapid evolution of wireless technologies and high growth of subscribers and penetration rate. Thus, this paper aims to shed some light on the logic of value innovation in the telecommunication service industry. The present study adopted a qualitative research approach to investigate the value innovation activities on Malaysian telecommunications service sector. Specifically, it concentrates on the motivation of telecommunications companies to shift their strategies from tariff competition to value competition in order to improve customer value and promote customer loyalty which lead to increased business performance and profitable growth. An analytical case study was used to examine services and loyalty packages offered by telecommunications companies. In this regard, the paper discusses the shortfalls and actions that discouraged customer satisfaction and loyalty. The paper also provides recommendations for service providers on how to attain long-term profitable growth
Green information technology adoption antecedence: a conceptual framework
No full textGreen Information Technology (Green IT) has been adopted in developed countries, as a strategic initiative consideration for developing sustainable business practices, through the balancing economic and environmental performance of an organization. However, there is still a low adoption in developing countries like Malaysia, and limited empirical research on the Green IT adoption, despite it being a necessity even though numerous government incentives were provided to adopt the green IT in Malaysia. The aims of this research are to develop a conceptual framework to determine the relationship between learning capability, innovation capability, emotional capability, and government incentives as drivers toward green IT Adoption. In order to develop the green IT adoption’s antecedents’ relationships in Malaysia, this research will rely on some technology adoption theories such as Technology-organization-environment (TOE), Resource-based theory (RBT), Institutional theory (IT). Based on previous literature, there is a relationship among green IT adoption with organization capabilities, and government incentives, but in a scattered manner. Furthermore, this study has provided integrated framework for researchers and business homes, it showed that internal and external factors play major role in green IT adoption within organizations, So, they can start their green initiatives to cope with requirements in a new industrial revolution 4.0. Also, economic and environment sustainability should be considered as consequences factors in future researches
Improving Prediction of Bursa Malaysia Stock Index Using Time Series and Deep Learning Hybrid Model
No full textThe stock market is an important component of the financial world. Most of the stock market contains uncertainty and volatility leading to difficulty in predicting the future price of stocks and the market’s movement. The computing approach is a widely used technique in stock market forecasting that can assist the rapid and precise study of massive datasets. Existing studies have shown that such a technique can yield comparable or even better performances than traditional time series models in stock forecasting. Hybridizing both computing and traditional approaches lead to better performance since hybrid models utilized the advantage of each model. In this study, a hybrid model which combines autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH) and long short-term memory (LSTM) model was proposed to forecast the closing price of Bursa Malaysia Kuala Lumpur Composite Index (KLCI). The proposed model operated by capturing the linear and volatility pattern from the time series model while the deep learning model handled the remaining non-linear pattern. The findings indicated an overall improvement of 13.32% for RMSE and 0.97% for MAE as compared to other benchmark models. The hybrid models can also forecast the actual data with a shorter computational time of 0.82% of that taken by the regular LSTM model
Sustainable e-Learning framework: expert views
Get PDFThe efforts toward sustainable development goals in the educational context are of growing importance even in an e-learning perspective. Sustainability aims to improve the e-learning quality since it supports long-term innovation processes while benefiting society, economy, and the environment. In order to portray its prosperous mission, this paper presented the outline of a Sustainable e-Learning Framework (SeLF). This paper aimed to collect the expert perspectives on this framework. Qualitative data were collected through expert interviews during which the utility and usability of the framework were iteratively evaluated and refined. In order to achieve the framework practicability in different contexts, stakeholders from various universities were invited to participate. The expert perceptions and expectations of sustainable e-learning in the context of SeLF were presented in this paper. The experts were asked to reflect on the possible impact of SeLF toward sustainable practices at their own university and personal practice of e-learning sustainability. The findings indicated that SeLF can be used as a guideline for developing sustainable e-learning that supports the continuity of e-learning initiatives
Detecting false messages in the smartphone fault reporting system
Get PDFThe emergence of the Internet of Things (IoT) in Smart City allows mobile application developers to develop reporting services with an aim for local citizens to interact with municipalities regarding city issues in an efficient manner. However, the credibility of the messages sent rise as a great challenge when users intentionally send false reports through the application. In this research, an evidence detection framework is developed and divided into three parts that are a data source, IoT device’s false text classification engine and output. Text-oriented digital evidence from an IoT mobile reporting service is analyzed to identify suitable text classifier and to build this framework. The Agile model that consists of define, design, build and test is used for the development of the false text classification engine. Focus given on text-based data that does not include encrypted messages. Our proposed framework able to achieve 97% of accuracy and showed the highest detection rate using SVM compared to other classifiers. The result shows that the proposed framework is able to aid digital forensic evidence experts in their initial investigation on detecting false report of a mobile reporting service application in the IoT environment
A Single Channel EEG-Based Algorithm for Neonatal Sleep-Wake Classification
No full textSleep is categorized as an arrangement of modifications occurring in our body inside our brain, muscles, working its way through our eyes (occipital lobe), respiratory along with cardiac activity. It makes the human body fresh and ready for the next day. In neonates, it is essential for brain and physical development. Polysomnography is the gold standard for determining and classification of sleep stages. However, it is expensive and requires human intervention. Therefore, over the past two decades, researchers proposed multiple algorithms for automatic neonatal sleep stage classification. All the previous studies used multichannel EEG recordings for classification. Not every intensive care unit contains a multichannel EEG extraction device. For this reason, a single channel automatic neonatal sleep-wake classification algorithm, using a support vector machine, has been proposed in this paper. 3525 30-s training and testing were used to train and test the network. The proposed algorithm can reach sleep-wake classification accuracy of 77.5% with mean kappa 0.55 using single channel EEG. The results were extracted using five-fold cross-validation and the mean has been reported in this paper. Experimental results and statistical analysis show that single channel EEG can be used for neonatal sleep classification with notable accuracy.</p
Cross-layer based intrusion detection system for wireless sensor networks: challenges, solutions, and future directions
Get PDFWireless Sensor Networks (WSNs) consist of numerous affordable, energy-efficient, compact wireless sensors. These sensors are designed to collect, process, and communicate data from their surrounding environment. Several energy-efficient protocols have been created specifically for WSNs to optimize data transfer rates and prolong network lifespan. Multi-channel protocols in WSN are one of the ways to optimize efficiency and enable seamless communication between nodes, thereby reducing interference and minimizing packet loss through multiple channels. Despite their numerous advantages in data sensing and monitoring, various attacks can pose a threat to a WSN. There are several types of attacks that a WSN may encounter, including spoofing, eavesdropping, jamming, sinkhole attacks, wormhole attacks, black hole attacks, Sybil attacks, and DoS attacks. One of the strategies for enhancing security in WSNs is implementing a cross-layer intrusion detection system (IDS) that can detect initial indicators of attacks that target vulnerabilities across multiple WSN layers. This paper reviews the existing IDS at each layer and the challenges in an energy-efficient cross-layer IDS for WSN in terms of the attacks and IDS approaches
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