Search

Knowledge is an important asset for an organisation as it facilitates organisational growth. To facilitate knowledge creation and sharing, this is where a knowledge-intensive system is required. One key area that hinders the effective use of knowledge-intensive systems in an organisation is the lack of knowledge quality. This causes the system to be underutilised, and as a result, knowledge will not be captured or shared effectively. Recent KM findings identified that machine learning could be beneficial to knowledge management. A literature review was conducted to identify knowledge of quality attributes and machine learning algorithms. From the findings, it was identified that the decision tree algorithm has a strong potential at classifying knowledge quality. An experiment was then devised to identify the training model required and measure its effectiveness using a pilot test. This involved using a knowledge-intensive system and mapping its variables to the respective knowledge quality attributes. From the experimentation result, the training model is then devised before implemented in a pilot test. The pilot test involved collecting knowledge using the same knowledge-intensive system before running the training model. From the results, it was identified that the decision tree could classify knowledge quality though the results yielded four different outputs at classifying knowledge quality. It was concluded that machine learning is beneficial in the area of knowledge management.

As the number of aging population increases, their Quality of Life (QoL) becomes a concern in the society. The elderly is not only vulnerable due to their chronic degeneration issues but may also be insensitive to technological innovation which could possibly improve their QoL in the Industrial Revolution 4.0. The outbreak of COVID-19 has significantly threatened their well-being in their living context and will consequentially change people’s perspective towards normal lifestyle after this public health crisis. Recent studies have highlighted the usability of the drone technology in the automation of navigation, monitoring, and load carrying which can potentially facilitate various purposes of use in our daily life. This paper reviews recent academic works related to Drone-Based Internet of Things (DIoT) technology and extracts the advantages of DIoT applications, which have the potential to assist elderly’s Activity of Daily Living (ADL) in post-epidemic time. Our results suggest that with the low energy consumption, the DIoT techniques are capable of reducing the body exposure under pandemic situation and satisfying the appreciation to normal and digital-connected life in the future. Nevertheless, the limited flying range and low technology penetration among elderly users significantly impede the implementation of DIoT application. Importantly, the DIoT technology upgrades manpower-based manual work. This paper updates to the knowledge of drone technology application in the context of elderly centre during post-pandemic.

In the United States, the manufacturing ecosystem is rebuilt and developed through innovation with the promotion of AMP 2.0. For this reason, the industry has spurred the development of 5G, Artificial Intelligence (AI), and Machine Learning (ML) technologies which is being applied on the smart factories to integrate production process management, product service and distribution, collaboration, and customized production requirements. These smart factories need to effectively solve security problems with a high detection rate for a smooth operation. However, number of security related cases occurring in the smart factories has been increasing due to botnet Distributed Denial of Service (DDoS) attacks that threaten the network security operated on the Internet of Things (IoT) platform. Against botnet attacks, security network of the smart factory must improve its defensive capability. Among many security solutions, botnet detection using honeypot has been shown to be effective in early studies. In order to solve the problem of closely monitoring and acquiring botnet attack behaviour, honeypot is a method to detect botnet attackers by intentionally creating resources within the network. As a result, the traced content is recorded in a log file. In addition, these log files are classified quickly with high accuracy with a support of machine learning operation. Hence, productivity is increase, while stability of the smart factory is reinforced. In this study, a botnet detection model was proposed by combining honeypot with machine learning, specifically designed for smart factories. The investigation was carried out in a hardware configuration virtually mimicking a smart factory environment.

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy. [ABSTRACT FROM AUTHOR]