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Digital transformation driven by artificial intelligence (AI) allows Industry 4.0 and the internet of things (IIoT) to make significant advancements in automating, controlling, and improving the quality of numerous manufacturing processes. Three-dimensional (3D) surface topography of manufactured products holds important information about the quality of manufacturing processes. Surface topography consists of unique properties, which makes the current monitoring approaches ineffective in identifying local and spatial surface faults. In this paper, we develop a generalised spatially weighted autocorrelation approach based on AI for monitoring changes in products based on their 3D topographic surfaces. We propose two effective algorithms to identify and assign spatial weights to the topographic regions with suspicious characteristics. The normal surface hard thresholding algorithm initially enhances the representation of surface characteristics through binarization, followed by the normal surface connected-component labelling algorithm, which utilises the obtained binary results to identify and assign spatial weights to the suspicious regions. We then introduce a generalised spatially weighted Moran index, which exploits the assigned weights to locally characterise and monitor changes in the spatial autocorrelation structure of identified regions. After an anomaly surface is detected, we extract different fault diagnostic information. The proposed approach proves its robustness and efficiency in characterising, monitoring, and diagnosing different patterns of faults in 3D topographic surfaces. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) optical systems have been recently deployed for the assessment of 3D topography of finished products during manufacturing processes. Although the 3D topographic data contain rich information about the product and manufacturing processes, existing monitoring approaches are incapable of capturing the complex characteristics between the topographic values, which makes them ineffective in detecting local and spatial surface faults. We develop a spatially weighted graph theory-based approach for accurate monitoring of 3D topographic surfaces. We imporove the representation of surface characteristicsby proposing the in-control multi-region surface segmentation algorithm, which segments the observed topographic pixels into clusters according to the information learned from in-control surfaces. We propose the maximum local spatial randomness feature for the effective description of local and spatial topographic characteristics. After representing the surface characteristics as a spatially weighted graph network, we monitor its connectivity through the developed spatial graph connectivity statistic. The proposed approach is robust in detecting and locating different forms of local and spatial faults that appear on simulated and real-life topographic surfaces and outperforms the existing monitoring approaches. [ABSTRACT FROM AUTHOR]

The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming, Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat in the research area. A botnet is a collection of infected devices that are managed by a botmaster and communicate with each other via a command server in order to carry out malicious attacks. With the rise in malicious attacks, detecting botnet applications has become more challenging. Therefore, it is essential to investigate mobile botnet attacks to uncover the security issues in severe financial and ethical damages caused by a massive coordinated command server. Current state of the art, various solutions were provided for the detection of botnet applications, but in general, the researchers suffer various techniques of machine learning-based methods with static features which are usually ineffective when obfuscation techniques are used for the detection of botnet applications. In this paper, we propose an approach by exploring the concept of a deep learning-based method and present a well-defined Convolutional Neural Network (CNN) model. Using the visualization approach, we obtain the colored images through byte code files of applications and perform an experiment. For analysis of the results of an experiment, we differentiate the performance of the model from other existing research studies. Furthermore, our method outperforms with 94.34% accuracy, 92.9% of precision, and 92% of recall. [ABSTRACT FROM AUTHOR]

The 3D surface topography of finished products is a key characteristic for monitoring the quality of products and manufacturing processes. The topography has unique properties in which the topographic values are spatially autocorrelated with their neighbours and the locations of topographic values randomly change from one surface to another under the in-control process behaviour, making the online detection of local topographic changes challenging. Due to the complex structure of topographic data, the existing monitoring approaches lack the detection of local changes. Therefore, we develop a novel online monitoring approach for detecting local changes in 3D topographic surfaces. We introduce a multilevel surface thresholding algorithm for enhancing the representation of topographic values by slicing the 3D surface topography into cumulative levels in reference to the characteristics of the in-control surfaces. The spatial and random properties of topographic values are quantified at each surface level through the proposed spatial randomness profile. After obtaining the spatial randomness profile, an effective monitoring statistic based on the functional principal component analysis is developed for detecting anomaly surfaces. The proposed approach shows superior performance in identifying a wide range of fault patterns and outperforms the existing approaches in both simulated and real-life topographic data. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of human intelligence is still an ongoing process, i.e., human and information security are not yet perfectly matched. By understanding cognitive processes, designers can design humanized cognitive information systems (CIS). The need for this research is justified because today’s business decision makers are faced with questions they cannot answer in a given amount of time without the use of cognitive information systems. The researchers aim to better strengthen cognitive information systems with more pronounced cognitive thresholds by demonstrating the resilience of cognitive resonant frequencies to reveal possible responses to improve the efficiency of human-computer interaction (HCI). A practice-oriented research approach included research analysis and a review of existing articles to pursue a comparative research model; thereafter, a model development paradigm was used to observe and monitor the progression of CIS during HCI. The scope of our research provides a broader perspective on how different disciplines affect HCI and how human cognitive models can be enhanced to enrich complements. We have identified a significant gap in the current literature on mental processing resulting from a wide range of theory and practice. [ABSTRACT FROM AUTHOR]