Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.

A grave concern to an organization’s information security is employees’ behavior when they do not value information security policy compliance (ISPC). Most ISPC studies evaluate compliance and noncompliance behaviors separately. However, the literature lacks a comprehensive understanding of the factors that transform the employees’ behavior from noncompliance to compliance. Therefore, we conducted a systematic literature review (SLR), highlighting the studies done concerning information security behavior (ISB) towards ISPC in multiple settings: research frameworks, research designs, and research methodologies over the last decade. We found that ISPC research focused more on compliance behaviors than noncompliance behaviors. Value conflicts, security-related stress, and neutralization, among many other factors, provided significant evidence towards noncompliance. At the same time, internal/external and protection motivations proved positively significant towards compliance behaviors. Employees perceive internal and external motivations from their social circle, management behaviors, and organizational culture to adopt security-aware behaviors. Deterrence techniques, management behaviors, culture, and information security awareness play a vital role in transforming employees’ noncompliance into compliance behaviors. This SLR’s motivation is to synthesize the literature on ISPC and ISB, identifying the behavioral transformation process from noncompliance to compliance. This SLR contributes to information system security literature by providing a behavior transformation process model based on the existing ISPC literature.

Anomaly detection has been used to detect and analyze anomalous elements from data for years. Various techniques have been developed to detect anomalies. However, the most convenient one is Machine learning which is performing well but still has limitations for large-scale unlabeled datasets. Deep Reinforcement Learning (DRL) based techniques outperform the existing supervised or unsupervised and other alternative techniques for anomaly detection. This study presents a Systematic Literature Review (SLR), which analyzes DRL models that detect anomalies in their application. This SLR aims to analyze the DRL frameworks for anomaly detection applications, proposed DRL methods, and their performance comparisons against alternative methods. In this review, we have identified 32 research articles from 2017-2022 that discuss DRL techniques for various anomaly detection applications. After analyzing the selected research articles, this paper presents 13 different applications of anomaly detection found in the selected research articles. We identified 50 different datasets applied in experiments on anomaly detection and demonstrated 17 distinct DRL models used in the selected papers to detect anomalies. Finally, we analyzed the performance of these DRL models and reviewed them. Additionally, we observed that detecting anomalies using DRL frameworks is a promising area of research and showed that DRL had shown better performance for anomaly detection where other models lack. Therefore, we provide researchers with recommendations and guidelines based on this review.

Industry 4.0 (I4.0) is a technological development in the manufacturing industry that has revolutionized Total Quality Management (TQM) practices. There has been scant empirical research on the multidimensional perspective of TQM. Thus, this study aims to empirically examine the effect of the multidimensional view of TQM (soft and hard) on I4.0 readiness in small and medium-sized (SMEs) manufacturing firms. Based on the sociotechnical systems (STS) theory, a framework has been developed and validated empirically through an online survey of 209 Malaysian SMEs manufacturing firms. Unlike the existing TQM studies that used structural equation modeling (SEM), a two-stage analysis was performed in this study. First, the SEM approach was used to determine which variable significantly affects I4.0 readiness. Second, the artificial neural network (ANN) technique was adopted to rank the relative influence of significant predictors obtained from SEM. The results show that the soft and hard TQM practices have supported the I4.0 readiness. Moreover, the results highlight that hard TQM practices have mediating role between soft TQM practices and I4.0 readiness. The ANN results affirmed that customer focus is considered an important TQM factor for I4.0 managerial readiness, advanced manufacturing technology for operational readiness and top management commitment for technology readiness. In a nutshell, the SEM-ANN approach uniquely contributes to the TQM and I4.0 literature. Finally, the findings can help managers to prioritize firms’ soft and hard quality practices that promote I4.0 implementation, especially in emerging economies.

Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method’s applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.

Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou’s Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D Dominic Communicated by Ramaswamy H. Sarma