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Phishing attacks have evolved in recent years due to high-tech-enabled economic growth worldwide. The rise in all types of fraud loss in 2019 has been attributed to the increase in deception scams and impersonation, as well as to sophisticated online attacks such as phishing. The global impact of phishing attacks will continue to intensify, and thus, a more efficient phishing detection method is required to protect online user activities. To address this need, this study focussed on the design and development of a deep learning-based phishing detection solution that leveraged the universal resource locator and website content such as images, text and frames.

Deep learning techniques are efficient for natural language and image classification. In this study, the convolutional neural network (CNN) and the long short-term memory (LSTM) algorithm were used to build a hybrid classification model named the intelligent phishing detection system (IPDS). To build the proposed model, the CNN and LSTM classifier were trained by using 1m universal resource locators and over 10,000 images. Then, the sensitivity of the proposed model was determined by considering various factors such as the type of feature, number of misclassifications and split issues.

An extensive experimental analysis was conducted to evaluate and compare the effectiveness of the IPDS in detecting phishing web pages and phishing attacks when applied to large data sets. The results showed that the model achieved an accuracy rate of 93.28% and an average detection time of 25 s.

The hybrid approach using deep learning algorithm of both the CNN and LSTM methods was used in this research work. On the one hand, the combination of both CNN and LSTM was used to resolve the problem of a large data set and higher classifier prediction performance. Hence, combining the two methods leads to a better result with less training time for LSTM and CNN architecture, while using the image, frame and text features as a hybrid for our model detection. The hybrid features and IPDS classifier for phishing detection were the novelty of this study to the best of the authors' knowledge.

Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

The purpose of this paper is to assess the logistics performance of national trade corridors in Myanmar based on a theoretical portrayal of multimodal transport in logistics chains combined with the real-time operation of such chains. A cost-time-distance model was used as the core theoretical framework for the discussion. Empirical data related to cost, time and distance was obtained to evaluate national trade corridors in Myanmar. The study explored the performance of trade corridor in the pulses and beans sector from the largest sown and harvest areas to the main seaports in Myanmar. The pulses and beans sector was selected because the country is the 2^nd highest exporter in the world and would benefit from improved access to its national seaports. Under the cost-time-distance model used, it was observed that physical infrastructure, institutional environment as well as limited capability of local providers hindered the overall performance of the trade corridors under study.

Human resource (HR) transformation research has not studied the role of digital HR technology and HR role in the context of Indian organisations. To address the gap, the current research aims to investigate the impact of HR role and digital HR technology on successful HR transformation. Further, the research shall investigate the mediating role of various HR roles (i.e. administrative, employee champion, change agent and strategic partner role) on digital HR technology and business outcomes.

The research used a post-positivist methodology using survey method. Data has been collected from 918 executives representing several sectors of Fortune 500 Indian companies. Validated instrument has been used and the collected data are analysed using AMOS and structural equation modelling.

HR transformation using Digital human resource technology (HRT) can significantly enhance business outcome of fortune 500 companies of India if it is mediated by different HR role (strategic, employee champion, change agent and administrative expert). The result also proved that just implementation and adaption of the Digital HRT may not guarantee HR Transformation unless HR optimise the specific role as per the need of the hour.

HR transformation research has not studied the role of digital HR technology and HR role in the context of fortune 500 Indian organisations.

This study aims to investigate the intention of using mobile payment (m-payment) services in Sarawak, Malaysia.

A total of 194 online payment users were selected to respond to the structured questionnaire. The partial least squares-structural equation modelling (PLS-SEM) was used to analyse the data by assessing the measurement and model.

Perceived usefulness (PU) and perceived ease of use mediated the relationship between perceived compatibility (PC) and the intention to use the mobile payment for mobile network operators’ services.

The analysis provides insights that PC is considered as a significant determinant for mobile payment of mobile network operators’ services.

The operators can consider factors such as PC in the design of their mobile applications and the potential to expand the m-payment services to others e-wallet such as Sarawak e-wallet. The model possesses medium prediction power, which suggests that other variables such as perceived security and personal innovativeness also can be used to predict the usage behaviour of mobile payment for the mobile network services.

The present study contributes to the m-payment users’ behaviour intention literature by investigating the mobile-based predictors of using m-payment technology in an emerging digital economy state in Sarawak, Malaysia. This study also extends the knowledge of technology acceptance model by introducing the mediation effect of PU and ease of use between the mobile-based predictors and m-payment intention.

The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals have created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology and so on.

The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training deep learning (DL) models on thousands of images of these tests using transfer learning, this paper (1) classifies the type of the assay and (2) classifies the colourimetric results.

This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time.

To the best of the authors’ knowledge, this is the first attempt to provide colourimetric assay type classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities, it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.