Despite of swift advancements in the field of Artificial Intelligence, an effective immediate assistive solution for visually impaired individuals remains limited. Whereas, Deep Learning and Natural Language Processing (NLP) have attained significant gain in visual sympathetic and language generation but their addition into accessible systems for ecological perception is still underexplored. This dictates the development of intelligent frameworks capable of accurately rendering visual scenes and delivering meaningful descriptions to enhance liberation and situational awareness for visually impaired users. Consequently, we proposed a framework of three levels using the deep learning and NLP approaches to address aforementioned. It also developed a novel approach to learning the better relational features among the objects, scenes, and persons captured in an image and generated an accurate caption. Firstly, object detection algorithms are used to detect the objects in an image. The next level generated the image captions by maximizing the likelihood of the expected captions using deep learning. The third level, the text in the caption, is converted into a voice using NLP algorithms. The proposed model has substantially compared the existing state-of-the-art image captioning and voice conversion methods by experimenting with benchmark datasets such as Flickr 8k, Flickr 30k, COCO Caption, and RyanSpeech. The proposed model outperformed in terms of recall on Flickr 8K dataset @100 images has scored 67.25
Fourth Industrial Revolution (4IR) technologies like Internet of Things (IoT), blockchain, and artificial intelligence (AI) are being integrated into the manufacturing supply chains to boost efficiency and also improve the level of transparency. However, this shift means that cybersecurity issues are rising, which is a threat to operational resilience. In this research, the impacts of cybersecurity on supply chain management (SCM) are examined with the moderating factor of 4IR technologies. Questionnaires are administered and completed by 380 supply chain managers in manufacturing firms in Texas. Given that this research is descriptive, a quantitative research design is applied, and the Structural Equation Modelling is employed to establish the relationship between the variables. Thus, it is shown that security measures like access control (AC) and threat detection improve SCM and contribute to appropriate introduction of 4IR solutions. These technologies facilitate enhanced visibility, enhanced capability to trace events, and enhanced collaborations. The research, therefore, is a well-needed call for mapping and blending strong cybersecurity features with innovative solutions to help design reliable supply networks. Future research could consider investigating other industries within which these types of relationships could exist and the long-term consequences considered and acted upon.
This research focuses on the impact of personalisation on customer involvement in the retail industry operating in Shanghai using Fifth Industrial Revolution (5IR) technologies (Artificial Intelligence [AI], automation). The rationale for the research is to find how different technologies enhance personalisation’s role in increasing loyalty, customer retention, and interaction quality, giving a competitive advantage to retailers. The research is relevant because Shanghai’s retail sector is shifting, and customer demand is rising, exploring how technology-based personalisation advances with the expanding consumer request. The survey method employed is a quantitative cross-sectional survey, and the respondents are supply chain managers of 137 varieties of retail outlets in Shanghai. The data are obtained through questionnaires with options on a Likert scale; the respondents’ responses are statistically analysed to make social interpretations on personalisation, 5IR technologies, and engagement. Data suggest a positive linear relationship between personalisation and engagement, the effects of which are exponentially driven by 5IR technologies that adapt, and scale presented content. Quantitative reviews prove that applying AI solutions to gain deep insights and automating the client experience enhances positive client attitudes. The research indicates that combining AI and automation increases customer interactions and exposes actual benefits for retail managers who want to advance the customer experience. Further research may examine other technologies within the 5IR framework and their role across retail and within distinct cultural and market settings.
This article presents a method for the classification of cracks in reinforced concrete structures using an artificial neural network classifiers ensemble. The proposed approach addresses common challenges in crack detection by utilizing unsupervised learning techniques, which eliminate the need for manual image annotation during the training process. The model architecture includes multiple classification stages, the number of which is determined empirically to optimize the precision of crack type recognition. One of the core strengths of this method lies in its use of a diverse ensemble of neural network classifiers. Each model contributes to the final decision through a weighted voting mechanism, resulting in a robust and highly accurate classification system. The methodology demonstrates improved diagnostic precision across various types of structural cracks. Experimental validation using the DeepCrack dataset confirms the effectiveness of the proposed approach. The ensemble model significantly outperforms individual classifiers, achieving high recognition accuracy. Moreover, the system’s flexibility allows it to be integrated into intelligent computer vision platforms for automated inspection and condition monitoring of infrastructure. Overall, the proposed method enhances the reliability and automation of structural assessment tasks and can be applied in a wide range of engineering and industrial applications related to visual crack analysis.
The study focuses on the role of user orientation and artificial intelligence (AI) incorporation concerning clients’ experience in New York’s banking sector in the 5IR context. There, the aim is placed on identifying how these strategies influence sustainable customer development for the institute. A quantitative collection method is used to complete a survey with 97 Customer Experience (CX) Managers from 73 diverse banks in New York City on customer feedback integration, recommendation systems, personalisation levels, etc. Consequently, regression analysis provided substantial evidence of the relationships between these variables. The study reveals that user involvement and AI are crucial in reimagining banking innovation in the 5IR era. Customer engagement is critical and can only be solved by customer feedback and AI personalisation. Future studies may examine the consequences of these approaches on customers’ satisfaction and their continued patronage of a firm.
