The engagement between customers and brands is being transformed by artificial intelligence (AI). However, there has been little study into AI-powered customer experiences; hence, this research aims to examine how the incorporation of AI in purchasing might result in a better AI-powered customer experience. This research will develop a conceptual model based on the service quality model and trust-commitment theory. Further to this, an online questionnaire was distributed to individuals who had utilised an AI-powered service provided by a particular brand, and consequently, a total of 354 responses were analysed using Structural Equation Modelling (SEM). The results that were deduced from the responses demonstrated that relationship commitment has begun to substantially impact AI-powered customer experiences. In addition to this, the results also revealed that perceived sacrifice and trust both play an important role in mediating the impacts of perceived convenience, personalisation, and AI-powered service quality. This finding contributes to the previous literature by highlighting the mediating impacts of perceived sacrifice and trust, as well as the significant influence of relationship commitment on AI-powered customer experience. Furthermore, the research poses significant implications for merchants who use AI in services provided to their customers.

Advancement of banking and financial investment has led to the rapid expansion of services automation. The consistent increase of Artificial Intelligence (AI) usage in investment management implies the impending popularity of technology-based service. This study examined influencer endorsement and perceived security benefits as moderators to the relationship between perceived risk and financial AI services. Questionnaires were disseminated to 300 respondents who were customers with experience of using financial AI services in Jordan, and they were chosen through purposive sampling method. Structural equation modeling run using Smart-partial least squares (PLS 3.3.6) was employed in analyzing the data obtained from 220 completed questionnaires. The results show that perceived risk negatively affects financial AI services, while influencer endorsement and perceived security moderate the relationship between perceived risk and financial AI services. This study provides insight to companies on how to reduce perceived risk to encourage people to use business intelligence applications, as in the use of financial technology services.

The computer numerical control (CNC) machines are chiefly used for the production of jobs with high accuracy and high speed. The CNC machining centers perform the machining operations according to the given program instructions which are commonly programmed by a CNC programmer. In this paper, a procedure to develop an automatic CNC program for machining of different types of holes by using different machine learning algorithms is developed. The machine learning algorithms namely support vector machine (SVM) and restricted boltzmann machine algorithm (RBM) with deep belief network (DBN) are used for the au-tomatic development of CNC machining programs of different types of holes. Initially, the position and other parameters of machining operations are identified and thereafter the CNC machining program is developed by using the MATLAB application. The automatically de-veloped CNC programs are tested on a CNC simulator. It is found that the application of RBM machine learning algorithm with DBN outperforms the SVM machine learning algo-rithm for the development of automatic CNC machining program for the machining of blind holes, through holes, counterbores and countersink operations.

As in today's era firms are looking to sustain while facing multiple challenges. Ultimately talented employees are the backbone of any firm that provides a sustainable competitive position at a global level. The major aspect is to make appropriate strategies to stay effective and efficient. Firms are focused on their strategies of recruitment and selection, training, and leadership capabilities to build up. A sample is collected from top-level management of the pharmaceutical sector sample data is of 320 Professionals from the different pharmaceutical sector of Karachi Pakistan. For Data collection Survey method is adopted with a close-ended Questionnaire. The study reveals the impact of artificial intelligence on competitive advantage. This research finds out certain strategies to be aligned with the mediation of Artificial intelligence to gain sustainable competitive advantage and serial mediation of the talent management process among the pharmaceutical sector of Karachi Pakistan. Talent management and Artificial Intelligence serial mediation aligned with HR practices to gain competitive advantage. As the study indicates that recruitment and selection are positively aligned with Artificial intelligence and serial mediation of talent management, further aspects of talent development and talent retention are directly linked with a competitive advantage as suitable components. This research merely focused on the pharmaceutical sector of Karachi Pakistan and the results generalized on the professionals of this particular sector, while new aspects of HR practices could be linked up with Artificial intelligence that will help to boost and find better insight into other industries.

Transmission Network Expansion Planning (TNEP) is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI) tools such as Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS) and Artificial Neural Networks (ANNs) are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs) and Harmony Search Algorithm (HSA) was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.

The FMCG sector in developing nations is still not prepared to withstand any disruption brought on by the worldwide pandemic. In order to adapt to the new normal, businesses must make both micro and broad changes to their supply chain strategy. The goal of this research is to create plans to minimize any disruptions caused by upcoming pandemics. To restore the broken supply chain, a number of new implications and adjustments to the current attributes were proposed in the areas of sourcing, manufacturing, and distribution. Finding the fundamental drivers that are frequently impacted by the disturbance is part of the technique. The afflicted locations were the focus of the models' development. The ideas work as preventative measures intended to thwart the disturbance when and if it happens. In order to assess the model's viability, the Key Performance Indicators (KPI) value was ultimately retrieved with the aid of 25 industry experts. These suggestions may result in improved transparency, real-time monitoring, cost effectiveness, and responsiveness, among other benefits. Our analysis indicates that the KPI scores for procurement, production, and distribution are 92.86%, 82.14%, and 87.50%, respectively. The models' total viability is 87.50%. The most recent Covid-19 pandemic has provided us with a vivid illustration of what could go wrong in such circumstances. In both pandemic and non-pandemic conditions, the adaptation of stated suggestions at the aspect of sourcing, production, and distribution might result in a significant shift to organization-wide activities.