A well-functioning supply chain is a key to success for every business entity. Having an accurate projection on inventory offers a substantial competitive advantage. There are many internal factors like product introductions, distribution network expansion; and external factors such as weather, extreme seasonality, and changes in customer perception or media coverage that affects the performance of the supply chain. In recent years Artificial Intelligence (AI) has been proved to become an extension of our brain, expanding our cognitive abilities to levels that we never thought would be possible. Though many believe AI will replace humans, it is not true, rather it will help us to unleash our true strategic and creative potential. AI consists of a set of computational technologies developed to sense, learn, reason, and act appropriately. With the technological advancement in mobile computing, the capacity to store huge data on the internet, cloud-based machine learning and information processing algorithms etc. AI has been integrated into many sectors of business and been proved to reduce costs, increase revenue, and enhance asset utilization. AI is helping businesses to get almost 100% accurate projection and forecast the customer demand, optimizing their R&D and increase manufacturing with lower cost and higher quality, helping them in the promotion (identifying target customers, demography, defining the price, and designing the right message, etc.) and providing their customers a better experience. These four areas of value creation are extremely important for gaining competitive advantage. Supply-chain leaders use AI-powered technologies to a) make efficient designs to eliminate waste b) real-time monitoring and error-free production and c) facilitate lower process cycle times. These processes are crucial in bringing Innovation faster to the market.

Artificial Intelligence (AI) consists a set of computational technologies that designed to sense, learn, reason, and take action. AI has already been integrated in many applications including automating the business processes, gaining insight through data analysis, and engaging with customers and employees. AI enabled machines using deep learning algorithms has been shown to exceed human performance, particularly in visual tasks like playing Atari games, strategic board games like Go and object recognition etc. Furthermore, with the technological advancement in mobile computing, artificial neural networks, robotics, storage of huge data in internet, cloud-based machine learning and information processing algorithms etc. application of AI has been integrated in many sectors including transportation, service robots, health care, education, low-resource communities, public safety and security, employment and workplace, and entertainment etc. In this review, we have highlighted the recent trends and applications of AI in healthcare i.e. mining medical records (EHR), designing treatment plans, robotics mediated surgeries, medical management and supporting hospital operations etc. There are many AI platform has been designed which has also been proved to be instrumental in the development of innovative drugs. There is no doubt that AI will enhance the cooperation between humans and machines in years to come and will play an integral role in the improvement of the health index and quality of life.This work is licensed under Creative Commons Attribution 4.0 License ABEB.MS.ID.000503.

Photoacoustic imaging (PAI) is an emerging medical imaging modality with a steady growth in both pre-clinical and clinical applications. In linear-array PAI, beamformers play a significant role in the image reconstruction procedure. Generally, beamformers assume a point-like model for the elements of the array, and the elements are assumed to be omnidirectional. In this study, we investigated the effects of receiver element directivity on the Photoacoustic (PA) image formation procedure where delay-andsum (DAS) and delay-multiply-and-sum (DMAS) algorithms have been used as beamformers. The proposed method is evaluated experimentally (wire phantom and ex vivo imaging). It has been shown that the contribution of the directivity of the transducer elements improves the PA image quality. The results show that using the directivity, the sidelobes of the formed image (wire phantom) by DAS are attenuated about 20 %, compared to when directivity is not used. Moreover, signal-to-noise ratio achieved by DAS and DMAS is improved by about 12.5 % and 11.3 %, respectively.