Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

AI Democratization in Optometry: Developing a Prototype with Azure Cognitive Services Platform

As Artificial Intelligence (AI) technology advances, it is used in almost every aspect of our lives. However, AI is still complicated to implement without help from computer engineers. In the health care field, knowledge of medical and computer knowledge is necessary to create AI-based medical systems. Close cooperation between medical experts and computer experts is essential. For this reason, even if there has been a continuous effort to apply AI into the medical field, it has yet to be universalized. In particular, in the field of optometry and ophthalmology, more complex technology is required than in other medical fields because it is necessary to analyze an eye image to diagnose a disease. Therefore, this study explores the possibility for medical professionals with little computer knowledge in the field of ophthalmology to develop an AI-based diagnostic system without the help of computer engineers. In addition, it explores not only the possibilities but also the diagnostic accuracy of the developed system. Our results show that the diagnostic system discriminates against five common eye diseases to some extent. This study explores whether AI democratization is possible even in the field of ophthalmology that requires advanced skills and knowledge.

Encapsulation Video Classification and Retrieval Based on Arabic Text

Since Arabic video classification is not a popular field and there isn’t a lot of researches in this area especially in the educational field. A system was proposed to solve this problem and to make the educational Arabic videos more available to the students. A survey was fulfilled to study several papers in order to design and implement a system that classifies videos operative in the Arabic language by extracting its audio features using azure cognitive services which produce text transcripts. Several preprocessing operations are then applied to process the text transcript. A stochastic gradient descent SGD algorithm was used to classify transcripts and give a suitable label for each video. In addition, a search technique was applied to enable students to retrieve the videos they need. The results showed that SGD algorithm recorded the highest classification accuracy with 89.3 % when compared to other learning models. In the section below, a survey was introduced consisting of the most relevant and recent papers to this work.

AI-Based Chatbot to Solve Modern-Day Enterprise Business Problems

Communicating with employees through forums and emails has become an increasingly popular way for many multinational companies to provide human resource services in real time. Today, employee chat service agents are often replaced by conversational software agents or chatbots. These systems are designed to communicate with human users through natural language, generally based on artificial intelligence (AI). Time and cost saving opportunities have led to the widespread deployment of AI-based chatbots. Chatbots are one of the most basic and popular examples of human-computer intelligent interaction (HCI). Designed to convincingly simulate the way humans behave as dialogue partners. In the proposed system, we propose a chat robot that can dynamically respond to employee human resource queries. The proposed HR system is based on the Microsoft Cognitive Services chatbot. This Microsoft Teams-based platform provides a broad foundation of intelligence and is trained based on various data sets provided by the organization's HR.

Exploration of the Relationships Among Narcissism, Life Satisfaction, and Loneliness of Instagram Users and the High- and Low-Level Features of Their Photographs

We examined the associations between the characteristics of Instagram users and the features of their photographs. Narcissism, life satisfaction, and loneliness were employed for user variables and the features at high- (content) and low-levels (pixel) were employed to analyze the Instagram photographs. An online survey was conducted with 179 university students, and their Instagram photographs, 25,394 in total, were collected and analyzed. High-level features were extracted using Computer Vision and Emotion Application Programming Interfaces (APIs) in Microsoft Azure Cognitive Services, and low-level features were extracted utilizing the program written by the authors. The results of correlation analysis indicate that narcissism, life satisfaction, and loneliness were significantly associated with a part of photograph features at high- and low-levels. The results of the predictive analysis suggest that narcissism, loneliness in total, and social loneliness could be predicted with acceptable accuracy from Instagram photograph features, while characteristics such as life satisfaction, family loneliness, and romantic loneliness could not be predicted. Implications of this research and suggestions for further research were presented.

The remuneration of the community pharmacist in the developing world: the case in Indonesia

Introduction: The remuneration of pharmacist is critical to ensure sustainability of pharmacist services. There has been limited study about pharmacist remuneration in Indonesia. Aim: This study aims to investigate pharmacist remuneration system in Indonesia. Methods: A nationwide community pharmacy survey was conducted involving 7,000 pharmacies. Questions around remuneration models and amounts, types of incentives and other financial benefits structured the questionnaire. Descriptive analysis was used to evaluate the findings. Results: Of 2,087 pharmacists participated in the survey, only 1,952 respondents were recorded. More than half of respondents did not receive any particular fees designated to compensate provision of cognitive services. Fixed monthly salary predominantly formed the structure of remuneration system with less than half of the respondents received additional incentives to top up this monthly salary. Conclusion: The current remuneration system which mainly relies on monthly salary basis may not be sustainable to support provision of pharmacist-led cognitive services.

Artificial Intelligence based BOT to Manage and Administer Cloud Services

In the era of cloud computing, every company uses cloud technology for its applications and other infrastructure to provide a highly available and easily accessible user experience. While monitoring and managing these assets becomes a hectic work for the IT admins. On which the Level of Effort (LOE) of the resource allocated will be high and the resource must reach different console for different information. Introducing an AI-powered bot which can monitor and manage the cloud assets will reduce the manpower drastically. Most enterprises currently have very rudimentary systems of resource management where someone in the role of an Azure or resource administrator log on to the Admin Portal of their resources and have to apply filters and search through multiple screens to find even the most basic information regarding utilization and cost. This leads to inefficient management of resources and almost leads to overspending in resources that are being underutilized. The implementation of the project will involve creating a cloud services management bot that can be integrated with an enterprise’s collaboration suite as a way to enhance the enterprise’s modern workspace. The bot is to be trained on a set of query data as part of the artificial intelligence process using the natural language processing packages that are included in the Azure Cognitive Services suite. Once queries are processed, the system will connect with the respective endpoints of the Azure Resource Management REST APIs to retrieve relevant resource utilization information and show that to the end-user.