Edge Intelligence for Empowering IoT-based Healthcare Systems

The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart healthcare systems, artificial intelligence (AI) provides the possibility of detecting and predicting high-risk diseases in advance, decreasing medical costs for patients, and offering efficient treatments. The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems. Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems. Additionally, the paper discusses issues and research directions arising when integrating these different technologies together.Comment: This paper has been accepted in IEEE Wireless Communication Magazin

Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function

Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.Вступ. Динамічна економна диспетчеризація викидів – це розширена версія традиційної задачі економної диспетчеризації викидів, в якій враховується коефіцієнт нарощування для межі генераторів в енергомережі. Призначення. Динамічна економна  диспетчеризація викидів розглядала питання економії та викидів як конкурентні цілі для оптимальних задач диспетчеризації, і для розв‘язання задачі потрібне певне вирішення конфліктів. Новизна. Метод прийняття рішень для розв‘язання задачі динамічної економної диспетчеризації викидів має мету для кожної цільової функції, для цього багатоцільова задача трансформується в оптимізацію однієї цілі за допомогою методу зваженої суми, а потім контролюється/розв‘язується за допомогою алгоритму оптимізації китів. Методологія. У цій роботі представлена нещодавно розроблена метаевристична методика, заснована на алгоритмі оптимізації китів для розв‘язання задачі динамічної економної диспетчеризації викидів. Основним натхненням для цієї методики оптимізації є той факт, що метаевристичні алгоритми стають популярними з кожним днем завдяки своїй простоті, відсутності вимог до інформації про градієнт, легкості обходу локальних оптимумів та можливості бути використаними для ряду інших задач. Цей алгоритм включає в себе всі можливі фактори, які забезпечать мінімальні вартість та викиди задачі динамічної економної диспетчеризації викидів для ефективної роботи генераторів в енергомережі. Запропонований підхід добре працює для розв‘язання задач і наближення рішення до найкращого оптимального. Результати. Запропонована стратегія перевірена шляхом моделювання на MATLAB® для 5 стандартних тестових систем IEEE. Чисельні результати демонструють можливості запропонованого алгоритму для встановлення оптимального рішення задачі динамічної економної диспетчеризації викидів за кілька прогонів. Запропонований алгоритм демонструє хорошу ефективність порівняно з нещодавно запропонованими алгоритмами, такими як багатоцільова нейронна мережа, навчена з використанням диференціальної еволюції, оптимізація рою частинок, еволюційне програмування, імітаційний відпал, пошук за шаблоном, багатоцільова диференціальна еволюція та багатоцільова гібридна диференціальна еволюція з імітаційним методом відпалу

HEMA: A Proposed Robot for Improving Healthcare Access in Underserved Communities

Abstract- Healthcare access is a major challenge in underserved communities, where people often face barriers such as distance, cost, and lack of transportation. HEMA (Horus Expert Medical Assistant Robot) is a new technology with the potential to revolutionize healthcare access in underserved communities by providing basic healthcare services on-site. HEMA is a mobile, affordable, and easy-to-use robot that can collect patient data, diagnose common diseases, and provide basic treatment.HEMA can address the challenges of healthcare access in underserved communities in a number of ways. First, HEMA can provide healthcare services to people who live in remote areas and who may not have access to a traditional healthcare facility. Second, HEMA can provide affordable healthcare services to people who may not be able to afford to pay for healthcare out-of-pocket or who may not have health insurance. Third, HEMA can provide healthcare services to people who may have difficulty traveling to a traditional healthcare facility due to a disability or lack of transportation.HEMA has the potential to make a significant impact on the future of healthcare delivery in underserved communities. By providing basic healthcare services on-site, HEMA can help to improve access to care, reduce disparities in health outcomes, and improve the overall health and well-being of people in underserved communitie

IoT and its business impact on remote monitoring of patients with chronic diseases in Germany

The goal of this dissertation is to determine the business impact of IoT and remote monitoring on patients with chronic diseases in Germany. Therefore, expert interviews with representatives of the four major affected stakeholder groups were conducted. These four groups consist of statutory health insurance companies, businesses, doctors and patients. The aim of these interviews was to assess the current status of IoT and remote monitoring in the German healthcare system and to find out about the main obstacles that currently keep the business impact at a low level. Although only representatives from the first three groups could be interviewed all interviewees agreed that IoT is in its early stages in Germany. The main obstacles impeding a significant growth of IoT and remote monitoring in Germany are identified as technological, regulatory, and cultural ones. Additionally, the self-governing structures of the German healthcare system and the multidisciplinary approach of already ongoing IoT projects complicate the diffusion of IoT solutions. Despite these barriers the interviewed experts are convinced that IoT and remote monitoring will prevail in Germany sooner or later.O objetivo desta dissertação é determinar o impacto económico da Internet das Coisas (IoT – Internet of Things) e monitorização remota dos pacientes com doenças crónicas na Alemanha. Portanto, foram conduzidas entrevistas com representantes dos quatro maiores grupos de intervenientes afetados. Estes quatro grupos consistem em seguradoras de saúde, negócios, médicos e pacientes. O objetivo destas entrevistas foi aferir o estado atual da IoT e monitorização remota no sistema de saúde Alemão e averiguar os principais obstáculos que mantêm atualmente um baixo nível de impacto económico. Apesar de apenas os representantes dos primeiros três grupos terem sido entrevistados, todos concordaram que a IoT está na sua fase inicial na Alemanha. Os maiores obstáculos que impedem um crescimento significativo da IoT e monitorização remota na Alemanha foram identificados como sendo tecnológicos, regulatórios e culturais. Para além do mais, as estruturas autónomas do sistema de saúde Alemão e a abordagem multidisciplinar dos projetos de IoT já em curso complicam a difusão de soluções IoT. Não obstante estas barreiras, os especialistas entrevistados estão convencidos que a IoT e monitorização remota vai prevalecer na Alemanha mais tarde ou mais cedo

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p