AI-driven approaches for optimizing the energy efficiency of integrated energy system

To decarbonize the global energy system and replace the unidirectional architecture of existing grid networks, integrated and electrified energy systems are becoming more demanding. Energy integration is critical for renewable energy sources like wind, solar, and hydropower. However, there are still specific challenges to overcome, such as their high reliance on the weather and the complexity of their integrated operation. As a result, this research goes through the study of a new approach to energy service that has arisen in the shape of data-driven AI technologies, which hold tremendous promise for system improvement while maximizing energy efficiency and reducing carbon emissions. This research aims to evaluate the use of data-driven AI techniques in electrical integrated energy systems, focusing on energy integration, operation, and planning of multiple energy supplies and demand. Based on the formation point, the main research question is: "To what extent do AI algorithms contribute to attaining greater efficiency of integrated grid systems?". It also included a discussion on four key research areas of AI application: Energy and load prediction, fault prediction, AI-based technologies IoT used for smart monitoring grid system optimization such as energy storage, demand response, grid flexibility, and Business value creation. The study adopted a two-way approach that includes empirical research on energy industry expert interviews and a Likert scale survey among energy sector representatives from Finland, Norway, and Nepal. On the other hand, the theoretical part was from current energy industry optimization models and a review of publications linked to a given research issue. The research's key findings were AI's significant potential in electrically integrated energy systems, which concluded AI's implication as a better understanding of energy consumption patterns, highly effective and precise energy load and fault prediction, automated energy management, enhanced energy storage system, more excellent business value, a smart control center, smooth monitoring, tracking, and communication of energy networks. In addition, further research directions are prospects towards its technical characteristics on energy conversion

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State

Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease’s development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers

AI Techniques for COVID-19

© 2013 IEEE. Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses

RESIST: an intelligent system to predict antibiotic resistance

Tese de mestrado, Engenharia Informática (Sistemas de Informação), Universidade de Lisboa, Faculdade de Ciências, 2015Os recentes avanços na tecnologia e poder computacional e o cada vez mais frequente uso de registos de saúde eletrónicos abriram as portas a novas pesquisas que exploram a informação destes registos para melhorar os cuidados médicos, nomeadamente nos diagnósticos e nas prescrições terapêuticas. Uma das maiores preocupações em termos de saúdes pública é a resistência a antibióticos. Este fenómeno ocorre quando algumas das subpopulações de um microrganismo sobrevivem após serem expostas a antibióticos, tornando-se mais difíceis de controlar. É, portanto, essencial utilizar antibióticos de uma forma mais eficaz. A Organização Mundial de Saúde já declarou publicamente que, a não ser que se consiga reduzir o rápido crescimento da resistência a antibióticos a que tem assistido, estamos a caminhar para uma era pós-antibióticos, onde a taxa de mortalidade por infeções comuns vai disparar devido à falha expectável de tratamentos médicos habituais. Hoje em dia, o antibiótico mais adequado apenas pode ser descoberto após os resultados dos testes dos laboratórios de análise serem conhecidos, então a maioria dos médicos fazem prescrições com base na sua experiência. No entanto, ao analisar um grande volume de dados clínicos, é possível que o pessoal clínico descubra informações mais relevantes que podem ajudá-los nas suas decisões. A equipe médica deve ter mais informações aquando da tomada de decisões. A análise computacional dos registos de saúde electrónicos representa uma oportunidade para combater a tendência crescente de resistência aos antibióticos, pois a nova informação descoberta pode auxiliar os médicos na tomada de melhores diagnósticos e prescrições. Isso poderia aumentar a qualidade da assistência médica, reduzindo não só a mortalidade e morbidade, mas também os custos. O objetivo deste projeto foi investigar se era possível desenvolver modelos de aprendizagem supervisionadas que fossem capazes de classificar os pacientes consoante o risco de resistência a antibióticos utilizando as informações que são geralmente recolhidas a nível clínico e laboratorial em termos de resistência aos antibióticos. O conjunto de dados que apoiaram este projecto foi gentilmente partilhado através de uma colaboração com o Laboratório de BIOFIG na FCUL, e representa dados reportados por vários hospitais portugueses em matéria de resistência aos antibióticos durante um período de 11 anos. Duas tarefas foram realizadas para cumprir os objectivos: pré-processamento dos dados e aprendizagem supervisionada. No pré-processamento dos dados foram usadas técnicas de limpeza, de estandardização e de transformação de dados, de modo a tornar os dados o mais consistente possível para que pudessem depois seguir para a aprendizagem supervisionada. Aqui foram aplicados métodos de aprendizagem automática sobre os dados para treinar um modelo capaz de prever a resistência aos antibióticos ao nível do paciente, com base em parâmetros demográficos, clínicos e laboratoriais. Numa primeira fase, a classificação de cada paciente como resistente ou não resistente a cada antibiótico foi realizada individualmente. Nela foram testados diversos algoritmos, como Decision Tables (DT), Random Forests (RF), Multilayer Perceptron (MP) e Support Vector Machines (SVM), sempre com validação cruzada com 10 subconjuntos. Foram ainda feitos testes com os filtros SMOTE a 200% e 500% e Spread Subsample com um rácio 1:1. Os resultados não foram satisfatórios, portanto os testes foram repetidos após se fazer uma avaliação sobre ganho de informação dos atributos, de modo se testar apenas sobre os atributos mais relevantes. No entanto, os resultados pouco melhoraram. Foi então compreendido que a formulação inicial do problema (uma classe para cada antibiótico) era provavelmente inadequada. Assim sendo, problema de classificação foi reformulado, desta feita seguindo para uma abordagem de classificação por perfil de resistência dos pacientes. Técnicas de agrupamento foram aplicadas sobre os dados para identificar perfis de resistência, ou seja, pacientes que apresentaram resistência ao mesmo conjunto de antibióticos. Após isso, uma estratégia de classificação de dois níveis foi concebida de forma a classificar os pacientes de acordo com o seu perfil de resistência. Para o primeira nível, a classificação filtrada, uma estratégia de classificação duas classes foi utilizada, em que todas as instâncias pertencentes a grupos de perfis resistentes foram agrupados numa única classe, enquanto que os restantes doentes sem qualquer resistência foram agrupados noutra classe distinta. A classificação filtrada foi sempre realizada com um filtro SMOTE com a percentagem a 500% e os algoritmos de classificação foram testados Decision Tables e Random Forests, com uma validação cruzada com 10 subconjuntos. Seguidamente, no segundo nível, as instâncias que foram classificadas como resistentes foram novamente separadas consoante os resultados da técnica de agrupamento anteriormente utilizada, classificadas via classificação multi-classe, para que o conjunto de dados multi-classe pudesse ser tratado por classificadores de 2 classes. Os algoritmos de classificação utilizados foram os mesmos que para o primeiro nível, apenas sem filtro, e os métodos utilizados para transformar o problema multi-classe em vários de 2 classes foram 1-contra-todos e 1-contra-1. Notou-se uma melhoria geral nos resultados, mas ainda com um desempenho bastante reduzido na maioria dos perfis. Outras duas abordagens foram feitas usando esta estratégia de classificação de dois níveis. Uma baseada numa classificação direta de instâncias em perfis de resistência, corrigindo algumas das atribuições erradas dos agrupamentos feitas pelo algoritmo de agrupamento, tendo as instâncias que foram erradamente colocadas num agrupamento sido realocadas. A outra, para além do reajustamento que acabou de ser explicado, continha ainda o número de instâncias pertencentes a cada agrupamento por mês. Novamente, apesar de terem sido notadas melhoras gerais, não eram suficientemente satisfatórias. Foram ainda realizadas previsões futuras sobre a evolução futura do número de pacientes resistentes por perfil de resistência recorrendo a séries temporais. Apesar dos resultados da classificação por perfil de resistência terem um baixo desempenho no geral, tiveram algum sucesso com o perfil onde os pacientes eram resistentes a Tetramicina e Cloranfenicol. Dadas as várias falhas detectadas a nível da qualidade dos dados (dados em falta, heterogeneidade de nomeações e categorias, número reduzido de pacientes resistentes para alguns antibióticos) é expectável que o desempenho para outros perfis possa aumentar, utilizando um conjunto de dados com maior qualidade e representatividade. Este projecto realçou dois aspectos importantes: a qualidade e representatividade dos dados recolhidos, pois após terem sido testadas várias abordagens diferentes e os resultados correspondentes analisados, foi determinado que a informação reportada não tinha a capacidade preditiva apropriada, pelo que não foi possível desenvolver o modelo anteriormente descrito; e a compreensão dos dados e do seu domínio, verificado quando se demonstrou que a classificação por perfil de resistência obteve melhores resultados que a classificação por antibiótico. Uma vez que os dados recolhidos cobrem um período de até há 10 anos, é expectável que com as recentes evoluções nos sistemas de informação de saúde empregues por hospitais portugueses, uma recolha de dados mais recentes iria fornecer dados de melhor qualidade. Seria assim interessante aplicar a estratégia proposta sobre dados mais recentes, e testar estes iriam de facto melhorar o desempenho da classificação.The recent advances in technology and computation power and the expanding use of electronic health records have opened new avenues of research that explore the information in these records to improve healthcare, namely in diagnosis and therapeutic prescriptions. One increasingly relevant public health concern is antibiotic resistance. The World Health Organization has already stated that unless the antibiotic resistance's growing trend is reduced, we are heading towards a post-antibiotic era, where the death rate of common infection will rise due to the expected failure of standard medical treatments. The ability to successfully predict antibiotic resistance risk can have a significant impact worldwide, because it can help clinicians in selecting appropriate antibiotics. This can help reduce antibiotic resistance levels, improve patient treatment, and ultimately decrease healthcare costs. This project's goal is to investigate if it is possible to develop supervised learning models that are able to classify patients regarding their antibiotic resistance risk using the information that has been usually collected at a clinical and laboratorial level and reported by Portuguese hospitals. This was accomplished by taking electronic health records data, pre-processing it using data cleaning, standardization and transformation techniques, and then applying machine learning methods to it to train a model capable of predicting antibiotic resistance at the patient level. The most successful classification strategy was based on a two-stage multi-class approach, where patients were classified into resistance profiles previously obtained using clustering techniques. Nevertheless, performance was still very low for most resistance profiles, no doubt influenced by the several issues in data quality detected. An improved collection of data, with fewer errors and other variables reported would likely have a great impact in performance

AI Techniques for COVID-19

© 2013 IEEE. Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses

Artificial intelligence applications in disease diagnosis and treatment: recent progress and outlook

The use of computers and other technologies to replicate human-like intelligent behaviour and critical thinking is known as artificial intelligence (AI).The development of AI-assisted applications and big data research has accelerated as a result of the rapid advancements in computing power, sensor technology, and platform accessibility that have accompanied advances in artificial intelligence. AI models and algorithms for planning and diagnosing endodontic procedures. The search engine evaluated information on artificial intelligence (AI) and its function in the field of endodontics, and it also incorporated databases like Google Scholar, PubMed, and Science Direct with the search criterion of original research articles published in English. Online appointment scheduling, online check-in at medical facilities, digitization of medical records, reminder calls for follow-up appointments and immunisation dates for children and pregnant women, as well as drug dosage algorithms and adverse effect warnings when prescribing multidrug combinations, are just a few of the tasks that already use artificial intelligence. Data from the review supported the conclusion that AI can play a significant role in endodontics, including the identification of apical lesions, classification and numbering of teeth, detection of dental caries, periodontitis, and periapical disease, diagnosis of various dental problems, aiding dentists in making referrals, and helping them develop more precise treatment plans for dental disorders. Although artificial intelligence (AI) has the potential to drastically alter how medicine is practised in ways that were previously unthinkable, many of its practical applications are still in their infancy and need additional research and development. Over the past ten years, artificial intelligence in ophthalmology has grown significantly and will continue to do so as imaging techniques and data processing algorithms improve

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

An Infectious Disease Prediction Method Based on K-Nearest Neighbor Improved Algorithm

With the continuous development of medical information construction, the potential value of a large amount of medical information has not been exploited. Excavate a large number of medical records of outpatients, and train to generate disease prediction models to assist doctors in diagnosis and improve work efficiency.This paper proposes a disease prediction method based on k-nearest neighbor improvement algorithm from the perspective of patient similarity analysis. The method draws on the idea of clustering, extracts the samples near the center point generated by the clustering, applies these samples as a new training sample set in the K-nearest neighbor algorithm; based on the maximum entropy The K-nearest neighbor algorithm is improved to overcome the influence of the weight coefficient in the traditional algorithm and improve the accuracy of the algorithm. The real experimental data proves that the proposed k-nearest neighbor improvement algorithm has better accuracy and operational efficiency

A Survey on Image Mining Techniques: Theory and Applications

Image mining is a vital technique which is used to mine knowledge straightforwardly from image. Image segmentation is the primary phase in image mining. Image mining is simply an expansion of data mining in the field of image processing. Image mining handles with the hidden knowledge extraction, image data association and additional patterns which are not clearly accumulated in the images. It is an interdisciplinary field that integrates techniques like computer vision, image processing, data mining, machine learning, data base and artificial intelligence. The most important function of the mining is to generate all significant patterns without prior information of the patterns. Rule mining has been adopting to huge image data bases. Mining has been done in accordance with the integrated collections of images and its related data. Numerous researches have been carried on this image mining. This paper presents a survey on various image mining techniques that were proposed earlier in literature. Also, this paper provides a marginal overview for future research and improvements. Keywords— Data Mining, Image Mining, Knowledge Discovery, Segmentation, Machine Learning, Artificial Intelligence, Rule Mining, Datasets

Forecasting Financial Distress With Machine Learning – A Review

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic