A Comparative Analysis of Machine Learning Models for Banking News Extraction by Multiclass Classification With Imbalanced Datasets of Financial News: Challenges and Solutions

Online portals provide an enormous amount of news articles every day. Over the years, numerous studies have concluded that news events have a significant impact on forecasting and interpreting the movement of stock prices. The creation of a framework for storing news-articles and collecting information for specific domains is an important and untested problem for the Indian stock market. When online news portals produce financial news articles about many subjects simultaneously, finding news articles that are important to the specific domain is nontrivial. A critical component of the aforementioned system should, therefore, include one module for extracting and storing news articles, and another module for classifying these text documents into a specific domain(s). In the current study, we have performed extensive experiments to classify the financial news articles into the predefined four classes Banking, Non-Banking, Governmental, and Global. The idea of multi-class classification was to extract the Banking news and its most correlated news articles from the pool of financial news articles scraped from various web news portals. The news articles divided into the mentioned classes were imbalanced. Imbalance data is a big difficulty with most classifier learning algorithms. However, as recent works suggest, class imbalances are not in themselves a problem, and degradation in performance is often correlated with certain variables relevant to data distribution, such as the existence in noisy and ambiguous instances in the adjacent class boundaries. A variety of solutions to addressing data imbalances have been proposed recently, over-sampling, down-sampling, and ensemble approach. We have presented the various challenges that occur with data imbalances in multiclass classification and solutions in dealing with these challenges. The paper has also shown a comparison of the performances of various machine learning models with imbalanced data and data balances using sampling and ensemble techniques. From the result, it’s clear that the performance of Random Forest classifier with data balances using the over-sampling technique SMOTE is best in terms of precision, recall, F-1, and accuracy. From the ensemble classifiers, the Balanced Bagging classifier has shown similar results as of the Random Forest classifier with SMOTE. Random forest classifier's accuracy, however, was 100% and it was 99% with the Balanced Bagging classifier

The BG News January 30, 1992

The BGSU campus student newspaper January 30, 1992. Volume 74 - Issue 85https://scholarworks.bgsu.edu/bg-news/6321/thumbnail.jp

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Opportunities and Challenges for Implementing Smart City Solutions in Finnish Municipalities : Viewpoint of Sustainable Transportation

Global trend of accelerated urbanization has caused challenges for the transportation systems of cities. A smart city’s smart transportation solutions are now thriving to solve different defects and develop the overall transportation system operation in cities. The smart transportation solutions have been considered as a robust part of developing the future transportation system in cities more efficient and sustainable. Internet of things has been a remarkable rendering factor on the development of these new smart solutions. The thesis examines the opportunities and challenges for implementing smart city solutions in Finnish municipalities from the viewpoint of sustainable transportation. The research question of the thesis is “What are the main opportunities and challenges for Finnish municipalities when implementing smart city solutions for sustainable transportation?” Conceptual framework of the thesis is based on literature of recent international studies which were systematically selected. The literature review consists of reviewing the concept of smart city and sustainability dimensions of it, concept of smart transportation and the sustainability concerns of it, and internet of things in the smart transportation. In addition, contemporary state of smart transportation development in Finnish municipalities is reviewed. Methodology of this thesis was conducted through semi-structured interviews with five different experts of smart transportation sector in Finland. The interviewees were from different operators and organizations which offered versatile and comprehensive view of the topic and gave high quality answers to the research question and complemented the research objectives. Based on this study, the contemporary state of smart transportation development in Finnish municipalities is still in tentative stage even though there has been implemented concrete solutions already. However, the overall atmosphere towards smart transportation development is affirmative and there is knowledge of the possible opportunities of utilizing different smart technologies. Main opportunities for implementing smart transportation solutions for Finnish municipalities were to promote a change in modal split, to promote a change of the power source of vehicles, and to make the city transportation more efficient and safer for citizens. The main challenges of implementing smart transportation were that the solutions need to be rather far developed before they are capable of functioning in the transportation ecosystem, to get these solutions fitted in to the budgets of municipalities, when assembling new sensors there can be difficulties to acquire a power source, and business models for the new solutions can be challenging to develop. Kaupungistumisen kiihtyvä maailmanlaajuinen trendi on aiheuttanut haasteita kaupunkien liikennöinnin järjestämiseen. Älykkäiden kaupunkien älykkäät liikenneratkaisut pyrkivät nyt ratkaisemaan erilaisia haasteita ja kehittämään liikennejärjestelmiä kaupungeissa. Älykkäitä liikennejärjestelmiä on luonnehdittu merkittäväksi tekijäksi, jotta tulevaisuuden kaupunkien liikennejärjestelmistä saadaan tehokkaampia ja kestävämpiä. Esineiden internet on ollut tärkeä osa älykkäiden ratkaisujen läpimurrossa ja nopeassa kehityksessä. Tämä tutkielma tutkii älykkään liikenteen ratkaisujen käyttöönoton mahdollisuuksia ja haasteita Suomen kunnissa. Tutkielman tutkimuskysymys on: ”Mitkä ovat pääasialliset mahdollisuudet ja haasteet Suomen kunnissa liittyen älykkään kaupungin ratkaisujen käyttöönottoon näkökulmana kestävä liikenne?” Tutkielman teoreettinen viitekehys perustuu kansainvälisiin akateemisiin tutkimuksiin, jotka koskevat älykästä kaupunkia ja sen kestävyyden ulottuvuuksia, älykästä liikennettä ja kestävyyteen liittyviä seikkoja ja esineiden internetiä. Sen lisäksi eri saatavilla olevien eri toimijoiden julkaisemien lähteiden perusteella on tutkittu Suomen kuntien älykkäisiin kaupunkeihin liittyvien ratkaisujen kehitystä ja älykkään liikenteen kehityksen nykytilaa. Tutkielman tutkimusosa koostuu puolistrukturoiduista haastatteluista, joissa on haasteltu viittä eri älykkään liikenteen asiantuntijaa eri sektoreilta. Koska haastateltavat olivat eri sektoreilta, saatiin tutkimustavoitteiden ja -kysymyksen kannalta monipuolinen ja kokonaisvaltainen katsanto. Tutkielman perusteella älykkään liikenteen kehityksen nykytila Suomen kunnissa on edelleen pitkälti kokeellinen, vaikkakin erilaisia ratkaisuja on jo otettu käyttöön monissa kunnissa. Yleinen ilmapiiri älykkäitä liikenneratkaisuja kohtaan on positiivinen ja kunnissa ollaan tietoisia älykkääseen liikenteeseen liittyvien ratkaisujen mahdollisuuksista. Pääasialliset mahdollisuudet älykkään liikenteen ratkaisujen käyttöönotossa oli tutkielman perustella niiden hyödyntäminen kulkumuotojakauman muuttamisessa, ajoneuvojen käyttövoimajakauman muuttamisessa ja liikennejärjestelmän kokonaisvaltaisessa kehittämisessä tehokkaammaksi ja turvallisemmaksi. Sen sijaan yksi pääasiallisista haasteista liittyi siihen, että uusien älykkään liikenteen ratkaisujen pitää olla melko pitkälle kehitettyjä, jotta ne voidaan ottaa osaksi liikennöinnin ekosysteemiä muun muassa eri lait ja standardit huomioiden. Lisäksi haasteiksi on koettu älykkään liikenteen ratkaisuihin vaadittavien resurssien sisällyttäminen osaksi kuntien budjetointia, uusien sensoreiden sähkön saanti ja uusien älykkäiden liikenneratkaisujen bisnesmallien rakentaminen toimivaksi

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)

The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field

Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks

Authorship classification is a method of automatically determining the appropriate author of an unknown linguistic text. Although research on authorship classification has significantly progressed in high-resource languages, it is at a primitive stage in the realm of resource-constraint languages like Bengali. This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. For this purpose, this work develops a new embedding corpus (named WEC) and a Bengali authorship classification corpus (called BACC-18), which are more robust in terms of authors’ classes and unique words. Using three text embedding techniques (Word2Vec, GloVe and FastText) and combinations of different hyperparameters, 90 embedding models are created in this study. All the embedding models are assessed by intrinsic evaluators and those selected are the 9 best performing models out of 90 for the authorship classification. In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD). Among the models, the optimized CNN with GloVe model achieved the highest classification accuracies of 93.45%, 95.02%, and 98.67% for the datasets BACC-18, BAAD16, and LD, respectively

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Cybersecurity of Industrial Cyber-Physical Systems: A Review

Industrial cyber-physical systems (ICPSs) manage critical infrastructures by controlling the processes based on the "physics" data gathered by edge sensor networks. Recent innovations in ubiquitous computing and communication technologies have prompted the rapid integration of highly interconnected systems to ICPSs. Hence, the "security by obscurity" principle provided by air-gapping is no longer followed. As the interconnectivity in ICPSs increases, so does the attack surface. Industrial vulnerability assessment reports have shown that a variety of new vulnerabilities have occurred due to this transition while the most common ones are related to weak boundary protection. Although there are existing surveys in this context, very little is mentioned regarding these reports. This paper bridges this gap by defining and reviewing ICPSs from a cybersecurity perspective. In particular, multi-dimensional adaptive attack taxonomy is presented and utilized for evaluating real-life ICPS cyber incidents. We also identify the general shortcomings and highlight the points that cause a gap in existing literature while defining future research directions.Comment: 32 pages, 10 figure

Electronics and Its Worldwide Research

The contributions of researchers at a global level in the journal Electronics in the period 2012–2020 are analyzed. The objective of this work is to establish a global vision of the issues published in the Electronic magazine and their importance, advances and developments that have been particularly relevant for subsequent research. The magazine has 15 thematic sections and a general one, with the programming of 385 special issues for 2020–2021. Using the Scopus database and bibliometric techniques, 2310 documents are obtained and distributed in 14 thematic communities. The communities that contribute to the greatest number of works are Power Electronics (20.13%), Embedded Computer Systems (13.59%) and Internet of Things and Machine Learning Systems (8.11%). A study of the publications by authors, affiliations, countries as well as the H index was undertaken. The 7561 authors analyzed are distributed in 87 countries, with China being the country of the majority (2407 authors), followed by South Korea (763 authors). The H-index of most authors (75.89%) ranges from 0 to 9, where the authors with the highest H-Index are from the United States, Denmark, Italy and India. The main publication format is the article (92.16%) and the review (5.84%). The magazine publishes topics in continuous development that will be further investigated and published in the near future in fields as varied as the transport sector, energy systems, the development of new broadband semiconductors, new modulation and control techniques, and more