Wyniki wyszukiwania - BazTech

1

Analiza możliwości zastosowania sztucznej inteligencji do grupowania profili dobowych zapotrzebowania na energię elektryczną w Krajowym Systemie Elektroenergetycznym (KSE)

Piotrowski Paweł, Trybułowska Ewelina

Elektro Info

|

2024

|

nr 4

34--40

PL

W artykule przedstawiono analizę statystyczną wieloletnich danych (wartości godzinowe zapotrzebowania na energię elektryczną) z KSE oraz analizę możliwości zastosowania sztucznej sieci neuronowej samoorganizującej się (Self Organizing Map) do podziału dobowych profili zapotrzebowania na energię elektryczną w KSE. Artykuł kończy podsumowanie oraz wnioski z wykonanych analiz statystycznych oraz badań związanych z zastosowaniem SOM do grupowania profili zapotrzebowania na energię.

EN

The article presents a statistical analysis of long-term data (hourly values of electricity demand) from the NPS and an analysis of the possibility of using a self-organizing artificial neural network (Self Organizing Map) to divide daily profiles of electricity demand in the NPS. The article concludes with a summary and conclusions from the conducted statistical analyses and studies related to the application of SOM for clustering electricity demand profiles.

2

Semantic web techniques for clinical topic detection in health care

Raman R., Sahayaraj Kishore Anthuvan, Soni Mukesh, Nayak Nihar Ranjan, Govindarajan Ramya, Singh Nikhil Kumar

Computer Assisted Methods in Engineering and Science

|

2024

|

Vol. 31, no. 2

139--155

EN

The scope of this paper is that it investigates and proposes a new clustering method thattakes into account the timing characteristics of frequently used feature words and thesemantic similarity of microblog short texts as well as designing and implementing mi-croblog topic detection and detection based on clustering results. The aim of the proposedresearch is to provide a new cluster overlap reduction method based on the divisions ofsemantic memberships to solve limited semantic expression and diversify short microblogcontents. First, by defining the time-series frequent word set of the microblog text, a fea-ture word selection method for hot topics is given; then, for the existence of initial clusters,according to the time-series recurring feature word set, to obtain the initial clustering ofthe microblog.

3

Machine Activity Recognition Using Clustering Method

Kęsek Marek, Ogrodnik Romuald, Podobińska-Staniec Marta

Inżynieria Mineralna

|

2023

|

no. 1

327--332

EN

Machine activity recognition is important for benchmarking and analysing the performance of individual machine, machine maintenance needs and automated monitoring of work progress. Additionally, it can be the basis for optimizing manufacturing processes. This article presents an attempt to use object clustering algorithms for recognizing the type of activity in the production complex. For this purpose, data from the production process and the k-means algorithm were used. The most common object clustering algorithms were also discussed. The results and the presented analysis approach demonstrate that this method can be successfully utilized in practice.

PL

Rozpoznawanie czynności realizowanych przez maszyny jest bardzo istotne dla porównania i analizy wydajności poszczególnych maszyn, potrzeb konserwacji maszyn oraz automatycznego monitorowania postępu prac. Dodatkowo, może być ono podstawą do optymalizacji realizowanych procesw produkcyjnych. W niniejszym artykule przedstawiono próbę wykorzystania algorytmów grupowania obiektów do rozpoznawania rodzaju aktywności kompleksu urabiającego. Do tego celu użyto danych pochodzących z procesu produkcyjnego oraz algorytmu k-means. Przyblizono także najpowszechniejsze algorytmy grupowania obiektów. Wyniki olraz zaprezentowany sposb przeprowadzania analizy pokazują, że taki sposob postępowania może być z powodzeniem wykorzystywany w praktyce.

4

Fuzzy-based firefly and ACO algorithm for densely deployed WSN

Sharma Tripti, Mohapatra Amar Kumar, Tomar Geetam

Computer Assisted Methods in Engineering and Science

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2023

|

Vol. 30, no. 2

223--246

EN

Most of the wireless sensor networks (WSNs) used in healthcare and security sectors are affected by the battery constraints, which cause a low network lifetime problem and prevents these networks from achieving their maximum performance. It is anticipated that by combining fuzzy logic (FL) approximation reasoning approach with WSN, the complex behavior of WSN will be easier to handle. In healthcare, WSNs are used to track activities of daily living (ADL) and collect data for longitudinal studies. It is easy to understand how such WSNs could be used to violate people’s privacy. The main aim of this research is to address the issues associated with battery constraints for WSN and resolve these issues. Such an algorithm could be successfully applied to environmental monitoring for healthcare systems where a dense sensor network is required and the stability period should be high.

5

Deciphering Clinical Narratives - Augmented Intelligence for Decision Making in Healthcare Sector

Dey Lipika, Jana Sudeshna, Dasgupta Tirthankar, Gupta Tanay

Annals of Computer Science and Information Systems

|

2023

|

Vol. 35

11--24

EN

Clinical notes that describe details about diseases, symptoms, treatments and observed reactions of patients to them, are valuable resources to generate insights about the effectiveness of treatments. Their role in designing better clinical decision making systems is being increasingly acknowledged. However, availability of clinical notes is still an issue due to privacy violation concerns. Hence most of the work done are on small datasets and neither the power of machine learning is fully utilized, nor is it possible to vaidate the models properly. With the availability of Medical Information Mart for Intensive Care (MIMIC-III v1.4) dataset for researchers though, the problem has been somewhat eased. In this paper we have presented an overview of our earlier work on designing deep neural models for prediction of outcomes and hospital stay for patients using MIMIC data. We have also presented new work on patient stratification and explanation generation for patient cohorts. This is early work targeted towards studying trajectories for treatment for different cohorts of patients, which can ultimately lead to discovery of low-risk models for individual patients to ensure better outcomes.

6

Rekomendacje jako wynik oceny preferencji na podstawie wskazanych przykładów

Kwiatkowski Włodzimierz

Przegląd Teleinformatyczny

|

2021

|

T. 9, Nr 1-4 (27)

3--16

PL

Rozpatrywany jest problem wyznaczania rekomendacji na podstawie wskazanych przykładów decyzji akceptowalnych i przykładów decyzji nieakceptowalnych. Wskazanie przez decydenta tych przykładów jest podstawą oceny jego preferencji. Istota przedstawionego rozwiązania polega na określeniu preferencji jako klastra wyznaczonego poprzez uzupełnianie wskazanych przykładów. W artykule zaproponowano procedurę kolejnych przybliżeń bazującą na rozwiązaniach zadania klasyfikacji na podstawie zadanych przykładów.

EN

The problem of determining a decision recommendation according to examples of acceptable decisions and examples of unacceptable decisions indicated by the decision-maker is considered in the paper. The decision-maker's examples are the foundation for assessing his preferences. The essence of the presented solution consists in determining the preferences of the decision-maker as a cluster designated by supplementing the indicated examples. The paper proposes a procedure of successive approximations based on the classification task according to given examples.

7

Fitting a Gaussian mixture model through the Gini index

López-Lobato Adriana Laura, Avendaño-Garrido Martha Lorena

International Journal of Applied Mathematics and Computer Science

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2021

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Vol. 31, no. 3

487--500

EN

A linear combination of Gaussian components is known as a Gaussian mixture model. It is widely used in data mining and pattern recognition. In this paper, we propose a method to estimate the parameters of the density function given by a Gaussian mixture model. Our proposal is based on the Gini index, a methodology to measure the inequality degree between two probability distributions, and consists in minimizing the Gini index between an empirical distribution for the data and a Gaussian mixture model. We will show several simulated examples and real data examples, observing some of the properties of the proposed method.

8

Miękkie grupowanie dla systemu kooperacyjnej detekcji

Cichoń Krzysztof

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

|

2020

|

nr 7-8

286--290, CD

PL

W artykule przedstawiono analizę efektywności kooperacyjnej detekcji sygnałów dla gwarantowanego poziomu jakości detekcji i minimalizacji zużycia energii. Kryteria uwzględnione w analizie są dwojakie: typ wymienianych wiadomości oraz metody grupowania węzłów (według ‘miękkich’ lub ‘twardych’ kryteriów). Wyniki przeprowadzonych symulacji wskazują, że przy gwarantowanym globalnym prawdopodobieństwie detekcji na poziomie 0.85, minimalny poziom energii jest gwarantowany dla algorytmu miękkiego grupowania.

EN

In the article the analysis of efficient cooperative signal detection is prepared with minimization of energy usage under the constraint of global probability of detection. Two types of criteria have been considered in the analysis: type of exchanged messages and type of grouping (conducted in ‘soft’ or ‘hard’ manner). Provided simulations have shown that for minimum global probability criterion of 0.85, the minimum energy usage is observed when soft grouping scheme is applied.

9

Learning from student browsing data on e-learning platforms: case study

Cansiz Sergen, Sudan Berk, Ogretici Emre, Siddik Aktas Mehmet

Annals of Computer Science and Information Systems

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2020

|

Vol. 22

37--44

EN

Interpretation of the behaviors of students in e-learning platforms with machine learning models has become an emerging need in recent years. Increase in the number of registered students on e-learning platforms is one of the reasons for choosing machine learning models. Tracking, modeling and understanding student activities gets more complex when the number of students is increased. This study is focusing modeling student activities on e-learning platforms with Complex Event Processing (CEP), Association Rule Mining (ARM) and Clustering methods based on distributed software architecture. Within the scope of this study, different modules that work real-time have been developed. An admin panel has been also developed in order to control all modules and track the student actions. Performance results of modules also obtained and evaluated on distributed system architecture.

10

Context Clustering-based Recommender Systems

Kannout Eyad

Annals of Computer Science and Information Systems

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2020

|

Vol. 21

85--91

EN

Recommender systems have gained lots of attention due to the rapid increase in the amount of data on the internet. Therefore, the demand for finding more advanced techniques to generate more useful recommendations becomes an urgent. The increasing need for generating more relevant recommendations led to the emergence of many novel recommendation systems, such as Context-aware Recommender System (CARS), which is based on incorporating the contextual information in recommendation systems. The goal of this paper is to propose new recommender systems that utilize the contextual information to find more relevant recommendations.

11

Cluster-based approach for successful solving real-world vehicle routing problems

Žunić Emir, Đonko Dženana, Šupić Haris, Delalić Sead

Annals of Computer Science and Information Systems

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2020

|

Vol. 21

619--626

EN

Vehicle routing problem as the generalization of the Travelling Salesman Problem (TSP) is one of the most studied optimization problems. Industry itself pays special attention to this problem, since transportation is one of the most crucial segments in supplying goods. This paper presents an innovative cluster-based approach for the successful solving of real-world vehicle routing problems that can involve extremely complex VRP problems with many customers needing to be served. The validation of the entire approach was based on the real data of a distribution company, with transport savings being in a range of 10-20 %. At the same time, the transportation routes are completely feasible, satisfying all the realistic constraints and conditions.

12

Recommendations as a result of decision evaluations based on reference examples

Kwiatkowski Włodzimierz

Przegląd Teleinformatyczny

|

2019

|

T. 7, Nr 1-2 (48)

1--23

EN

The problem of evaluation of decisions is considered, which evaluation consists in selecting from the set of possible decisions those that meet the decision-maker's preferences. The added value of solving this problem lies in the reduction of the number of decisions one can choose. Evaluation of decisions is based on their complete characteristics, rather than on a pre-defined quality indicator. The basis for the quality assessment are given pattern examples of decisions made. These are decisions that the decision maker has found to be exemplary or acceptable. They are used as defining his preferences. The methods proposed in this article concern the ordering and clustering of decisions based on their characteristics. The set of decisions selected by an algorithm is interpreted as recommended for the decision maker. Presented solutions can find a variety of applications, for example in investment planning, routing, diagnostics or searching through multimedia databases.

PL

Rozpatrywany jest problem ewaluacji decyzji polegający na wytypowaniu spośród możliwych decyzji tych, które spełniają preferencje decydenta. Użyteczność rozwiązania problemu polega na zredukowaniu liczby możliwych do wyboru decyzji. Ewaluacja decyzji bazuje na ich kompletnych charakterystykach, a nie na wcześniej zdefiniowanym wskaźniku jakości. Podstawą oceny jakości są wzorcowe przykłady decyzji. Są to decyzje, które decydent uznał za doskonałe lub akceptowalne. Wskazane przez decydenta przykłady są wykorzystywane jako określające jego preferencje. Proponowane w artykule metody dotyczą porządkowania i grupowania decyzji na podstawie ich charakterystyk. Wytypowany zbiór decyzji jest interpretowany jako rekomendowany dla decydenta. Przedstawione rozwiązania mogą znaleźć różnorakie zastosowania, np. w planowaniu inwestycji, trasowaniu, diagnostyce czy przeszukiwaniu multimedialnych baz danych.

13

Hybrid approach to energy efficient clustering for heterogeneous wireless sensor network using biogeography based optimization and k-means

Bhushan Shashi, Antoshchuk Svitlsna G., Teslenko Pavlo O., Kuzmenko Vasyl V., Wójcik Waldemar, Mamyrbaev Orken, Zhunissova Ulzhalgas

Przegląd Elektrotechniczny

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2019

|

R. 95, nr 4

138--141

EN

The paper presents the proposed protocol a hybrid approach is applied for clustering of sensor networks combining BBO and K-means algorithm. The performance of the protocol is compared with SEP, IHCR and ERP in terms of stability period, network life time, residual energy and throughput. The simulation results show that the proposed protocol named as KBBO has improved the performance of these parameters significantly.

PL

W pracy przedstawiono protokół, w którym stosuje się podejście hybrydowe do grupowania sieci czujników łączących algorytm BBO i K-średnich. Jego wydajność jest porównywana z SEP, IHCR i ERP pod względem okresu stabilności, żywotności sieci, energii resztkowej I przepustowości. Wyniki symulacji pokazują, że prezentowany protokół nazwany KBBO znacznie poprawił wydajność tych parametrów.

14

Inefficiency of data mining algorithms and its architecture: with emphasis to the shortcoming of data mining algorithms on the output of the researches

Tesema Workineh

Applied Computer Science

|

2019

|

Vol. 15, no 3

73--86

EN

This review paper presents a shortcoming associated to data mining algorithm(s) classification, clustering, association and regression which are highly used as a tool in different research communities. Data mining researches has successfully handling large amounts of dataset to solve the problems. An increase in data sizes was brought a bottleneck on algorithms to retrieve hidden knowledge from a large volume of datasets. On the other hand, data mining algorithm(s) has been unable to analysis the same rate of growth. Data mining algorithm(s) must be efficient and visual architecture in order to effectively extract information from huge amounts of data in many data repositories or in dynamic data streams. Data visualization researchers believe in the importance of giving users an overview and insight into the data distributions. The combination of the graphical interface is permit to navigate through the complexity of statistical and data mining techniques to create powerful models. Therefore, there is an increasing need to understand the bottlenecks associated with the data mining algorithms in modern architectures and research community. This review paper basically to guide and help the researchers specifically to identify the shortcoming of data mining techniques with domain area in solving a certain problems they will explore. It also shows the research areas particularly a multimedia (where data can be sequential, audio signal, video signal, spatio-temporal, temporal, time series etc) in which data mining algorithms not yet used.

15

Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

Puskarczyk Edyta

Acta Geophysica

|

2019

|

Vol. 67, no. 6

1991--2003

EN

Unconventional oil and gas reservoirs from the lower Palaeozoic basin at the western slope of the East European Craton were taken into account in this study. The aim was to supply and improve standard well logs interpretation based on machine learning methods, especially ANNs. ANNs were used on standard well logging data, e.g. P-wave velocity, density, resistivity, neutron porosity, radioactivity and photoelectric factor. During the calculations, information about lithology or stratigraphy was not taken into account. We apply different methods of classification: cluster analysis, support vector machine and artificial neural network—Kohonen algorithm. We compare the results and analyse obtained electrofacies. Machine learning method–support vector machine SVM was used for classification. For the same data set, SVM algorithm application results were compared to the results of the Kohonen algorithm. The results were very similar. We obtained very good agreement of results. Kohonen algorithm (ANN) was used for pattern recognition and identification of electrofacies. Kohonen algorithm was also used for geological interpretation of well logs data. As a result of Kohonen algorithm application, groups corresponding to the gas-bearing intervals were found. Analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in gas-saturated beds is present. It is concluded that ANN appeared to be a useful and quick tool for preliminary classification of members and gas-saturated identification.

16

Zastosowanie metod taksonomicznych do analizy zużycia energii elektrycznej przez poszczególne województwa

Tutak M.

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

|

2018

|

z. 117

675--686

PL

Najważniejszym czynnikiem wpływającym z jednej strony na rozwój gospodarczy i dobrobyt społeczeństwa, a z drugiej na łagodzenie skutków jego ubóstwa jest energia elektryczna i jej dostępność. Rozwijająca się gospodarka kraju generuje coraz większe zapotrzebowanie na energię. Poszczególne województwa Polski charakteryzują się różnym stopniem wykorzystania energii elektrycznej w podstawowych sektorach ekonomicznych. Wykorzystanie energii elektrycznej uzależnione jest od wielu czynników, m.in. od stopnia uprzemysłowienia regionu, lokalizacji elektrowni, a także od liczby ludności i gospodarstw domowych. W artykule przedstawiono wyniki analizy porównawczej zużycia energii elektrycznej w poszczególnych województwach Polski z uwzględnieniem sektorów ekonomicznych. Do uzyskania klasyfikacji województw w zakresie wykorzystania energii elektrycznej w sektorach ekonomicznych wykorzystano metodę analizy wielowymiarowej, która przyporządkowuje województwa do odpowiednich grup (skupień) o zbliżonej ilości zużycia energii elektrycznej.

EN

The most crucial factor influencing economic development and social well-being, as well as resulting in the mitigation of the effects of social poverty is electricity and its availability. The country’s growing economy generates an increasingly higher demand for energy. The individual Provinces of Poland have a different degree of electricity use in the basic economic sectors. The use of electricity is dependent on a number of factors, such as a given region’s degree of industrialisation, the locations of power plants, as well as the population and household numbers. The article presents the results of a comparative analysis of electricity consumption in the individual Provinces of Poland, with account being taken of the main economic sectors. The classification of the Provinces in terms of electricity use in economic sectors was performed by means of a multi-dimensional analysis method, which assigns the Provinces to appropriate groups (clusters) having similar quantities of electricity consumed.

17

Analysis and evaluation of grouping methods for effective cutting tool operation

Dobrzyński M., Przybylski W.

Journal of Machine Construction and Maintenance - Problemy Eksploatacji

|

2018

|

no. 1

53--61

EN

This article presents the possibilities for using cluster analysis in the assignment of machine tools in automated manufacturing systems. Based on the similarity of manufacturing processes in the system, cutting tools have been grouped. The objective was to obtain groups of similar objects, which could potentially ensure the reduction of the frequency and time of setups, optimizing the maintenance of tool resources and improving the efficiency and quality of production. With the application of similarity coefficients and hierarchical clustering algorithms, tool sets were formed with their composition specified. The assumed key factor was the limited tool magazine capacity for the machine tool. Therefore, it was necessary to separate the group with the largest multiplicity, not exceeding the assumed tool magazine capacity, from each group. The final part of the study includes an evaluation of the obtained solutions with selected measures used.

PL

W niniejszym artykule przedstawiono możliwości zastosowania analizy skupień w przydziale narzędzi do obrabiarek w zautomatyzowanych systemach wytwarzania. Bazując na podobieństwie używanych w systemie procesów wytwórczych grupowaniu, poddano narzędzia obróbkowe. Celem było uzyskanie grup obiektów podobnych, które potencjalnie zapewnić mogły zmniejszenie liczby i czasu przezbrojeń, lepsze wykorzystanie zasobu narzędziowego oraz poprawę efektywności i jakości produkcji. Z wykorzystaniem współczynników podobieństwa i hierarchicznych algorytmów grupowania stworzono zestawy narzędziowe i określono ich skład. Jako czynnik kluczowy przyjęto ograniczoną pojemność magazynu narzędziowego obrabiarki. Koniecznym stało się zatem wyodrębnienie z każdej możliwej liczby grup grupy o największej liczności, która nie przekraczała założonej pojemności magazynu narzędziowego. W ostatniej części opracowania przeprowadzono ocenę uzyskanych rozwiązań z wykorzystaniem wybranych miar.

18

The North Sea Bicycle Race ECG project : time-domain analysis

Długosz D., Eftestøl T., Królak A., Wiktorski T., Ørn S.

Journal of Automation Mobile Robotics and Intelligent Systems

|

2018

|

Vol. 12, No. 1

23--32

EN

Analysis of electrocardiogram and heart rate provides useful information about health condition of a patient. The North Sea Bicycle Race is an annual cycling competition in Norway. Examination of ECG recordings collected from participants of this race may allow defining and evaluating the relationship between physical endurance exercises and heart electrophysiology. Parameters reflecting potentially alarming deviations are to be identified in this study. This paper presents results of a time-domain analysis of ECG data collected in 2014, implementing K-Means clustering. A double stage analysis strategy, aimed at producing hierarchical clusters, is proposed. The first phase allows rough separation of data. Second stage is applied to reveal internal structure of the majority clusters. In both steps, discrepancies driving the separation could stem from three sources. Firstly, they could be signs of abnormalities in electrical activity of the heart. Secondly, they may allow discriminating between natural groups of participants – according to sex, age, physical fitness. Finally, some deviations could result from faults in data extraction, therefore serving in evaluation of the parameters. The clusters were defined predominantly by combinations of features: heartbeat signals correlation, P-wave shape, and RR intervals; none of the features alone was discriminative for all the clusters.

19

A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques

Nilashi M., Ibrahim O., Ahmadi H., Shahmoradi L., Farahmand M.

Biocybernetics and Biomedical Engineering

|

2018

|

Vol. 38, no. 1

1--15

EN

Parkinson's Disease (PD) is a progressive degenerative disease of the nervous system that affects movement control. Unified Parkinson's Disease Rating Scale (UPDRS) is the baseline assessment for PD. UPDRS is the most widely used standardized scale to assess parkinsonism. Discovering the relationship between speech signal properties and UPDRS scores is an important task in PD diagnosis. Supervised machine learning techniques have been extensively used in predicting PD through a set of datasets. However, the most methods developed by supervised methods do not support the incremental updates of data. In addition, the standard supervised techniques cannot be used in an incremental situation for disease prediction and therefore they require to recompute all the training data to build the prediction models. In this paper, we take the advantages of an incremental machine learning technique, Incremental support vector machine, to develop a new method for UPDRS prediction. We use Incremental support vector machine to predict Total-UPDRS and Motor-UPDRS. We also use Non-linear iterative partial least squares for data dimensionality reduction and self-organizing map for clustering task. To evaluate the method, we conduct several experiments with a PD dataset and present the results in comparison with the methods developed in the previous research. The prediction accuracies of method measured by MAE for the Total-UPDRSand Motor-UPDRS were obtained respectively MAE = 0.4656 and MAE = 0.4967. The results of experimental analysis demonstrated that the proposed method is effective in predicting UPDRS. The method has potential to be implemented as an intelligent system for PD prediction in healthcare.

20

Outlier mining using the DBSCAN algorithm

Nowak-Brzezińska A., Xięski T.

Journal of Applied Computer Science

|

2017

|

Vol. 25, nr 2

53--68

EN

This paper introduces an approach to outlier mining in the context of a real-world dataset containing information about the mobile transceivers operation. The goal of the paper is to analyze the influence of using different similarity measures and multiple values of input parameters for the densitybased clustering algorithm on the number of outliers discovered during the mining process. The results of the experiments are presented in section 4 in order to discuss the significance of the analyzed parameters.