Wyniki wyszukiwania - BazTech

1

TS-based Mobility-aware Multi-hierarchical Caching Model with Vehicle Clustering and Content Popularity Prediction

Baghiani Radouane, Guezouli Lyamine, Korichi Ahmed

Journal of Telecommunications and Information Technology

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2024

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nr 3

65--78

EN

This research is concerned with the fusion of artificial intelligence (AI) and machine learning within multi-hierarchical caching systems, specifically targeting vehicular and edge caching domains. This study introduces an innovative architecture harmonizing Thompson sampling learning-based caching policies with advanced vehicle clustering and content-popularity prediction methods (TS-MMCM). Simulations show substantial performance improvements and a big impact of the proposed approach on system efficiency in dynamic network environments. The proposal demonstrates a notable gain in cache hit rates and decreased latency levels, highlighting the potential of AI to improve caching techniques in dynamic network environments.

2

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

Piotrowski Paweł, Trybułowska Ewelina

Elektro Info

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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.

3

Generalizing clustering inferenceswith ml augmentation ofordinal survey data

Kumar Bhupendera, Kumar Rajeev

Computer Science

|

2024

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T. 25 (1)

63--93

EN

In this paper, we attempt to generalize the ability to achieve quality inferences of survey data for a larger population through data augmentation andunification. Data augmentation techniques have proven effective in enhancingmodels’ performance by expanding the dataset’s size. We employ ML dataaugmentation, unification, and clustering techniques. First, we augment thelimitedsurvey data size using data augmentation technique(s). Second, wecarry out data unification, followed by clustering for inferencing. We took twobenchmark survey datasets to demonstrate the effectiveness of augmentationand unification. The first dataset contains information on aspiring studententrepreneurs’ characteristics, while the second dataset comprises survey datarelated to breast cancer. We compare the inferences drawn from the originalsurvey data with those derived from the transformed data using the proposedscheme. The results of this study indicate that the machine learning approach,data augmentation with the unification of data followed by clustering, can bebeneficial for generalizing the inferences drawn from the survey data.

4

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

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2024

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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.

5

Attacks against communication and computing task classification and mitigation techniques

Younes Salmi, Bogucka Hanna

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

|

2023

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nr 4

186--189

EN

Machine learning-based classification algorithms allow communication and computing (2C) task allocation to network edge servers. This article considers poisoning of classifiable 2C data features in two scenarios: noise-like jamming and targeted data falsification. These attacks have a fatal effect on classification in the feature areas with unclear decision boundary. We propose training and noise detection using the Silhouette Score to detect and mitigate attacks. We demonstrate effectiveness of our methods.

PL

Algorytmy klasyfikacji oparte na uczeniu maszynowym umożliwiają zoptymalizowaną alokację zadań telekomunikacyjnych i obliczeniowych (2C) do serwerów brzegowych sieci. W artykule omówiono ataki zatruwające, które mają negatywny wpływ na klasyfikację zadań 2C w obszarach, w których granica decyzyjna jest niejasna. Proponujemy metodę trenowania modelu oraz wykorzystanie testu Silhouette do wykrywania i unikania ataków. Wykazujemy skuteczność tych metod wobec rozważanych ataków.

6

Performance Evaluation of a Zone-based Three-level Heterogeneous Clustering Protocol for WSNs

Rao Sanapala Shanmukha, Shilpi, Kumar Arvind

Journal of Telecommunications and Information Technology

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2023

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nr 3

85--93

EN

This paper proposes a zone-based three-level heterogeneous clustering protocol (ZB-TLHCP) for heterogeneous WSNs. In ZB-TLHCP, the sensor field/region is divided into zones where super, advance, and normal nodes are deployed uniformly and randomly. The performance of the proposed ZB-TLHCP system is compared with that of zonal-stable election protocol (Z-SEP), distributed energy efficient clustering (DEEC), and threshold-based DEEC (TDEEC) protocol by varying the number of super and advance nodes, their energy levels for the fixed sensor field, and the total number of nodes. Matlab simulation results revealed that the proposed ZB-TLHCP solution performed better than Z-SEP, DEEC, and TDEEC protocols, as it increased the instability period, prolonged the network's lifetime, and achieved higher throughput values.

7

An Efficient Controller Placement Algorithm using Clustering in Software Defined Networks

Jacob Joshua, Shinde Sumedha, Narayan D. G.

Journal of Telecommunications and Information Technology

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2023

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nr 4

9--17

EN

Software defined networking (SDN) is an emerging network paradigm that separates the control plane from data plane and ensures programmable network management. In SDN, the control plane is responsible for decision-making, while packet forwarding is handled by the data plane based on flow entries defined by the control plane. The placement of controllers is an important research issue that significantly impacts the performance of SDN. In this work, we utilize clustering techniques to group networks into multiple clusters and propose an algorithm for optimal controller placement within each cluster. The evaluation involves the use of the Mininet emulator with POX as the SDN controller. By employing the silhouette score, we determine the optimal number of controllers for various topologies. Additionally, to enhance network performance, we employ the meeting point algorithm to calculate the best location for placing the controller within each cluster. The proposed approach is compared with existing works in terms of throughput, delay, and jitter using six topologies from the Internet Zoo dataset.

8

Clustering Analysis of Soil Environmental Quality for Perennial Crop Recommendations in Vinh Long Province, Vietnam

Minh Vo Quang, Vu Pham Thanh, Khoa Le Van, Tinh Tran Kim, Dang Pham Cam

Journal of Ecological Engineering

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2023

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Vol. 24, nr 8

343--352

EN

The study aimed to evaluate the soil environmental characteristics of Vinh Long Province’s perennial crop-growing area using principal component analysis (PCA) and cluster analysis (CA). Soil environmental quality data were collected in eight districts of Vinh Long province for 27 physical and chemical parameters. CA and PCA analysis was used to group and identify critical parameters affecting perennial crops’ soil environment. The findings demonstrate low to moderate soil compaction porosity, buffering capacity, and structure for perennial crops. In addition, the soil has a low pH, electrical conductivity, total soluble salts, aluminum, and cation exchange capacity. Although rich in nutrients, the content of organic matter, available phosphorus, cations, and trace elements is only low to moderate. CA results showed three districts suitable for strongly developing perennial crops: Tra On, Mang Thit, and Vung Liem. The PCA results showed that except for density, the buffer capacity of the soil, and dissolved Al3+, the upcoming monitoring program must incorporate all remaining criteria. The study’s findings offer crucial information to help the management organization devise strategies for enhancing and sustainably expanding perennial crops in the province. It is necessary to further evaluate the soil’s environmental quality over time and soil depth and determine the frequency of monitoring in the study area.

9

Finding playing styles of badminton players using firefly algorithm-based clustering algorithms

Ilankoon I.M.T.P.K., Samarasinghe U.S., Ariyaratne M.K.A., Silva R. M.

Computer Science

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2023

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T. 24 (3)

427--450

EN

Cluster analysis can be defined as applying clustering algorithms with the goal of finding any hidden patterns or groupings in a data set. Different clustering methods may provide different solutions for the same data set. Traditional clustering algorithms are popular, but handling big data sets is beyond the abilities of such methods. We propose three big data clustering methods basedon the firefly algorithm (FA). Three different fitness functions were definedon FA using inter-cluster distance, intra-cluster distance, silhouette value, and the Calinski-Harabasz index. The algorithms find the most appropriate cluster centers for a given data set. The algorithms were tested with nine popular synthetic data sets and one medical data set and are later applied on two badminton data sets with the intention of identifying the different playing styles of players based on their physical characteristics. The results specify that the firefly algorithm could generate better clustering results with high accuracy. The algorithms cluster the players to find the most suitable playing strategy for a given player where expert knowledge is needed in labeling the clusters. Comparisons with a PSO-based clustering algorithm (APSO) and traditional algorithms point out that the proposed firefly variants work in a similar fashion as the APSO method, and they surpass the performance of traditional algorithms.

10

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

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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.

11

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

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2023

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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.

12

K-means is probabilistically poor

Kłopotek Mieczysław

Studia Informatica : systems and information technology

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2022

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Vol. 2(27)

5--26

EN

Kleinberg introduced the concept of k-richness as a requirement for an algorithm to be a clustering algorithm. The most popular algorithm k means dos not fit this definition because of its probabilistic nature. Hence Ackerman et al. proposed the notion of probabilistic k-richness claiming without proof that k-means has this property. It is proven in this paper, by example, that the version of k-means with random initialization does not have the property probabilistic k-richness, just rebuking Ackeman's claim.

13

Light Weight Clustered Trust Sensing Mechanism for Internet of Things Network

Rajendra Prasad M., Krishna Reddy D.

International Journal of Electronics and Telecommunications

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2022

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Vol. 68, No. 1

5--12

EN

Internet of Things (IoT) is the new research paradigm which has gained a great significance due to its widespread applicability in diverse fields. Due to the open nature of communication and control, the IoT network is more susceptible to several security threats. Hence the IoT network requires a trust aware mechanism which can identify and isolate the malicious nodes. Trust Sensing has been playing a significant role in dealing with security issue in IoT. A novel a Light Weight Clustered Trust Sensing (LWCTS) model is developed which ensures a secured and qualitative data transmission in the IoT network. Simulation experiments are conducted over the proposed model and the performance is compared with existing models. The obtained results prove the effectiveness when compared with existing approaches.

14

KFOA: K-mean Clustering, Firefly Based Data Rate Optimization and ACO Routing for Congestion Control in WSN

Jadhav Savita Sandeep, Jadhav Sangeeta

International Journal of Electronics and Telecommunications

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2022

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Vol. 68. No. 4

659--665

EN

Wireless sensor network (WSN) is assortment of sensor nodes proficient in environmental information sensing, refining it and transmitting it to base station in sovereign manner. The minute sensors communicate themselves to sense and monitor the environment. The main challenges are limited power, short communication range, low bandwidth and limited processing. The power source of these sensor nodes are the main hurdle in design of energy efficient network. The main objective of the proposed clustering and data transmission algorithm is to augment network performance by using swarm intelligence approach. This technique is based on K-mean based clustering, data rate optimization using firefly optimization algorithm and Ant colony optimization based data forwarding. The KFOA is divided in three parts: (1) Clustering of sensor nodes using K-mean technique and (2) data rate optimization for controlling congestion and (3) using shortest path for data transmission based on Ant colony optimization (ACO) technique. The performance is analyzed based on two scenarios as with rate optimization and without rate optimization. The first scenario consists of two operations as k-mean clustering and ACO based routing. The second scenario consists of three operations as mentioned in KFOA. The performance is evaluated in terms of throughput, packet delivery ratio, energy dissipation and residual energy analysis. The simulation results show improvement in performance by using with rate optimization technique.

15

Methods for detecting and selecting areas on texture biomedical images of breast cancer

Orazayeva Ainur, Tussupov Jamalbek, Wójcik Waldemar, Pavlov Sergii, Abdikerimova Gulzira, Savytska Liudmyla

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2022

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T. 12, nr 2

69--72

EN

This paper is devoted to topical issues - the development of methods for analyzing texture images of breast cancer. The main problem that is resolved in the article is that the requirements for the results of pre-processing are increasing. As a result of the task, images of magnetic resonance imaging of the breast are considered for image processing using texture image analysis methods. The main goal of the research is the development and implementation of algorithms that allow detecting and isolating a tumor in the breast in women in an image. To solve the problem, textural features, clustering, orthogonal transformations are used. The methods of analysis of texture images of breast cancer, carried out in the article, namely: Hadamard transform, oblique transform, discrete cosine transform, Daubechies transform, Legendre transform, the results of their software implementation on the example of biomedical images of oncological pathologies on the example of breast cancer, it is shown that The most informative for image segmentation is the method based on the Hadamard transform and the method based on the Haar transform. The article presents recommendations for using the results in practice, namely, it is shown that clinically important indicators that make a significant contribution to assessing the degree of pathology and the likelihood of developing diseases, there are other information parameters: diameter, curvature, etc. Therefore, increased requirements for the reliability, accuracy, speed of processing biomedical images.

PL

Niniejszy artykuł jest poświęcony aktualnemu tematowi - opracowaniu metod analizy obrazów tekstury raka piersi. Główny problem, który został rozwiązany w artykule, polega na tym, że wymagania wobec wyników przetwarzania wstępnego są coraz większe. W wyniku realizacji zadania rozpatrzono obrazy rezonansu magnetycznego piersi przeznaczone do przetwarzania metodami teksturowej analizy obrazu. Głównym celem badań jest opracowanie i wdrożenie algorytmów wykrywania i odróżniania na obrazie guza w piersi u kobiet. Do rozwiązania tego problemu wykorzystuje się cechy tekstury, grupowanie i transformacje ortogonalne. W artykule przedstawiono metody analizy obrazów teksturowych raka piersi, t j. transformatę Adamarda, transformatę skośną, transformatę dyskretno-cosinusową, transformatę Dobeshiego, transformatę Lejandre'a, oraz wyniki ich implementacji programowej na przykładzie obrazów biomedycznych patologii onkologicznej w przypadku raka piersi. Metoda oparta na transformacie Adamarda oraz metoda oparta na transformacie Haara są najbardziej przydatne do segmentacji obrazów. W artykule przedstawiono zalecenia dotyczące wykorzystania wyników w praktyce, a mianowicie wykazano, że inne parametry informacyjne, takie jak średnica, krzywizna itp. są ważnymi klinicznie wskaźnikami, które w istotny sposób przyczyniają się do oceny stopnia patologii i prawdopodobieństwa rozwoju choroby. W związku z tym wzrastają wymagania dotyczące niezawodności, dokładności i szybkości przetwarzania obrazów biomedycznych w urządzeniach diagnostycznych.

16

Clustering of data represented by pairwise comparisons

Dvoenko Sergey

Control and Cybernetics

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2022

|

Vol. 51, No. 3

343--387

EN

In this paper, experimental data, given in the form of pairwise comparisons, such as distances or similarities, are considered. Clustering algorithms for processing such data are developed based on the well-known k-means procedure. Relations to factor analysis are shown. The problems of improving clustering quality and of finding the proper number of clusters in the case of pairwise comparisons are considered. Illustrative examples are provided.

17

Bank Loan Analysis using Data Mining Techniques

Trinh Thi-Nhi, Nguyen Hoang-Diep

Annals of Computer Science and Information Systems

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2022

|

Vol. 33

295--298

EN

Nowadays, a bank loan can provide people with cash to fund home improvements or start a business. However, some customers who are accepted with a loan cannot repay or someone usually repays in a delayed time. Therefore, to minimize losses, examining loan applications is particularly evident for the bank. This paper study on bank loan analysis using data mining techniques. We use association rules mining, clustering, and classification techniques on the applicant's profile to help the bank quickly decide for a loan applicant.

18

Rekomendacje jako wynik oceny preferencji na podstawie wskazanych przykładów

Kwiatkowski Włodzimierz

Przegląd Teleinformatyczny

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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.

19

Prospects for the Reorientation of Investment Flows for Sustainable Development under the Influence of the COVID-19 Pandemic

Prokopenko Olha, Toktosunova Cholpon, Sharsheeva Nurmira, Zablotska Rita, Mazurenko Valerii, Halaz Lina

Problemy Ekorozwoju

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2021

|

Vol. 16, nr 2

7--17

EN

The article is devoted to substantiating the expediency of reorienting international investment flows, under the influence of the COVID-19 pandemic, from traditional directions to projects related to social transformation. It is proved that such transformations should be expressed first of all in qualitative changes in education, medicine and employment. Particular attention is paid to the modernization of the paradigm of sustainable development, the components of which should be ranked from social to environmental. The necessity of interpretation of investment strategies implemented in the countries following their common problems is substantiated. Also, attention is paid to the substantiation of the cyclical component, its role in the redistribution of investment flows at the state level. The article proposed cluster investment to solve this problem.

PL

Artykuł poświęcony jest uzasadnieniu celowości przeorientowania międzynarodowych przepływów inwestycyjnych pod wpływem pandemii COVID-19 z tradycyjnych kierunków na projekty związane z transformacją społeczną. Udowodniono, że takie przemiany powinny wyrażać się przede wszystkim w jakościowych zmianach w edukacji, medycynie i zatrudnieniu. Szczególną uwagę zwraca się na unowocześnienie paradygmatu zrównoważonego rozwoju, którego elementy należy uszeregować od społecznych do środowiskowych. Uzasadniono konieczność interpretacji strategii inwestycyjnych realizowanych w krajach podążających za wspólnymi problemami. Zwrócono również uwagę na uzasadnienie składnika cyklicznego, jego rolę w redystrybucji przepływów inwestycyjnych na poziomie państwa. W celu rozwiązania tego problemu w artykule zaproponowano inwestycje w klastry.

20

Development of Data-mining Technique for Seismic Vulnerability Assessment

Wojcik Waldemar, Karmenova Markhaba, Smailova Saule, Tlebaldinova Aizhan, Belbeubaev Alisher

International Journal of Electronics and Telecommunications

|

2021

|

Vol. 67, No. 2

261--266

EN

Assessment of seismic vulnerability of urban infrastructure is an actual problem, since the damage caused by earthquakes is quite significant. Despite the complexity of such tasks, today’s machine learning methods allow the use of “fast” methods for assessing seismic vulnerability. The article proposes a methodology for assessing the characteristics of typical urban objects that affect their seismic resistance; using classification and clustering methods. For the analysis, we use kmeans and hkmeans clustering methods, where the Euclidean distance is used as a measure of proximity. The optimal number of clusters is determined using the Elbow method. A decision-making model on the seismic resistance of an urban object is presented, also the most important variables that have the greatest impact on the seismic resistance of an urban object are identified. The study shows that the results of clustering coincide with expert estimates, and the characteristic of typical urban objects can be determined as a result of data modeling using clustering algorithms.