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Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique

Mishra Purnendu, Bhoi Nilamani

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

916--932

EN

Recognizing the cancer genes from the microarray dataset is considered as the most essential research topic in bioinformatics and computational biology domain. Microarray dataset represents the state of each cell at the molecular level which is identified as the important diagnostic tool in medical field. Analyzing the microarray data may provide a huge support for cancer gene classification. Therefore recently a number of artificial intelligence and machine learning techniques are developed which utilize the microarray data for distinguishing the cancer and non-cancer cells. But still now these techniques does not achieved a satisfactory performance. Therefore, an efficient technique that provides a crisp output for cancer classification is required. To overcome such defect, an enhanced ANFIS (EANFIS) method is used in this proposed architecture for classifying the cancer genes. The convergence time of ANFIS gets increased during learning process, therefore to avoid such issue the Manta ray foraging optimization (MaFO) algorithm is hybrid along with ANFIS which improves the overall classification performance. The data given as an input to the classification process is pre-processed at the initial phase using the Ensemble Kalman Filter (EnKF) technique. After pre-processing, the genes having similar properties are clustered using an adaptive density-based spatial clustering with noise (ADBSCAN) clustering technique. Finally, the performance of proposed enhanced ANFIS is evaluated using the precision, accuracy, f-measure, recall, sensitivity, and specificity metrics. Further, the clustering based performance evaluation is also carried out using the cluster index metrics. Finally, the comparison with the state-of-the-art techniques is also performed to show the effectiveness of proposed approach.

2

An integrated approach for performance evaluation of mining industry: a case study of Iranian Steel Complex

Pezeshkan Mehdi, Hosseini Navid

Gospodarka Surowcami Mineralnymi

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2020

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T. 36, z. 3

5--28

EN

The continuous improvement in the industries and organizations hinges upon the evaluation of their performance. In fact, the performance evaluation assists organizations to identify their strengths and weaknesses and, accordingly, enhance their efficiency. As soon as the concept of sustainability was propounded in the engineering based industries, the performance evaluation got more importance due to the environmental issues and social concerns along with the economical aspects. Therefore, this paper is an attempt to propose an approach based on fuzzy best-worst method (BWM) and fuzzy inference system (FIS) in order to evaluate the performance of an Iranian steel complex in terms of sustainability concept. In the proposed approach, the weights of some selected criteria were determined by fuzzy BWM method and, then, the score of the under study industry was calculated in terms of economic, environmental, and social aspects. At the end, an FIS was developed to calculate the final score of the intended industry. In order to check the efficiency of the proposed approach, its performance was measured using expert knowledge as well as real data of a steel complex in Iran. A moderate to high performance has been achieved for the understudy case through conducting the proposed approach. It was suggested that the industry should focus on the criteria with both high weights and low evaluated scores (for example the environmental management technologies and knowledge criterion) to increase its performance evaluation score. The obtained results were indicative of the efficiency of the proposed approach.

PL

Ciągłe doskonalenie branż i organizacji zależy od oceny ich wydajności. W rzeczywistości ocena wyników pomaga organizacjom zidentyfikować ich mocne i słabe strony, a co za tym idzie, zwiększyć ich efektywność. Jak tylko koncepcja zrównoważonego rozwoju została zaproponowana w branżach opartych na inżynierii, ocena wydajności nabrała większego znaczenia ze względu na kwestie środowiskowe i społeczne, a także aspekty ekonomiczne. Artykuł jest próbą zaproponowania podejścia opartego na rozmytej metodzie best-worst (BWM) i rozmytym systemie wnioskowania (FIS) w celu oceny wydajności irańskiego kompleksu stalowego pod kątem koncepcji zrównoważonego rozwoju. W proponowanym podejściu, wagi wybranych kryteriów wyznaczono metodą rozmytą BWM, a następnie obliczono punktację badanej branży pod względem ekonomicznym, środowiskowym i społecznym. Na koniec opracowano rozmyty system wnioskowania FIS, aby obliczyć końcowy wynik dla planowanej branży. Aby sprawdzić efektywność proponowanego podejścia, mierzono jego wydajność, wykorzystując wiedzę ekspercką oraz rzeczywiste dane dotyczące kompleksu stalowego w Iranie. W analizowanym przypadku, poprzez zastosowanie proponowanego podejścia osiągnięto wyniki od umiarkowanych do wysokich. Zasugerowano, że w celu zwiększenia oceny wyników, branża powinna skupić się na kryteriach zarówno o dużej wadze, jak i nisko ocenianych punktach (na przykład technologie zarządzania środowiskowego i kryterium wiedzy). Uzyskane wyniki świadczyły o skuteczności zaproponowanego podejścia.

3

Fuzzy inference system and prediction

Žák L., Valliš D.

Journal of TransLogistics

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2015

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Vol. 1, nr 1

193--202

EN

This paper describes the implementation of fuzzy set theory and Fuzzy Inference System (FIS) for prediction of electric load. The proposed technique utilizes fuzzy rules to incorporate historical weather and load data. The use of fuzzy logic effectively handles the load variations due to special events. The fuzzy logic has been extensively tested on actual data obtained from the Czech Electric Power Company (ˇCEZ) for 24-hour ahead prediction. Test results indicate that the fuzzy rule base can produce results better in accuracy than artificial neural networks (ANNs) method.