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The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.
Sk Hassan; Arnab Maji; Michał Jasiński; Zbigniew Leonowicz; Elżbieta Jasińska. Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach. Electronics 2021, 10, 1388 .
AMA StyleSk Hassan, Arnab Maji, Michał Jasiński, Zbigniew Leonowicz, Elżbieta Jasińska. Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach. Electronics. 2021; 10 (12):1388.
Chicago/Turabian StyleSk Hassan; Arnab Maji; Michał Jasiński; Zbigniew Leonowicz; Elżbieta Jasińska. 2021. "Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach." Electronics 10, no. 12: 1388.
A considerable fraction of the female workforce worldwide is making ends meet by doing various jobs informally at home or in nearby places, rather than at employers’ premises. The contribution of these female home-based workers (FHBWs) is significant to the country’s economic growth. FHBWs are often confronted with numerous occupational diseases due to a lack of awareness of occupational safety and health measures, and unhealthy living and working conditions. The informality of FHBWs prevents them from getting proper healthcare, safety, and other dispensations enjoyed by formal employees. Despite their undeniable importance, health issues of FHBWs are still overlooked. This study is an attempt to discover the frequent co-occurring occupational diseases encountered by FHBWs in Punjab, a province of Pakistan. Frequent itemset mining (FIM) or co-occurrence grouping is a technique of data science that identifies the associations among different entities in the data. Based on FIM, the D-GENE algorithm is applied in this study to efficiently discover frequent co-occurring diseases in the data obtained from the Punjab Home-based Workers Survey (2016). The far-reaching goal of the study is to bring awareness of the occupational health issues and safety risks to the health authorities as well as to the FHBWs.
Muhammad Yasir; Ayesha Ashraf; Muhammad Chaudhry; Farhad Hassan; Jee-Hyong Lee; Michał Jasiński; Zbigniew Leonowicz; Elżbieta Jasińska. D-GENE-Based Discovery of Frequent Occupational Diseases among Female Home-Based Workers. Electronics 2021, 10, 1230 .
AMA StyleMuhammad Yasir, Ayesha Ashraf, Muhammad Chaudhry, Farhad Hassan, Jee-Hyong Lee, Michał Jasiński, Zbigniew Leonowicz, Elżbieta Jasińska. D-GENE-Based Discovery of Frequent Occupational Diseases among Female Home-Based Workers. Electronics. 2021; 10 (11):1230.
Chicago/Turabian StyleMuhammad Yasir; Ayesha Ashraf; Muhammad Chaudhry; Farhad Hassan; Jee-Hyong Lee; Michał Jasiński; Zbigniew Leonowicz; Elżbieta Jasińska. 2021. "D-GENE-Based Discovery of Frequent Occupational Diseases among Female Home-Based Workers." Electronics 10, no. 11: 1230.
Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier’s performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.
Yogendra Solanki; Prasun Chakrabarti; Michal Jasinski; Zbigniew Leonowicz; Vadim Bolshev; Alexander Vinogradov; Elzbieta Jasinska; Radomir Gono; Mohammad Nami. A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches. Electronics 2021, 10, 699 .
AMA StyleYogendra Solanki, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz, Vadim Bolshev, Alexander Vinogradov, Elzbieta Jasinska, Radomir Gono, Mohammad Nami. A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches. Electronics. 2021; 10 (6):699.
Chicago/Turabian StyleYogendra Solanki; Prasun Chakrabarti; Michal Jasinski; Zbigniew Leonowicz; Vadim Bolshev; Alexander Vinogradov; Elzbieta Jasinska; Radomir Gono; Mohammad Nami. 2021. "A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches." Electronics 10, no. 6: 699.
This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.
Imayanmosha Wahlang; Arnab Maji; Goutam Saha; Prasun Chakrabarti; Michal Jasinski; Zbigniew Leonowicz; Elzbieta Jasinska. Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography. Electronics 2021, 10, 495 .
AMA StyleImayanmosha Wahlang, Arnab Maji, Goutam Saha, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz, Elzbieta Jasinska. Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography. Electronics. 2021; 10 (4):495.
Chicago/Turabian StyleImayanmosha Wahlang; Arnab Maji; Goutam Saha; Prasun Chakrabarti; Michal Jasinski; Zbigniew Leonowicz; Elzbieta Jasinska. 2021. "Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography." Electronics 10, no. 4: 495.
Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is required as soon as possible. Machine learning technique has become reliable for medical treatment. With the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For this perspective, Chronic Kidney Disease prediction has been discussed in this article. Chronic Kidney Disease dataset has been taken from the UCI repository. Seven classifier algorithms have been applied in this research such as artificial neural network, C5.0, Chi-square Automatic interaction detector, logistic regression, linear support vector machine with penalty L1 & with penalty L2 and random tree. The important feature selection technique was also applied to the dataset. For each classifier, the results have been computed based on (i) full features, (ii) correlation-based feature selection, (iii) Wrapper method feature selection, (iv) Least absolute shrinkage and selection operator regression, (v) synthetic minority over-sampling technique with least absolute shrinkage and selection operator regression selected features, (vi) synthetic minority over-sampling technique with full features. From the results, it is marked that LSVM with penalty L2 is giving the highest accuracy of 98.86% in synthetic minority over-sampling technique with full features. Along with accuracy, precision, recall, F-measure, area under the curve and GINI coefficient have been computed and compared results of various algorithms have been shown in the graph. Least absolute shrinkage and selection operator regression selected features with synthetic minority over-sampling technique gave the best after synthetic minority over-sampling technique with full features. In the synthetic minority over-sampling technique with least absolute shrinkage and selection operator selected features, again linear support vector machine gave the highest accuracy of 98.46%. Along with machine learning models one deep neural network has been applied on the same dataset and it has been noted that deep neural network achieved the highest accuracy of 99.6%.
Pankaj Chittora; Sandeep Chaurasia; Prasun Chakrabarti; Gaurav Kumawat; Tulika Chakrabarti; Zbigniew Leonowicz; Michal Jasinski; Lukasz Jasinski; Radomir Gono; Elzbieta Jasinska; Vadim Bolshev. Prediction of Chronic Kidney Disease - A Machine Learning Perspective. IEEE Access 2021, 9, 17312 -17334.
AMA StylePankaj Chittora, Sandeep Chaurasia, Prasun Chakrabarti, Gaurav Kumawat, Tulika Chakrabarti, Zbigniew Leonowicz, Michal Jasinski, Lukasz Jasinski, Radomir Gono, Elzbieta Jasinska, Vadim Bolshev. Prediction of Chronic Kidney Disease - A Machine Learning Perspective. IEEE Access. 2021; 9 ():17312-17334.
Chicago/Turabian StylePankaj Chittora; Sandeep Chaurasia; Prasun Chakrabarti; Gaurav Kumawat; Tulika Chakrabarti; Zbigniew Leonowicz; Michal Jasinski; Lukasz Jasinski; Radomir Gono; Elzbieta Jasinska; Vadim Bolshev. 2021. "Prediction of Chronic Kidney Disease - A Machine Learning Perspective." IEEE Access 9, no. : 17312-17334.
Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of geoscience for forecasting earthquakes. We aim to develop a method for forecasting the magnitude range of earthquakes using machine learning classifier algorithms. Three different ranges have been categorized: fatal earthquake; moderate earthquake; and mild earthquake. In order to distinguish between these classifications, seven different machine learning classifier algorithms have been used for building the model. To train the model, six different datasets of India and regions nearby to India have been used. The Bayes Net, Random Tree, Simple Logistic, Random Forest, Logistic Model Tree (LMT), ZeroR and Logistic Regression algorithms have been applied to each dataset. All of the models have been developed using the Weka tool and the results have been noted. It was observed that Simple Logistic and LMT classifiers performed well in each case.
Papiya Debnath; Pankaj Chittora; Tulika Chakrabarti; Prasun Chakrabarti; Zbigniew Leonowicz; Michal Jasinski; Radomir Gono; Elżbieta Jasińska. Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers. Sustainability 2021, 13, 971 .
AMA StylePapiya Debnath, Pankaj Chittora, Tulika Chakrabarti, Prasun Chakrabarti, Zbigniew Leonowicz, Michal Jasinski, Radomir Gono, Elżbieta Jasińska. Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers. Sustainability. 2021; 13 (2):971.
Chicago/Turabian StylePapiya Debnath; Pankaj Chittora; Tulika Chakrabarti; Prasun Chakrabarti; Zbigniew Leonowicz; Michal Jasinski; Radomir Gono; Elżbieta Jasińska. 2021. "Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers." Sustainability 13, no. 2: 971.
This article presents big data analysis, based on the measurements from a stationary power quality (PQ) monitoring system of a 15 kW PV power plant. The study period is one year – 2018. In the article, a unified PQ database was proposed, which consists of PQ parameters and active power level. Then cluster analysis (CA) was performed to indicate the different working conditions of a PV power plant. The Ward algorithm was selected to obtain the automatic classification due to the data features. The importance rate of parameters for each classification was calculated, to indicate which parameters were important in point of different clusters obtained. Additionally, the PQ level comparison between the clusters was realized to assess the indicated working conditions of a PV power plant.
Michal Jasinski; Tomasz Sikorski; Zbigniew Leonowicz; Dominika Kaczorowska; Vishnu Suresh; Jaroslaw Szymanda; Elzbieta Jasinska. Different working conditions identification of a PV power plant using hierarchical clustering. 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) 2020, 1 -8.
AMA StyleMichal Jasinski, Tomasz Sikorski, Zbigniew Leonowicz, Dominika Kaczorowska, Vishnu Suresh, Jaroslaw Szymanda, Elzbieta Jasinska. Different working conditions identification of a PV power plant using hierarchical clustering. 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). 2020; ():1-8.
Chicago/Turabian StyleMichal Jasinski; Tomasz Sikorski; Zbigniew Leonowicz; Dominika Kaczorowska; Vishnu Suresh; Jaroslaw Szymanda; Elzbieta Jasinska. 2020. "Different working conditions identification of a PV power plant using hierarchical clustering." 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) , no. : 1-8.
This paper presents the analysis of power supply restoration time after failures occurring in power lines. It found that the power supply restoration time depends on several constituents, such as the time for obtaining information on failures, the time for information recognition, the time to repair failures, and the time for connection harmonization. All these constituents have been considered more specifically. The main constituents’ results values of the power supply restoration time were analyzed for the electrical networks of regional power supply company “Oreolenergo”, a branch of Interregional Distribution Grid Company (IDGC) of Center. The Delphi method was used for determining the time for obtaining information on failures as well as the time for information recognition. The method of mathematical statistics was used to determine the repair time. The determined power supply restoration time (5.28 h) is similar to statistical values of the examined power supply company (the deviation was equal to 9.9%). The technical means of electrical network automation capable of the reduction of the power supply restoration time have also been found. These means were classified according to the time intervals they shorten.
Alexander Vinogradov; Vadim Bolshev; Alina Vinogradova; Michał Jasiński; Tomasz Sikorski; Zbigniew Leonowicz; Radomir Goňo; Elżbieta Jasińska. Analysis of the Power Supply Restoration Time after Failures in Power Transmission Lines. Energies 2020, 13, 2736 .
AMA StyleAlexander Vinogradov, Vadim Bolshev, Alina Vinogradova, Michał Jasiński, Tomasz Sikorski, Zbigniew Leonowicz, Radomir Goňo, Elżbieta Jasińska. Analysis of the Power Supply Restoration Time after Failures in Power Transmission Lines. Energies. 2020; 13 (11):2736.
Chicago/Turabian StyleAlexander Vinogradov; Vadim Bolshev; Alina Vinogradova; Michał Jasiński; Tomasz Sikorski; Zbigniew Leonowicz; Radomir Goňo; Elżbieta Jasińska. 2020. "Analysis of the Power Supply Restoration Time after Failures in Power Transmission Lines." Energies 13, no. 11: 2736.
This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% assured the classification with a 95% agreement when compared to the complete database classification.
Michał Jasiński; Tomasz Sikorski; Zbigniew Leonowicz; Klaudiusz Borkowski; Elżbieta Jasińska. The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation. Energies 2020, 13, 2407 .
AMA StyleMichał Jasiński, Tomasz Sikorski, Zbigniew Leonowicz, Klaudiusz Borkowski, Elżbieta Jasińska. The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation. Energies. 2020; 13 (9):2407.
Chicago/Turabian StyleMichał Jasiński; Tomasz Sikorski; Zbigniew Leonowicz; Klaudiusz Borkowski; Elżbieta Jasińska. 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation." Energies 13, no. 9: 2407.
Recently a number of changes were introduced in amendment to standard EN 50160 related to power quality (PQ) including 1 min aggregation intervals and the obligation to consider 100% of measured data taken for the assessment of voltage variation in a low voltage (LV) supply terminal. Classical power quality assessment can be extended using a correlation analysis so that relations between power quality parameters and external indices such as weather conditions or power demand can be revealed. This paper presents the results of a comparative investigation of the application of 1 and 10 min aggregation times in power quality assessment as well as in the correlation analysis of power quality parameters and weather conditions and the energy production of a 100 kW photovoltaic (PV) power plant connected to a LV network. The influence of the 1 min aggregation time on the result of the PQ assessment as well as the correlation matrix in comparison with the 10 min aggregation algorithm is presented and discussed.
Michał Jasiński; Tomasz Sikorski; Paweł Kostyła; Dominika Kaczorowska; Zbigniew Leonowicz; Jacek Rezmer; Jarosław Szymańda; Przemysław Janik; Daniel Bejmert; Marek Rybiański; Elżbieta Jasińska. Influence of Measurement Aggregation Algorithms on Power Quality Assessment and Correlation Analysis in Electrical Power Network with PV Power Plant. Energies 2019, 12, 3547 .
AMA StyleMichał Jasiński, Tomasz Sikorski, Paweł Kostyła, Dominika Kaczorowska, Zbigniew Leonowicz, Jacek Rezmer, Jarosław Szymańda, Przemysław Janik, Daniel Bejmert, Marek Rybiański, Elżbieta Jasińska. Influence of Measurement Aggregation Algorithms on Power Quality Assessment and Correlation Analysis in Electrical Power Network with PV Power Plant. Energies. 2019; 12 (18):3547.
Chicago/Turabian StyleMichał Jasiński; Tomasz Sikorski; Paweł Kostyła; Dominika Kaczorowska; Zbigniew Leonowicz; Jacek Rezmer; Jarosław Szymańda; Przemysław Janik; Daniel Bejmert; Marek Rybiański; Elżbieta Jasińska. 2019. "Influence of Measurement Aggregation Algorithms on Power Quality Assessment and Correlation Analysis in Electrical Power Network with PV Power Plant." Energies 12, no. 18: 3547.
Computer-aided systems appliance to maintenance is a known problem and there are a lot of programs dedicated to supporting it. Although, the majority of them are based only on time indicators like mean time to repair (MTTR), mean time to failure (MTTF) or mean time between failures (MTBF). This article presents the appliance of Computerised Maintenance Management System (CMMS) to support a maintenance in KGHM Polska Miedi S.A - copper mining industry in Poland. It's realized by using data from CMMS to calculate the intensity of failures of mining machines components and systems. The calculations are based on quantitative and qualitative data of failure proceed from CMMS. The article presents a case study of the intensity of failures level analysis for the hydraulic system of the haul trucks and its selected component - hydraulic pump.
Marek Jasiński; Elzbieta Jasinska; Michał Jasiński; Lukasz Jasinski. Computer-aided appliances to underground machines maintenance – Selected issues. 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) 2018, 1 -4.
AMA StyleMarek Jasiński, Elzbieta Jasinska, Michał Jasiński, Lukasz Jasinski. Computer-aided appliances to underground machines maintenance – Selected issues. 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). 2018; ():1-4.
Chicago/Turabian StyleMarek Jasiński; Elzbieta Jasinska; Michał Jasiński; Lukasz Jasinski. 2018. "Computer-aided appliances to underground machines maintenance – Selected issues." 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) , no. : 1-4.