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Remote Sensing and Spatial Analysis for Land-Take Assessment in Basilicata Region (Southern Italy)

REMOTE SENSING 2022年第7期14卷 1692-1692页

作者： Santarsiero, Valentina Nole, Gabriele Lanorte, Antonio Tucci, Biagio Cillis, Giuseppe Murgante, Beniamino CNR IMAA I-85050 Potenza Italy Univ Basilicata Sch Engn 10 Viale Ateneo Lucano I-85100 Potenza Italy

Land use is one of the drivers of land-cover change (LCC) and represents the conversion of natural to artificial land cover. This work aims to describe the land-take-monitoring activities and analyze the development trend in test areas of the Basilicata region. Remote sensing is the primary technique for extracting land-use/land-cover (LULC) data. In this study, a new methodology of classification of Landsat data (TM-OLI) is proposed to detect land-cover information automatically and identify land take to perform a multi-temporal analysis. Moreover, within the defined model, it is crucial to use the territorial information layers of geotopographic database (GTDB) for the detailed definition of the land take. All stages of the classification process were developed using the supervised classification algorithm support vector machine (svm) change-detection analysis, thus integrating the geographic information system (GIS) remote sensing data and adopting free and open-source software and data. The application of the proposed method allowed us to quickly extract detailed land-take maps with an overall accuracy greater than 90%, reducing the cost and processing time.

关键词： land take remote sensing svm algorithm change detection analysis geographic information system

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3-Phase Induction motor speed control based space vector modulation technique for direct matrix converter under unbalanced conditions

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JOURNAL OF ELECTRICAL SYSTEMS 2022年第2期18卷 246-259页

作者： Boukhili, F. Zaafouri, A. Dami, M. A. ENSIT LISIER Lab ISET Jendouba Tunisia ENSIT LISIER Lab Tunis Tunisia

In this work, the performance analysis and discussion of a space vector modulation technique used for a three-phase direct matrix converter feeding a three-phase induction motor is presented. For this, the induction motor models and the method of the space vector modulation technique are developed. Then, the overall electrical drive system is simulated using MatLab/Simulik software. Results obtained from simulation are the harmonic spectrum analysis of the motor current and the total harmonic distortion for unbalanced input voltages. These results show the good performance of the control system strategy.

关键词： Direct matrix converter Induction-motor svm algorithm Harmonics Distortion

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Design of Smart Home Implementation Within IoT Natural Language Interface

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IEEE ACCESS 2020年 8卷 84929-84949页

作者： Kim, Tae-Yeun Bae, Sang-Hyun An, Young-Eun Chosun Univ SW Convergence Educ Inst Gwangju 61452 South Korea Chosun Univ Dept Comp Sci & Stat Gwangju 61452 South Korea Chosun Univ Coll Gen Educ Gwangju 61452 South Korea

To process continuous sensor data in Internet of Things (IoT) environments, this study optimizes queries using multiple MJoin operators. To achieve efficient storage management, it classifies and reduces data using a support vector machine (svm) classification algorithm. A global shared query execution technique was used to optimize multiple MJoin queries. By comparing each kernel function of the svm classification algorithm, the system's performance was evaluated through experiments according to the selected optimal kernel function and changes in sliding window size. Furthermore, to implement a smart home system that can actively respond to users, classified and reduced sensor data were utilized to enable the intelligent control of devices inside the home. The sensor data (e.g., temperature, humidity, gas) used to recognize the current conditions of an IoT-based smart home system and corresponding date data were classified into decision trees, and the system was designed using five sensors to intelligently control priorities such as ventilation, temperature, and fire and intrusion detection. The experiments demonstrated that the multiple MJoin technique yields high improvements in performance with relatively few searches. In this study, the sigmoid was selected as the optimal kernel function for the svm classification algorithm. According to the svm classification algorithm results, based on changes in the sliding window size, the average error rate was 2.42%, the reduction result was 17.58%, and the classification accuracy was 85.94%. According to the comparison of the classification performance of svm and other algorithms, the svm classification algorithm exhibited a minimum 9% better classification performance. Thus, compared to existing home systems, this algorithm is expected to increase system efficiency and convenience by enabling the configuration of a more intelligent environment according to the user's characteristics or requirements.

关键词： Application Internet of Things (IoT) sensor data smart home system svm algorithm

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Hybrid Approach to Improving the Results of the svm Classification Using the Random Forest algorithm

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Procedia Computer Science 2019年 150卷 455-461页

作者： L.A. Demidova I.A. Klyueva A.N. Pylkin Ryazan State Radio Engineering University 59/1 Gagarina Str. Ryazan 390005 Russia

The aim of this work is to improve the results of the svm classification (Support Vector Machine) by hybridizing the svm classifier with the random forest classifier (Random Forest, RF) used as the auxiliary. Specification of the classification decisions obtained on the basis of the svm classifier is performed for the objects located in the experimentally determined subareas near the hyperplane separating the classes and including both correctly and erroneously classified objects. In the case of improving the quality of the objects classification from the initial dataset, the proposed hybrid approach to the objects classification can be recommended for classification of new objects. When developing the svm classifier, the fixed default parameters values are used. A comparative analysis of the classification results obtained during the computational experiments in the hybridization of the svm classifier with two auxiliary classifiers – the random forest classifier (RF classifier) and the k nearest neighbor classifier ( k NN classifier), for which the parameters values are determined randomly, confirms the expediency of using of these classifiers to increase the svm classification quality. It was found that in most cases, the random forest classifier works better in terms of improving the svm classification quality in comparison with the k NN classifier.

关键词： intelligent classification algorithm svm algorithm k -nearest neighbors algorithm random forest algorithm support vector kernel function kernel function parameter regularization parameter hybridization

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Optimizing drying and storage for edible mushrooms: Study on gamma irradiation levels, drying temperatures, and packaging materials with svm-based predictions

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Case Studies in Thermal Engineering 2025年 72卷

作者： Ehsan Fartash Naeimi Mohammad Hadi Khoshtaghaza Kemal Çağatay Selvi Mariana Ionescu Soleiman Abbasi Department of Agricultural Machinery and Technologies Engineering Faculty of Agriculture Ondokuz Mayıs University 55139 Samsun Türkiye Department of Biosystems Engineering Faculty of Agriculture Tarbiat Modares University 14115-336 Tehran Iran Department of Biotechnical Systems Faculty of Biotechnical Systems Engineering National University of Science and Technology Politehnica Bucharest 060042 Bucharest Romania Department of Food Science and Technology Faculty of Agriculture Tarbiat Modares University 14115-336 Tehran Iran

The control of dried product quality is crucial for preservation and marketability. In this study, the effects of gamma irradiation doses (0, 1.2, 2.4, and 3.6 kGy) and drying temperatures (50, 60, and 70 °C) on the drying rate, lightness (L∗), and texture firmness of mushroom slices were evaluated. In addition, dried samples were packaged in three materials: polyethylene, polypropylene, and a silicone nanoemulsion-based nanocomposite. After four months, the effect of packaging on L∗ and firmness were assessed. Higher irradiation doses and drying temperatures significantly (P < 0.01) enhanced drying rates, ranging from 0.39 g/g∙min for control samples at 50 °C to 0.95 g/g∙min for 3.6 kGy and 70 °C. Higher irradiation doses and lower temperatures yielded better L∗ values (74–78) and texture firmness (41–50 N). Packaging materials and drying conditions significantly affected (P < 0.01) texture firmness, while packaging showed no significant effect (P > 0.01) on L∗. The Support Vector Machine (svm) algorithm accurately predicted change in L∗ and texture firmness after six months of storage, with the Pearson universal kernel producing the highest correlation coefficients (0.996, 1.000, and 1.000). This kernel outperformed the others in estimating parameter changes within the integrated svm-based modeling framework. Overall, gamma irradiation effectively reduced drying time while maintaining product quality, presenting a viable alternative to expensive industrial drying. Nanocomposite packaging preserved the appearance characteristics of the dried mushrooms, and the svm algorithm demonstrated strong potential for predicting quality changes prior to processing.

关键词： Food irradiation Drying rate Combined technology Nanocomposite packaging svm algorithm

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A Self-tuning Multiobjective Genetic algorithm with Application in the svm Classification

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Procedia Computer Science 2019年 150卷 503-510页

作者： L.A. Demidova M.M. Egin R.V. Tishkin Ryazan State Radio Engineering University 59/1 Gagarina Str. Ryazan 390005 Russia Limited Liability Company “Areal-98” 18 Lesteva 18 Moscow 115162 Russia

In this paper we suggest the self-tuning multiobjective genetic algorithm (STMGA) based on the NSGA-II. This new algorithm is aimed to improve the svm classification quality. The quality classification indicators such as overall accuracy, specificity, sensitivity and a number of support vectors represent the objective functions in the STMGA. The ways for realizing the self-tuning of the such STMGA parameters as the crossover probability, the crossover distribution index and the mutation distribution index have been proposed and investigated. The considered STMGA is more flexible in the context of selecting its parameters’ values and allows to refuse from the use of the parameters’ values which are set manually. In the case of the radial basis kernel function used for the svm classifier development, the STMGA finds the Pareto-front of such parameters values as the regularization parameter value and the Gaussian kernel parameter value which give the best values in the chosen set of the classification quality indicators. The experimental results obtained on the basis of the model and real datasets of loan scoring, medical and technical diagnostics, etc. confirm the efficiency of the proposed STMGA.

关键词： classification svm algorithm genetic algorithm NSGA-II self-tuning algorithm

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Learner modeling in cloud computing

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EDUCATION AND INFORMATION TECHNOLOGIES 2020年第6期25卷 5581-5599页

作者： Ghallabi, Sameh Essalmi, Fathi Jemni, Mohamed Kinshuk Res Lab Technol Informat & Commun & Elect Engn La Tunis Tunisia Arab League Educ Cultural & Sci Org ICT Dept Tunis Tunisia Univ North Texas Coll Informat Denton TX 76203 USA

With the emergence of technology, the personalization of e-learning systems is enhanced. These systems use a set of parameters for personalizing courses. However, in literature, these parameters are not based on classification and optimization algorithms to implement them in the cloud. Cloud computing is a new model of computing where standard and virtualized resources are provided as a service through the Internet. This paper proposes an approach that allows learner modeling in the cloud where these parameters are integrated. The suggested approach is based on the support vector machine algorithm, which analyzes the learners' traces to find the best classification of learners through selected parameters with a low cost. An experimentation is conducted to validate this approach. This experimentation is based on the produced traces for learner modeling. The obtained results show that this approach represents the learner model with low operation costs compared to classic systems (no cloud).

关键词： Learner modeling personalized learning systems learners' traces personalization parameters svm algorithm Cloud computing

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Advanced And Effective Classification of Parkinson's Disease Using Enhanced Neural Networks

Advanced And Effective Classification of Parkinson's Disease...

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International Conference on Intelligent Computing and Control Systems (ICICCS)

作者： Kumar, Kaveti Kiran Babu, Panthagani Vijay Gopi, Simhadri Chinna Arfa, Zoya Vignans Fdn Sci Technol & Res CSE Dept Vadlamudi Andhra Pradesh India MLRIT CSE Dept Hyderabad Telangana India

ISBN: (纸本)9781728148762

A disease that causes due to degeneration in the nerve cells in brain parts with which movements are controlled is a Parkinson's disease. Parkinson's disease increased its occurrence with age and it's becoming the second most common neurodegenerative disorder. Due to this disease, people begin to experience difficulty in speaking, writing, walking. We can diagnose this disease by using voice recordings dataset, handwritten images, and by using several techniques. In this paper, we are using a voice dataset to identify Parkinson's disease which is collected from different people with and without Parkinson's disease. There are several medical tools to identify this disease but it may take time and laborious to identify this disease. In this patients has to take any number of tests as a single test cannot give an effective diagnosis of a disease. So to mitigate this, our work establishes a computer-based analysis technique. We can identify and diagnose this disease using several classification algorithms in machine learning like decision trees, naive bayes algorithm, svm algorithm are few neural networks. In this paper, we use neural networks to classify the disease.

关键词： Decision trees Naive Bayes algorithm svm algorithm Parkinson's disease (PD) Multilayer Perceptron(MLP)

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Computer Software and Hardware Fault Diagnosis Method Based on svm

International Core Journal of Engineering

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International Core Journal of Engineering 2020年第12期6卷 447-453页

作者： Chuyan Dai

With the rapid development of information technology and the popularity of the Internet in the world, the status of computer in daily life and work is becoming more and more important. However, as a highly integrated electronic information equipment, the computer has high precision, and the probability of failure is very high. When there is a problem, the general staff can handle it, but it is difficult to repair because of the lack of computer knowledge. Based on the above background, the purpose of this paper is to discuss the fault diagnosis method of computer software and hardware based on svm. In view of the shortcomings of artificial diagnosis method of laboratory computer hardware fault, such as strong subjective factors, large error and high failure rate, a fault diagnosis method based on svm algorithm is proposed. In this paper, svm algorithm and maximum likelihood estimation algorithm are used to analyze the construction of fault diagnosis model in detail. Taking one or more hardware failures in the process of computer operation as an example, the svm algorithm diagnosis reasoning is carried out. The effectiveness of the method is verified by comparing the simulation results. The results show that compared with the artificial fault diagnosis method, the efficiency of svm algorithm is improved by about 20%, so the prediction method of svm algorithm plays a certain role in reducing the failure rate of computer software and hardware, and it is feasible.

关键词： svm algorithm Computer Failure Fault Diagnosis Diagnosis Prediction rate

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Comparative Analysis of Supervised Machine Learning Techniques for Sales Forecasting

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第11期12卷 102-110页

作者： Raizada, Stuti Saini, Jatinderkumar R. Symbiosis Int Deemed Univ Symbiosis Inst Comp Studies & Res Pune Maharashtra India

This study talks about how data mining can be used for sales forecasting in retail sales and demand prediction. Prediction of sales is a crucial task which determines the success of any organization in the long run. There are various techniques available for predicting the sales of a supermarket such as Time Series algorithm, Regression Techniques, Association rule etc. In this paper, a comparative analysis of some of the Supervised Machine Learning Techniques have been done such as Multiple Linear Regression algorithm, Random Forest Regression algorithm, K-NN algorithm, Support Vector Machine (svm) algorithm and Extra Tree Regression to build a prediction model and precisely estimate possible sales of 45 retail outlets of Walmart store which are at different geographical locations. Walmart is one of the foremost stores across the world and thus authors would like to predict the sales accurately. Certain events and holidays affect the sales periodically, which sometimes can also be on a daily basis. The forecast of probable sales is based on a combination of features such as previous sales data, promotional events, holiday week, temperature, fuel price, CPI i.e., Consumer Price Index and Unemployment rate in the state. The data is collected from 45 outlets of Walmart and the prediction about the sales of Walmart was done using various Supervised Machine Learning Techniques. The contribution of this paper is to help the business owners decide which approach to follow while trying to predict the sales of their Supermarket taken into account different scenarios including temperature, holidays, fuel price, etc. This will help them in deciding the promotional and marketing strategy for their products.

关键词： Sales forecasting linear regression random forest regression KNN regression algorithm svm algorithm supervised machine learning techniques

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