Sentiment analysis on film review in Gujarati language using machine learning

<span>Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer.</span>

Turbine recommender: The selection of wind turbine type using one of a machine learning technique

This study aims to utilize the machine learning technique to build a model to recommend the suitable wind turbine type based on some variables, such as air speed and air density, as well as visualize the location of the recommended wind turbine selection on a 3D map. Particularly, we applied the K-nearest neighbor model (KNN) to determine the amount of energy produced by a single wind turbine. We applied it on 10 separate wind farms in Saudi Arabia. The results indicate that the model performs very well in predicting the best wind turbine type with the mean accuracy of 88%, where ten wind stations resulted from the optimized model with the suggested turbine type in each station. Adding more wind attributes and other factors may assist in increasing the model mean accuracy. The project’s findings will assist decision-makers in Saudi Arabia to make informed decisions as to what kind of wind turbine is suitable for a specific location. In the long run, this will help to make wind energy-a sustainable source of energy-one of the main goals of the 2030 vision, specifically under National Industrial Development and Logistics Program.

Geochemical and Hydrothermal Alteration Patterns of the Abrisham-Rud Porphyry Copper District, Semnan Province, Iran

In this study, the zonality method has been used to separate geochemical anomalies and to calculate erosional levels in the regional scale for porphyry-Cu deposit, Abrisham-Rud (Semnan province, East of Iran). In geochemical maps of multiplicative haloes, the co-existence of both the supra-ore elements and sub-ore elements local maxima implied blind mineralization in the northwest of the study area. Moreover, considering the calculated zonality indices and two previously presented geochemical models, E and NW of the study have been introduced as ZDM and BM, respectively. For comparison, the geological layer has been created by combining rock units, faults, and alterations utilizing the K-nearest neighbor (KNN) algorithm. The rock units and faults have been identified from the geological map; moreover, alterations have been detected by using remote sensing and ASTER images. In the geological layer map related to E of the study area, many parts have been detected as high potential areas; in addition, both geochemical and geological layer maps only confirmed each other at the south of this area and suggested this part as high potential mineralization. Therefore, high potential areas in the geological layer map could be related to the mineralization or not. Due to the incapability of the geological layer in identifying erosional levels, mineralogy investigation could be used to recognize this level; however, because of the high cost, mineralogy is not recommended for application on a regional scale. The findings demonstrated that the zonality method has successfully distinguished geochemical anomalies including BM and ZDM without dependent on alteration and was able to predict erosional levels. Therefore, this method is more powerful than the geological layer.

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models.

KLASIFIKASI REGRESI LOGISTIK MULTINOMIAL DAN FUZZY K-NEAREST NEIGHBOR (FK-NN) DALAM PEMILIHAN METODE KONTRASEPSI DI KECAMATAN BULAKAMBA, KABUPATEN BREBES, JAWA TENGAH

The characteristics of society in choosing contraceptive methods are also the crucial factors for the government to prepare the family planning services needed at Bulakamba District, Brebes Regency, Central Java. In this case, a classification process needs to be done to assist the process of classifying the characteristics of society in the selection of contraceptive methods. Multinomial Logistic Regression classification is good in exploring data information meanwhile Fuzzy K Nearest Neighbor (FK-NN) classification is good for handling big data and noise. These two methods used in this study because they are relevant to the data applied and will be compared their classification accuracy through APER and Press's Q calculations.The classification accuracy results obtained based on the APER calculation for Multinomial Logistic Regression is 88,25% and Fuzzy K Nearest Neighbor (FK-NN) is 88,92%. Meanwhile, the Press's Q value of both methods are 9600,945 and 9518,014 greater than χ 2𝛼,1 which is 3,841, so that it is statistically accurate. Based on the results obtained, it can be concluded that Multinomial Logistic Regression classification method has a better classification accuracy than Fuzzy K Nearest Neighbor (FK-NN) method.

An adaptive Dendrite-HDMR metamodeling technique for high dimensional problems

High dimensional model representation (HDMR), decomposing the high-dimensional problem into summands of different order component terms, has been widely researched to work out the dilemma of “curse-of-dimensionality” when using surrogate techniques to approximate high-dimensional problems in engineering design. However, the available one-metamodel-based HDMRs usually encounter the predicament of prediction uncertainty, while current multi-metamodels-based HDMRs cannot provide simple explicit expressions for black-box problems, and have high computational complexity in terms of constructing the model by the explored points and predicting the responses of unobserved locations. Therefore, aimed at such problems, a new stand-alone HDMR metamodeling technique, termed as Dendrite-HDMR, is proposed in this study based on the hierarchical Cut-HDMR and the white-box machine learning algorithm, Dendrite Net. The proposed Dendrite-HDMR not only provides succinct and explicit expressions in the form of Taylor expansion, but also has relatively higher accuracy and stronger stability for most mathematical functions than other classical HDMRs with the assistance of the proposed adaptive sampling strategy, named KKMC, in which k-means clustering algorithm, k-Nearest Neighbor classification algorithm and the maximum curvature information of the provided expression are utilized to sample new points to refine the model. Finally, the Dendrite-HDMR technique is applied to solve the design optimization problem of the solid launch vehicle propulsion system with the purpose of improving the impulse-weight ratio, which represents the design level of the propulsion system.

A Novel Motion Recognition Method Based on Force Myography of Dynamic Muscle Contractions

Surface electromyogram-based pattern recognition (sEMG-PR) has been considered as the most promising method to control multifunctional prostheses for decades. However, the commercial applications of sEMG-PR in prosthetic control is still limited due to the ambient noise and impedance variation between electrodes and skin surface. In order to reduce these issues, a force-myography-based pattern recognition method was proposed. In this method, a type of polymer-based flexible film sensors, the piezoelectrets, were used to record the rate of stress change (RSC) signals on the muscle surface of eight able-bodied subjects for six hand motions. Thirteen time domain features and four classification algorithms of linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM) were adopted to decode the RSC signals of different motion classes. In addition, the optimal feature set, classifier, and analysis window length were investigated systematically. Results showed that the average classification accuracy was 95.5 ± 2.2% by using the feature combination of root mean square (RMS) and waveform length (WL) for the classifier of KNN, and the analysis window length of 300 ms was found to obtain the best classification performance. Moreover, the robustness of the proposed method was investigated, and the classification accuracies were observed above 90% even when the white noise ratio increased to 50%. The work of this study demonstrated the effectiveness of RSC-based pattern recognition method for motion classification, and it would provide an alternative approach for the control of multifunctional prostheses.

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.