Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Increasing the accuracy of thematic maps generated using satellite imagery is a crucial task in remote sensing. In this study, a product-level fusion (PLF) approach based on integration of different land-type maps generated using various satellite-derived indices including normalized difference water index (NDWI), normalized difference built-up index (NDBI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI) is proposed to improve the accuracy of land cover mapping. The suitability of the proposed approach for land cover mapping is evaluated in comparison with two high-performance image classification techniques including support vector machine (SVM) and artificial neural network (ANN). The results show that the overall accuracy and kappa values of about 95.95 % and 0.95, 94.91 % and 0.94, and 85.32 % and 0.82 are achieved for the PLF, SVM, and ANN approaches, respectively. The results indicate superiority of the PLF approach than SVM and ANN techniques for land cover classification of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery, especially for the extraction of forest, rice, and citrus classes. However, SVM technique also provided reliable result for land cover mapping.

Water is an important natural resource in our daily life. Water resources play a major role in sustainable development. Surveying and continuous monitoring of water bodies in a vast area is a tedious and timeconsuming task by conventional surveying techniques. Keeping track of changes in the areas of waterbodies due to urbanization and industrialization is very important. The present study aims at determining the spatiotemporal changes in the water bodies from 2008 to 2018 in the drought-hit area of Mahbubnagar, Telangana using satellite-derived spectral index namely,Modified Normalized Difference Water Index (MNDWI) and Support Vector Machine (SVM) technique using Resourcesat-1 Data. Extraction of water bodies using MNDWI and SVM can be done very easily with very little effort as compared to conventional surveying techniques. Using the MNDWI technique,the total area of waterbodies is 322.64 square kilometers in the year 2008 and 362.61 square kilometers in the year 2018. Using the SVM technique, the area of waterbodies is 368.17 square kilometers in the year 2008 and 395.72 in the year 2018. The kappa index values of 2018 by MNDWI technique and SVM technique are 0.86 and 0.91 respectively. The kappa index values of 2008 by MNDWI technique and SVM technique are 0.85 and 0.89 respectively. It is observed that the SVM technique is more accurate than the MNDWI technique. The area of water bodies has increased during the study period mainly due to increased awareness among the public on the importance of water and also initiatives taken by the Government of Telangana, like Mission Kakatiya for the revival of water bodies in the study area.

This research delves into the classification of cardiovascular disease (CVD) utilizing state-of-the-art machine learning algorithms, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). Before model training, extensive data preprocessing techniques were implemented, including data cleaning, feature scaling, encoding, Feature selection, handling imbalanced data, normalization, and cross-validation. After data preparation, an extensive evaluation of performance was carried out against various parameters like accuracy, precision, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odd ratio (DOR). The comparison of SVM and ANN techniques indicates that the SVM has a better sensitivity in detecting positive cases while ANNs have more accuracy in the classification. This paper not only documents the use of new methods but also highlights the advantages and disadvantages of SVM and ANN models, and therefore helps to improve the use of machine learning applications in making health care decisions on CVD diagnosis.

Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses

Parkinson's disease (PD) is a neurological disorder which is progressive in nature. Although there is no cure to this disease, symptomatic treatments are available. These treatments can slow the progressive development of the symptoms. Medications can treat some of the symptoms of the PD up to a great extent that in turn may help the patients to live a normal life. Besides these medications, the patients can also be provided with various therapies based on the types of their symptoms. But for providing any treatment, detection of its symptoms at an early stage is very important. This can reduce its future complexities considerably. Early diagnosis along with proper medications may treat the symptoms of PD significantly. This motivates to propose a new and effective methodology for detection and analysis of PD. In this work, an approach has been proposed for identification of PD patients by using Electroencephalogram (EEG) signals. Here, the EEG signals of normal persons and PD patients are processed in three stages. First, the raw EEG signals are pre-processed for removal of noises and artefacts present. Out of various techniques, Wavelet transform is used for this purpose. In the MATLAB environment, de-noising can be executed by using the in-built functions. Performances of the de-noising techniques are examined with the performance parameters namely Root Mean Square Error (RMSE) as well as Signal to Noise Ratio (SNR). In the second stage, statistical features are extracted from the pre-processed EEG signals. In this work, five statistical features are considered for performing the classification. In the final stage, the extracted features are classified using Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. ANN is an efficient classifier that predicts the human brain's working manners. SVM on the other hand has been proven as one of the most prevailing classification algorithms that gives highly accurate and robust results. This is a novel approach of analyzing the performances of the classification techniques by evaluating the best performing feature. In both the classifiers accuracy, precision, sensitivity and specificity are calculated from the confusion matrix evaluated from the values of the statistical features. In ANN, results using six different training algorithms at different hidden layers are calculated and compared. This proves the training algorithm Levenberg-Marquardt back-propagation with hidden layer 20 as the best combination for performing the classification. From the results it is seen that both ANN and SVM classifiers provide significant classification accuracies of 94.7% and 96.5% respectively. Amongst the five considered features, Mean performs the best in terms of classification accuracy.

Utilizing Artificial Neural Networks for Establishing Hearing-Loss Predicting Models Based on a Longitudinal Dataset and Their Implications for Managing the Hearing Conservation Program

Breast cancer is a frequent cancer that develops when normal cells in the breast transform into malignant cells. Breast cancer can arise from glandular tissue, muscular tissue, or fatty tissue in the breast. Many variables contribute to the risk of breast cancer, including genetics, environmental exposure, food, and lifestyle. Breast cancer should be detected early through breast self-examination, regular clinical evaluation, and mammography to identify any abnormal changes, In recent years, early detection of breast cancer in women has emerged as a beacon of hope and a pivotal point in the treatment of this dangerous disease, and its timely identification has become paramount. Modern advancements in technology, especially artificial intelligence algorithms, have played a vital role in developing systems that facilitate automated disease detection, diagnosis, rapid response, and a reduced risk of fatalities. This paper delves into a comparative study of various machine learning (ML) techniques, namely logistic regression (LR), support vector machines (SVM), linear SVM, Gaussian Naive Bayes (GNB), and artificial neural networks (ANNs). The evaluation metrics used in this study are accuracy and elapsed time. The results show that Gaussian Naive Bayes achieved the highest accuracy of 94.07% in just 0.005495 seconds, outperforming SVM (91.85%), linear SVM (90.19%), logistic regression (87.04%), and ANN (37.04%). These findings highlight the potential of Gaussian Naive Bayes in aiding the early detection of breast cancer, leading to more effective and timely interventions, ultimately improving patient outcomes. Keywords: Breast Cancer, Machine learning (ML), Logistic Regression (LR), Support Vector Machine (SVM), Linear SVM, Gaussian Naive Bayes (GNB) and Artificial Neural Networks (ANNs).

The present study deals with hyperspectral species mapping, utilization of optimal bands, and studying the spectral separability of vegetation species using AVIRIS-NG data. To reduce data redundancy, a wide range of optimal spectral bands (491 nm, 541 nm, 641 nm, 722 nm, 772 nm, 852 nm, 942 nm, 1047 nm, 1132 nm, 1443 nm, and 2475 nm) were selected and simulated for classification using artificial neural network (ANN) and support vector machine (SVM) techniques. Band-to-band correlative plots demonstrated huge data redundancy (correlation as high as R2 = 0.99, at 682 nm and 687 nm) owing to the need of spectral sub-setting. ANN classification exhibited dominance of low-elevation (0–800 m) evergreen forest (44.81%) followed by Tectona grandis plantation (25.97%). Mid-elevation (800–1450) evergreen forest contributes ~ 16.79% of area, and primarily consists of Coffee arabica (3.14%) and Camelia sinensis (0.7%). Spectrally, each vegetation species exhibited distinct spectral characteristics plotted against different wavelengths and unveiled unique spectral signatures and their classification provided comparable accuracy in species identification in ANN (86.45%) as well as in SVM (86.75%). With immense potential and applicability, spectroscopic AVIRIS-NG data is highly recommended for characterizing, quantifying, modeling, and mapping vegetation.

Burnup characteristics and performance of boron nitride and boron coated urania and urania-gadolinia fuels

Cancer is the most common disease to threaten human health. The ability to screen individuals with malignant tumours with only a blood sample would be greatly advantageous to early diagnosis and intervention. This study explores the possibility of discriminating between cancer patients and normal subjects with serum surface-enhanced Raman spectroscopy (SERS) and a support vector machine (SVM) through a peripheral blood sample. A total of 130 blood samples were obtained from patients with liver cancer, colonic cancer, esophageal cancer, nasopharyngeal cancer, gastric cancer, as well as 113 blood samples from normal volunteers. Several diagnostic models were built with the serum SERS spectra using SVM and principal component analysis (PCA) techniques. The results show that a diagnostic accuracy of 85.5% is acquired with a PCA algorithm, while a diagnostic accuracy of 95.8% is obtained using radial basis function (RBF), PCA–SVM methods. The results prove that a RBF kernel PCA–SVM technique is superior to PCA and conventional SVM (C-SVM) algorithms in classification serum SERS spectra. The study demonstrates that serum SERS, in combination with SVM techniques, has great potential for screening cancerous patients with any solid malignant tumour through a peripheral blood sample.

BACKGROUND Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. AIM To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. METHODS The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). RESULTS A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. CONCLUSION Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.

This paper presents a combined Entropy Decomposition and Support Vector Machine (EDSVM) technique for Synthetic Aperture Radar (SAR) image classification with the application on rice monitoring. The objective of this paper is to assess the use of multi-temporal data for the supervised classification of rice planting area based on different schedules. Since adequate priori information is needed for this supervised classification, ground truth measurements of rice fields were conducted at Sungai Burung, Selangor, Malaysia for an entire season from the early vegetative stage of the plants to the ripening stage. The theoretical results of Radiative Transfer Theory based on the ground truth parameters are used to de. ne training sets of the different rice planting schedules in the feature space of Entropy Decomposition. The Support Vector Machine is then applied to the feature space to perform the image classification. The effectiveness of this algorithm is demonstrated using multi-temporal RADARSAT-1 data. The results are also used for comparison with the results based on information of training sets from the image using Maximum Likelihood technique, Entropy Decomposition technique and Support Vector Machine technique. The proposed method of EDSVM has shown to be useful in retrieving polarimetric information for each class and it gives a good separation between classes. It not only gives significant results on the classification, but also extends the application of Entropy Decomposition to cover multi-temporal data. Furthermore, the proposed method offers the ability to analyze single-polarized, multi-temporal data with the advantage of the unique features from the combined method of Entropy Decomposition and Support Vector Machine which previously only applicable to multi-polarized data. Classification based on theoretical modeling is also one of the key components in this proposed method where the results from the theoretical models can be applied as the input of the proposed method in order to de. ne the training sets.

The focus is made to develop predictor models for wind speed prediction employing the support vector machine neural models. Basically, support vector machines (SVM) is employed as classifiers, but this contribution models variant of SVM to act as predictors. A developed model of linear support vector machine (LSVM) and proximal support vector machine (PSVM) is proposed to carry out the wind speed prediction using the available real time wind farm data. In developed PSVM predictor, it is modeled for both linear PSVM predictor and non-linear PSVM predictor. The difference between the developed linear and non-linear PSVM predictor models lies in their applicability of kernel functions to perform effective wind speed prediction. The prediction application is implemented for the set of wind farm data with a wind mill height of 50 m in a manner to minimize the mean square error. The training process of the neural network algorithmic flow is done with the developed LSVM, L-PSVM (Linear PSVM) and N-PSVM (nonlinear PSVM) for predicting the wind speed in renewable energy systems. Results computed are compared with the other types of predictors to prove the effectiveness of the proposed variants of SVM predictors. The simulated results presents the effectiveness of the proposed predictors for the real time wind farm data and the applicability of the predictors for the considered datasets.

Artificial Neural Network (ANN) techniques have recently been applied to many different fields and have demonstrated their capabilities in solving complex problems. In a business environment, the techniques have been applied to predict bond ratings and stock price performance. In these applications, ANN techniques outperformed widely-used multivariate statistical techniques. The purpose of this paper is to compare the ANN method with the Discriminant Analysis (DA) method in order to understand the merits of ANN that are responsible for the higher level of performance. The paper provides an overview of the basic concepts of ANN techniques in order to enhance the understanding of this emerging technique. The similarities and differences between ANN and DA techniques in representing their models are described. This study also proposes a method to overcome the limitations of the ANN approach, Finally, a case study using a data set in a business environment demonstrates the superiority of ANN over DA as a method of classification of observations.

Bio-oil production from rice husk, an abundant agricultural residue, has gained significant attention as a sustainable and renewable energy source. The current research aims to employ artificial neural network (ANN) and support vector machine (SVM) modeling techniques for the optimization of operating parameters for bio-oil extracted from rice husk ash (RHA) through pyrolysis. ANN and SVM methods are employed to model and optimize the operational conditions, including temperature, heating rate, and feedstock particle size, to enhance the yield and quality of bio-oil. Additionally, ANN modeling is utilized to create a predictive model for bio-oil properties, allowing for the efficient optimization of pyrolysis conditions. This research provides valuable insights into the production and properties of bio-oil from RHA. By harnessing the capabilities of ANN and SVM, this research not only aids in understanding the intricate relationships between process variables and bio-oil properties but also provides a means to systematically enhance the production process. The predictive results obtained from the ANN were found to be good when compared with the SVM. Several models with different numbers of neurons have been trained with different transfer functions. R values for the training, validation, and test phases are around 1.0, i.e., 0.9981, 0.9976, and 0.9978, respectively. The overall R-value was 0.9960 for the proposed network. The findings were considered acceptable, as the overall R-value was close to 1.0. The optimized operational parameters contribute to the efficient conversion of RHA into bio-oil, thereby promoting the use of this sustainable resource for renewable energy production. This approach aligns with the growing emphasis on reducing the environmental impact of traditional fossil fuels and advancing the utilization of alternative and environmentally friendly energy sources.