Percent Classification Accuracy Research Articles

Using automated text classification to explore uncertainty in NICE appraisals for drugs for rare diseases.

This study examined the application, feasibility, and validity of supervised learning models for text classification in appraisals for rare disease treatments (RDTs) in relation to uncertainty, and analyzed differences between appraisals based on the classification results. We analyzed appraisals for RDTs (n=94) published by the National Institute for Health and Care Excellence (NICE) between January 2011 and May 2023. We used Naïve Bayes, Lasso, and Support Vector Machine models in a binary text classification task (classifying paragraphs as either referencing uncertainty in the evidence base or not). To illustrate the results, we tested hypotheses in relation to the appraisal guidance, advanced therapy medicinal product (ATMP) status, disease area, and age group. The best performing (Lasso) model achieved 83.6 percent classification accuracy (sensitivity=74.4 percent, specificity=92.6 percent). Paragraphs classified as referencing uncertainty were significantly more likely to arise in highly specialized technology (HST) appraisals compared to appraisals from the technology appraisal (TA) guidance (adjusted odds ratio=1.44, 95 percent CI 1.09, 1.90, p=0.004). There was no significant association between paragraphs classified as referencing uncertainty and appraisals for ATMPs, non-oncology RDTs, and RDTs indicated for children only or adults and children. These results were robust to the threshold value used for classifying paragraphs but were sensitive to the choice of classification model. Using supervised learning models for text classification in NICE appraisals for RDTs is feasible, but the results of downstream analyses may be sensitive to the choice of classification model.

Accelerating Image Classification based on a Model for Estimating Descriptor-to-Class Distance

The article describes a method of image classification based on the estimation of the distance to the etalon class. The implementation of estimates gives a significant gain in classification speed compared to linear search while maintaining a decent level of accuracy. The methodology is based on the use of the triangle inequality for images given by a set of binary vectors as descriptors of the image key points. The evaluation is applied to the "object descriptor – etalon" classification method, which is based on the descriptor voting procedure. An analysis of evaluation options is carried out using the parameters of the etalon sets in the form of a medoid and the closest or farthest points from it. The gain in classification time compared to the traditional method proportionally depends on the number of descriptors in the etalon description. Software simulation of classifiers with the implementation of evaluation shows a gain in speed of 350-450 times for the description of 500 descriptors while maintaining one hundred percent classification accuracy on the training set of similar NFT images. A control sample experiment shows that the classifier with estimation can respond better to image details compared to the traditional method.

Nonlinear Feature Extraction Methods Based on Dual-Tree Complex Wavelet Transform Subimages of Brain Magnetic Resonance Imaging for the Classification of Multiple Diseases.

It has been a long time since we use magnetic resonance imaging (MRI) to detect brain diseases and many useful techniques have been developed for this task. However, there is still a potential for further improvement of classification of brain diseases in order to be sure of the results. In this research we presented, for the first time, a non-linear feature extraction method from the MRI sub-images that are obtained from the three levels of the two-dimensional Dual tree complex wavelet transform (2D DT-CWT) in order to classify multiple brain disease. After extracting the non-linear features from the sub-images, we used the spectral regression discriminant analysis (SRDA) algorithm to reduce the classifying features. Instead of using the deep neural networks that are computationally expensive, we proposed the Hybrid RBF network that uses the k-means and recursive least squares (RLS) algorithm simultaneously in its structure for classification. To evaluate the performance of RBF networks with hybrid learning algorithms, we classify nine brain diseases based on MRI processing using these networks, and compare the results with the previously presented classifiers including, supporting vector machines (SVM) and K-nearest neighbour (KNN). Comprehensive comparisons are made with the recently proposed cases by extracting various types and numbers of features. Our aim in this paper is to reduce the complexity and improve the classifying results with the hybrid RBF classifier and the results showed 100 percent classification accuracy in both the two class and the multiple classification of brain diseases in 8 and 10 classes. In this paper, we provided a low computational and precise method for brain MRI disease classification. the results show that the proposed method is not only accurate but also computationally reasonable.

Digital Mapping of Topsoil Texture Classes Using a Hybridized Classical Statistics–Artificial Neural Networks Approach and Relief Data

The demand for high quality and low-cost spatial distribution information of soil texture classes (STCs) is of great necessity in developing countries. This paper explored digital mapping of topsoil STCs using soil fractions, terrain attributes and artificial neural network (ANN) algorithms. The 4493 soil samples covering 10 out of 12 STCs were collected from the rice fields of the Guilan Province of Northern Iran. Nearly 75% of the dataset was used to train the ANN algorithm and the remaining 25% to apply a repeated 10-fold cross-validation. Spatial prediction of soil texture fractions was carried out via geostatistics and then a pixel-based approach with an ANN algorithm was performed to predict STCs. The ANN presented reasonable accuracy in estimating USDA STCs with a kappa coefficient of 0.38 and pixel classification accuracy percentage of 52%. Hybridizing soil particles with relief covariates yielded better estimates for coarse- and medium-STCs. The results also showed that clay particle and terrain attributes are more important covariates than plant indices in areas under single crop cultivation. However, it is recommended to examine the approach in areas with diverse vegetation cover.

Investigation of the effect of parameter estimation and classification accuracy in mixture IRT models under different conditions

This study aims to examine the effects of mixture item response theory (IRT) models on item parameter estimation and classification accuracy under different conditions. The manipulated variables of the simulation study are set as mixture IRT models (Rasch, 2PL, 3PL); sample size (600, 1000); the number of items (10, 30); the number of latent classes (2, 3); missing data type (complete, missing at random (MAR) and missing not at random (MNAR)), and the percentage of missing data (10%, 20%). Data were generated for each of the three mixture IRT models using the code written in R program. MplusAutomation package, which provides the automation of R and Mplus program, was used to analyze the data. The mean RMSE values for item difficulty, item discrimination, and guessing parameter estimation were determined. The mean RMSE values as to the Mixture Rasch model were found to be lower than those of the Mixture 2PL and Mixture 3PL models. Percentages of classification accuracy were also computed. It was noted that the Mixture Rasch model with 30 items, 2 classes, 1000 sample size, and complete data conditions had the highest classification accuracy percentage. Additionally, a factorial ANOVA was used to evaluate each factor's main effects and interaction effects.

Multimodal Biometrics Recognition Using a Deep Convolutional Neural Network with Transfer Learning in Surveillance Videos

Biometric recognition is a critical task in security control systems. Although the face has long been widely accepted as a practical biometric for human recognition, it can be easily stolen and imitated. Moreover, in video surveillance, it is a challenge to obtain reliable facial information from an image taken at a long distance with a low-resolution camera. Gait, on the other hand, has been recently used for human recognition because gait is not easy to replicate, and reliable information can be obtained from a low-resolution camera at a long distance. However, the gait biometric alone still has constraints due to its intrinsic factors. In this paper, we propose a multimodal biometrics system by combining information from both the face and gait. Our proposed system uses a deep convolutional neural network with transfer learning. Our proposed network model learns discriminative spatiotemporal features from gait and facial features from face images. The two extracted features are fused into a common feature space at the feature level. This study conducted experiments on the publicly available CASIA-B gait and Extended Yale-B databases and a dataset of walking videos of 25 users. The proposed model achieves a 97.3 percent classification accuracy with an F1 score of 0.97and an equal error rate (EER) of 0.004.

On significance of constant-Q transform for pop noise detection

Liveness detection has emerged as an important research issue for many biometrics, such as face, iris, hand geometry, etc. and significant research efforts are reported in the literature. However, less emphasis is given to liveness detection for voice biometrics or Automatic Speaker Verification (ASV). Voice Liveness Detection (VLD) can be a potential technique to detect spoofing attacks in ASV system. Presence of pop noise in the speech signal of live speaker provides the discriminative acoustic cue to distinguish between genuine vs. spoofed speech in the framework of VLD. Pop noise comes out as a burst at the lips, which is captured by the ASV system (since the speaker and microphone are close enough), indicating the liveness of the speaker and provides the basis of VLD. In this paper, we present the Constant-Q Transform (CQT) -based approach over the traditional Short-Time Fourier Transform (STFT) -based algorithm (baseline). With respect to Heisenberg’s uncertainty principle in signal processing framework, the CQT has variable spectro-temporal resolution, in particular, better frequency resolution for low frequency region and better temporal resolution for high frequency region, which can be effectively utilized to identify the low frequency characteristics of pop noise. We have also compared proposed algorithm with cepstral features, namely, Linear Frequency Cepstral Coefficients (LFCC) and Constant-Q Cepstral Coefficients. The experiments are performed on recently released POp noise COrpus (POCO) dataset with various statistical, discriminative, and deep learning-based classifiers, namely, Gaussian Mixture Model (GMM), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Light-CNN (LCNN), and Residual Network (ResNet), respectively. The significant improvement in performance, in particular, an absolute improvement of 14.23% and 10.95% in terms of percentage classification accuracy on development and evaluation set, respectively, is obtained for the proposed CQT-based algorithm along with SVM classifier, over the STFT-SVM (baseline) system. Similar trend of the performance improvement is observed for the GMM, CNN, LCNN, and ResNet classifiers for the proposed CQT-based algorithm vs. traditional STFT-based algorithm. The analysis is further extended by simulating the replay mechanism (in the standard framework of ASVSpoof-2019 PA challenge dataset) on the subset of POCO dataset in order to observe the effect of room acoustics onto the performance of the VLD system. By embedding the moderate simulated replay mechanism in POCO dataset, we obtained the percentage classification accuracy of 97.82% on evaluation set.

The Impact of Sample Size and Various Other Factors on Estimation of Dichotomous Mixture IRT Models.

The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11 different sample sizes from 100 to 5000), test length (10, 30, and 50), number of classes (2 and 3), the degree of latent class separation (normal/no separation, small, medium, and large), and class sizes (equal vs. nonequal). Effects were assessed using root mean square error (RMSE) and classification accuracy percentage computed between true parameters and estimated parameters. The results of this simulation study showed that more precise estimates of item parameters were obtained with larger sample sizes and longer test lengths. Recovery of item parameters decreased as the number of classes increased with the decrease in sample size. Recovery of classification accuracy for the conditions with two-class solutions was also better than that of three-class solutions. Results of both item parameter estimates and classification accuracy differed by model type. More complex models and models with larger class separations produced less accurate results. The effect of the mixture proportions also differentially affected RMSE and classification accuracy results. Groups of equal size produced more precise item parameter estimates, but the reverse was the case for classification accuracy results. Results suggested that dichotomous mixture IRT models required more than 2,000 examinees to be able to obtain stable results as even shorter tests required such large sample sizes for more precise estimates. This number increased as the number of latent classes, the degree of separation, and model complexity increased.

Machine learning characterization of cancer patients-derived extracellular vesicles using vibrational spectroscopies: results from a pilot study.

The early detection of cancer is a challenging problem in medicine. The blood sera of cancer patients are enriched with heterogeneous secretory lipid bound extracellular vesicles (EVs), which present a complex repertoire of information and biomarkers, representing their cell of origin, that are being currently studied in the field of liquid biopsy and cancer screening. Vibrational spectroscopies provide non-invasive approaches for the assessment of structural and biophysical properties in complex biological samples. In this pilot study, multiple Raman spectroscopy measurements were performed on the EVs extracted from the blood sera of 9 patients consisting of four different cancer subtypes (colorectal cancer, hepatocellular carcinoma, breast cancer and pancreatic cancer) and five healthy patients (controls). FTIR (Fourier Transform Infrared) spectroscopy measurements were performed as a complementary approach to Raman analysis, on two of the four cancer subtypes. The AdaBoost Random Forest Classifier, Decision Trees, and Support Vector Machines (SVM) distinguished the baseline corrected Raman spectra of cancer EVs from those of healthy controls (18 spectra) with a classification accuracy of above 90 percent when reduced to a spectral frequency range of 1800 to 1940 inverse cm and subjected to a 50:50 training: testing split. FTIR classification accuracy on 14 spectra showed an 80 percent classification accuracy. Our findings demonstrate that basic machine learning algorithms are powerful applied intelligence tools to distinguish the complex vibrational spectra of cancer patient EVs from those of healthy patients. These experimental methods hold promise as valid and efficient liquid biopsy for artificial intelligence-assisted early cancer screening.

A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification

The ubiquitous usage of feature selection in search space optimization, information retrieval, data mining, signal processing, software fault prediction, and bioinformatics is paramount to expert and intelligent systems. Most of the conventional feature selection methods implemented are based on filter and wrapper approaches that suffer from poor classification accuracy, high computational cost, and selection of irrelevant and redundant features. This is due to the limitations of the employed objective functions leading to overestimation of the feature significance. On the contrary, hybrid feature selection methods formulated from information theory and nature-inspired metaheuristic algorithms are preferred because of their high computational efficiency, scalability in avoiding redundant and less informative features, and independence from the classifier. However, these methods have three common drawbacks: (1) poor trade-off between exploration and exploitation phase, (2) getting stuck into an optimal local solution, and (3) avoiding irrelevancy and redundancy of selected features. The first and the second drawback is related to metaheuristic algorithm implementation, while the third is concerned with applied information-theoretic paradigms. To address the aforementioned problems, we developed a new hybrid feature selection method, namely, the Iterative Feature Selection using Dynamic Butterfly Optimization Algorithm based Interaction Maximization (IFS-DBOIM) that combines Dynamic Butterfly Optimization Algorithm (DBOA) with a mutual information-based Feature Interaction Maximization (FIM) scheme for selecting the optimal feature subset. There is evidence that DBOA performs better in exploration, exploitation, and avoidance of local optima entrapment, and FIM comparatively scores the maximum relevancy with minimum redundancy of the new features with previously selected ones. The performance of the proposed method is compared using twenty publicly available datasets with ten baseline feature selection approaches. The results revealed that IFS-DBOIM outperforms other approaches on most datasets, maximizing the percent classification accuracy with the least number of features. The nonparametric Wilcoxon rank test confirms the statistical significance of these outcomes. Moreover, this method realizes the best trade-off between accuracy and stability.

Extrapolation of Loan Default using Predictive Analytics: A Case of Business Analysis

The research assesses the validity of a customer's appropriateness for a loan using a machine learning approach called predictive modeling. Banks and Non-Banking Financial Companies (NBFCs) are at danger of significant Non-Performing Assets (NPAs) due to customer non-payment of loans (Non-Performing Assets). The data for this study came from Kaggle, and eight different prediction models were employed to determine if the borrower would be able to repay the loan. Adaboost, κ-Nearest Neighbors (k-NN), Logistic Regression, Support Vector Machines (SVM), Decision Tree, Naive Bayes, Neural Networks, and Random Forest (RF) are the eight models, respectively. The purpose is to back up decisions made on the basis of factual evidence rather than subjective reasons. Classification Accuracy, Precision, Recall, and F-1 scores are the four performance parameters used to determine the results. With 70% and 30% respectively, the dataset is separated into train and test datasets. The whole analysis is done in two phases, with the first being a full model that is trained on 70% of the train data and the second being observed on 30% of the test data. The purpose of this study is to see how objective characteristics influence borrowers to default on loans, to identify the most common reasons for default, and to predict which customers would default. There are two evaluations we did for the research, wherein, first we took overall train set and make predictions using predictive modeling. The Adaboost predictive model delivers the greatest results, with a recall rate of 0.384, classification accuracy of 59.2 percent, true-positive rate of 69.74 percent. Second, we performed feature selection and discovered that Credit History with 31 percent had the utmost impact on loan default detection. By partitioning the dataset into Credit_History 1 and 0, we discovered that Credit History 1 produces superior results, with a rate of 0.444, 60.5 percent classification accuracy, and a true-positive rate of 68.7%.

Characteristics of gaze tracking during movement analysis by therapists.

[Purpose] Visual assessment of the quality of movement is a common and important component of physiotherapy. The purpose of this study is to quantify the level of proficiency of therapists and to obtain a new index of proficiency by measuring the coordinates of the gaze tracking trajectories of therapists with years of experience. [Participants and Methods] Eighteen voluntary physiotherapists (1st year (n=4), 7th year (n=1), 9th year (n=4), 10th year (n=3), 11th year (n=4), 13th year (n=1), and 21st year (n=1)) were recruited for this study. [Results] Discriminant analysis according to the size of the deviation between the X-axis and Y-axis of the range of gaze tracking during movement analysis measured from each therapist showed that the percentage of classification accuracy in the 10th year or less was 72.2%. Cluster analysis showed that two clusters were formed. Thirteen therapists in Cluster 2 were in their 9th year or more. Eye tracking trajectories can be classified by the 10th year of experience as a therapist. [Conclusion] It was shown that full-fledged therapists with 10 years of experience also expanded the range of eye tracking. The trajectory in the Y-axis direction tends to be extended with their 9th year or more of experience. In this point, quantitative judgments of eye-tracking results can serve as indicators of proficiency. The eye movements are important as a tool to objectively measure skills from experience.

Optimized Polynomial Classifier for Classification of M-PSK Signals

Automatic modulation classification (AMC) is the emerging research area for military and civil applications. In this paper, M-PSK signals are classified using the optimized polynomial classifier. The distinct features i.e., higher order cumulants (HOC’s) are extracted from the noisy received signal and the dataset is generated with different number of samples, various SNR’s and on several fading channels. The proposed classifier structure classifies the overall modulation classification problem into binary sub-classifications. In each sub-classification, the extracted features are expanded using polynomial expansion into higher dimension space. In higher dimension space numerous non-linearly separable classes becomes linearly separable. The performance of the proposed classifier is evaluated on Rayleigh and Rician fading channels in the presence of additive white gaussian noise (AWGN). The polynomial classifier performance is optimized using one of the famous heuristic computational techniques i.e., Genetic Algorithm (GA). The extensive simulations have been carried with and without optimization, which shows relatively better percentage classification accuracy (PCA) as compared with the state of art existing techniques.

Classification of gear faults in internal combustion (IC) engine gearbox using discrete wavelet transform features and K star algorithm

Vibration-based fault diagnosis is one of the widely used techniques for condition monitoring of the machines equipped with a gearbox. Severe operating conditions of gearbox result in gear tooth failure. To develop an effective fault diagnosis technique for the mechanical system, a machine learning approach is highly necessary and plays a vital role in the area of condition monitoring. This paper presents the vibration-based fault diagnosis of IC engine gearbox operating under actual running condition. An Eddy current dynamometer is used to apply the external load on the output shaft of the engine. Driving gear with healthy condition and progressive tooth defect conditions are considered for the analysis. The vibration signals of engine gearbox under various gear tooth conditions are measured. Discrete wavelet transform features are extracted from the vibration signals and more contributing features for classification are selected using decision tree algorithm. The Lazy based classifiers viz, k-nearest neighbour algorithm, K-star algorithm and locally weighted learning algorithm are used for classification. A comparative study of these classifiers is made using percentage of classification accuracy. The maximum classification accuracy of about 97.5% is achieved by the K-star algorithm. Based on the experimental results, K-star algorithm and discrete wavelet transform technique can be used for diagnosing the gear faults in IC engine gearbox using vibration signals.

Rapid discrimination and quantification of kudzu root with its adulterant part using FT-NIR and a machine learning algorithm

Kudzu root (KR) is regarded as a natural health food with high medicinal value, and is commercially available in dietary supplements that are primarily marketed for women’s health. The nutrient and functional substances are mainly distributed in the root; and moreover, the appearance and taste of Kudzu stem (KS) are both similar to KR. Thus, the ability to differentiate KR and KS and to detect adulteration of KR with the addition of cheaper KS is important in preventing the possible commercial fraud that adversely affects both the food industry as well as the consumers. With this assumption, the present work had two different goals of developing a rapid, simple and accurate method to distinguish KR from KS, and to test the feasibility of quantifying the adulteration ratio of KR to KS. To achieve these goals, NIR spectroscopy was coupled with 4 different algorithms including the principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), partial least squares (PLS) test, and least squares support vector machine (LS-SVM). The results showed that PLS-DA achieved 100 percent classification accuracy for both KR and KS. The LS-SVM model had superior capability than did the PLS model in predicting the adulteration ratio between KR and KS. It has provided excellent predictive performance with a high Rp value of 0.9972 and a low root mean square error of prediction (RMSEP) value of 2.1724. These observations revealed the potential of NIR spectroscopy combined with machine learning methods as effective approach in distinguishing possible adulteration of KR.

FV 6. Factors influencing the adequacy of determining hemispheric language lateralization in presurgical epilepsy patients using functional transcranial Doppler sonography (fTCD)

Introduction . Functional transcranial Doppler sonography (fTCD) has become part of the routine work-up of epilepsy patients considered as candidates for epilepsy surgery in many epilepsy centers [1]. The adequacy of determining hemispheric language lateralization (HLL) using fTCD is of special importance, given its contribution to the prediction of possible postsurgical cognitive risks, and thus the planning of epilepsy surgery. Therefore, this study aimed to identify predictive factors for receiving inconclusive HLL results using fTCD. Methods . Data from 266 epilepsy patients who received fTCD to determine HLL before epilepsy surgery between 2015 and 2020 was analyzed retrospectively. A phonemic word generation paradigm was used, and fTCD was performed in a standardized fashion as described previously [2]. Logistic regression analyses were conducted to model the odds of receiving inconclusive HLL results based on several potential predictor variables. Results . Hemispheric language lateralization was left-sided in 147 patients (55.2%), right-sided in 43 patients (16.2%), and inconclusive in 76 patients (28.6%). Univariate logistic regression analyses revealed significant associations between a reduced probability of inconclusive HLL results and higher age, female gender, right-handedness, German native language, more years of education, older age at onset of epilepsy, temporal localization of the epileptogenic focus, etiology of hippocampal sclerosis, higher verbal IQ, and higher semantic and phonemic verbal fluency. In a multivariate model (overall percentage accuracy in classification = 71.5%, p < 0.001), only years of education (adjusted OR = 0.91, p < 0.001) remained independently predictive for a reduced probability of inconclusive HLL results. Discussion. Our findings indicate that the adequacy of determining HLL in presurgical epilepsy patients using fTCD is highly dependent on the patients“ education. On this basis, we recommend not to use the same paradigm for all patients, but to individually adapt the complexity of the task to the cognitive abilities of each patient. Therefore, the development and standardization of additional word generation paradigms for fTCD (e.g. based on semantic cues, or picture description) is encouraged, to further facilitate an appropriate presurgical evaluation of every epilepsy surgery candidate.

Improved multi-class discrimination by Common-Subset-of-Independent-Variables Partial-Least-Squares Discriminant Analysis

In this study, a new PLS-DA modelling approach for multi-class discriminant analysis, called Common-Subset-of-Independent-Variables Partial-Least-Squares Discriminant Analysis is proposed and evaluated. Because in this method Partial-Least-Squares models for one component are used, it is denoted as CSIV-PLS1-DA. In this method for each class vector, individual PLS1 models with individual model complexities are developed, based on one common set of independent variables, obtained after variable selection by the Final Complexity Adapted Models method, using the absolute values of the PLS regression coefficients, denoted as FCAM-REG. CSIV-PLS1-DA combines a common variable set for all class vectors, which is a characteristic of PLS2-DA, with the individual model complexity for each class vector, which is a characteristic of PLS1-DA. These characteristics make CSIV-PLS1-DA more flexible than PLS2-DA. CSIV-PLS1-DA is found to be an alternative for PLS1-DA or PLS2-DA when the correlations between the responses are low, which is often the case in discriminant analysis.The performance of the CSIV-PLS1-DA method is investigated using one simulated and eight real multi-class data sets from different sources. The classification abilities, measured by the percentage classification accuracy rates (%Acc), resulting from CSIV-PLS1-DA, are statistically compared with those of PLS1-DA and PLS2-DA, using one-tailed paired t-tests at the 95% confidence level.The results show that the %Acc values resulting from the CSIV-PLS1-DA method are significantly higher than those of the corresponding PLS1-DA and PLS2-DA methods, meaning that the classification ability of the CSIV-PLS1-DA method is significantly better.

Classification of SSVEP-based BCIs using Genetic Algorithm

Brain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

Morphology-based identification and classification of Pediastrum through AlexNet Convolution Neural Network

In general solutions and outcomes to an issue in image processing, a ton of trail and examination with an enormous assortment of test photographs is incorporated. Pediastrum, which comprises of pigments like carotene, xanthophyll, chlorophylls, and other nourishing elements like complex sugars, major and minor minerals, and protein, enzymes, fiber, is a minute coccal shaped colony forming green algae. In this research, an attempt was made to solve the problem of identifying and classifying different species of Pediastrum with the aid of CNN based deep learning model. The informational collection comprising of 12,000 algal pictures was being utilized by, AlexNet CNN (Convolution Neural Network) model for the training and validating purposes. The features like the presence of sporopollenin in the cell wall, the structures and the functions of the cells, and the structural properties of coenobia make the basis for automated classification by AlexNet deep learning model. The efficacy of the proposed approach is demonstrated by an experimental outcome of 99.54 percent classification accuracy with precision and Fl-score more than 0.98.

Analysis on Wavelet Feature and Softmax Discriminant Classifier for the detection of epilepsy

The most frequently diagnosed brain disease is epilepsy, which is characterised by the unexpected onset of frequent seizures. The detection of epilepsy in this paper was established by using the wavelet features Haar, dB2, Symlets (Sym8) and dB4, followed by the Softmax Discriminant Classifier, which uses to detect the epilepsy from the EEG signals. The performance of the wavelet features and classifier is evaluated based on the performance index, specificity, sensitivity, precision, time delay and quality values. Amongthe wavelet features, the sym8 performs better than the other and processed further using the Softmax Discriminant Classifier, which outperforms the 90.93 percent classification accuracy, with a low time delay of 1.991s, the 72.61 percent output index, the most promising result in this work.