PENGARUH ALOKASI PEMBIAYAAN BERDASARKAN JENIS AKAD MUDHARABAH, JENIS PENGGUNAAN MODAL KERJA DAN GOLONGAN DEBITUR NON UMKM TERHADAP NON PERFORMING FINANCING PERBANKAN SYARIAH DI INDONESIA TAHUN 2013-2015

ABSTRACT ANISSA NUR RAMADHANI. The Effects Of Financing Allocation Based On Kind Of Akad Mudharabah, Kind Of Working Capital Application, And Classification Of Non SME’s Debtor Towards Non Performing Financing Islamic Bank In Indonesia Year 2013-2015 This study aims to analyze The Effects of Financing Allocation Based on Kind of Akad Mudharabah, Kind of Working Capital Application, and Classification of Non SME’s Debtor Towards Non Performing Financing Islamic Bank in Indonesia Year 2013-2015. The research method used is time series data from January 2013 - December 2015 with expose facto approach. Data were obtained from Bank Indonesia and Otoritas Jasa Keuangan. Data analysis technique used in this research is multiple linear regression analysis. By using multiple regression analysis model, the output shows that the financing allocation based on akad mudharabah has a negative significant effect to Non Performing Financing while akad murabahah has positive significant effect to Non Performing Financing. Financing allocation based on kind application for working capital has a significant positive effect on Non Performing Financing while financing allocation based on kind application for investment has a negative significant effect on Non Performing Financing. Financing allocation based on non-SME debtor group has a positive significant effect on Non Performing Financing while the allocation of financing based on class of SME's debtors has a negative significant effect on Non Performing Financing. From the result of F test shows the significance value = 0.000000 < 0.05 it can be said financing allocation based on kind of akad, kind of application, and classification of debtor towards non performing financing islamic bank in indonesia simultaneously have a significant effect on a = 5% to Non Performing Financing of sharia banking in Indonesia. The coefficient of determination (R) is 0.931389 or 93.13%. Keywords: Non Performing Financing, Kind of Akad Mudharabah, Kind of Working Capital Application, Classification of Non SME’s Debtor

DEVELOPMENTS IN NONPARAMETRIC REGRESSION METHODS WITH APPLICATION TO RAMAN SPECTROSCOPY ANALYSIS

Raman spectroscopy has been successfully employed in the classification of breast pathologies involving basis spectra for chemical constituents of breast tissue and resulted in high sensitivity (94%) and specificity (96%) (Haka et al, 2005). Motivated by recent developments in nonparametric regression, in this work, we adapt stacking, boosting, and dynamic ensemble learning into a nonparametric regression framework with application to Raman spectroscopy analysis for breast cancer diagnosis. In Chapter 2, we apply compound estimation (Charnigo and Srinivasan, 2011) in Raman spectra analysis to classify normal, benign, and malignant breast tissue. We explore both the spectra profiles and their derivatives to differentiate different types of breast tissue. In Chapters 3-5 of this dissertation, we develop a novel paradigm for incorporating ensemble learning classification methodology into a nonparametric regression framework. Specifically, in Chapter 3 we set up modified stacking framework and combine different classifiers together to make better predictions in nonparametric regression settings. In Chapter 4 we develop a method by incorporating a modified AdaBoost algorithm in nonparametric regression settings to improve classification accuracy. In Chapter 5 we propose a dynamic ensemble integration based on multiple meta-learning strategies for nonparametric regression based classification. In Chapter 6, we revisit the Raman spectroscopy data in Chapter 2, and make improvements based on the developments of the methods from Chapter 3 to Chapter 4. Finally we summarize the major findings and contributions of this work as well as identify opportunities for future research and their public health implications

ENSEMBLE STACKING DALAM ANALISA SENTIMEN REAKSI VETERAN MILITER AS TERHADAP PENGAMBILALIHAN AFGHANISTAN OLEH TALIBAN

Abstrak— Sentiment analysis can be used to glean information about user opinions and identify social or political trends. There have been many studies on sentiment analysis using machine learning or lexicon-based methods that have been quite impressive. However, machine learning models often have difficulty generalizing to new data due to various reasons, such as overfitting and limited training data. These models are also prone to bias and variance, which negatively affect the accuracy of their predictions. This study discusses the application of the ensemble stacking method in sentiment analysis with the topic of the takeover of Afghanistan by the Taliban. By monitoring social media, the author uses a dataset in the form of comments on YouTube news channels related to the topic raised. Several studies have shown how the ensemble stacking method predicts better than the single model. The research was carried out by creating a sentiment classification model with logistic regression machine learning algorithms, SVM, KNN, and CART then the ensemble stacking classifier formed by the base learner of the four algorithms. As a result, for a single classifier, the highest average accuracy is the logistic regression algorithm of 74.6 percent. The four algorithms are compiled and predicted by logistic regression, and the stacking ensemble classifier that is applied produces better accuracy than the stand-alone classifier, which is 75.3 percen

Penerapan Combine Undersampling Pada Klasifikasi Data Imbalanced Biner (Studi Kasus : Desa Tertinggal Di Jawa Timur Tahun 2014)

Regresi Logistik memiliki beberapa kelebihan dibandingkan metode klasifikasi lainnya yaitu sebagai classifier dengan akurasi yang cukup tinggi dan penggunaan algoritma yang tepat akan mampu menghasilkan waktu perhitungan yang lebih cepat khususnya pada data besar. Adanya permasalahan data yang tidak seimbang akan berpengaruh pada hasil ketepatan klasifikasi. Pada penelitian ini, metode resampling yang digunakan adalah Combine Undersampling dan metode classifier yang digunakan adalah Regresi Logistik, Regresi Logistik Ridge, dan Analisis Diskriminan Kernel. Data yang diteliti adalah data status desa tertinggal di Jawa Timur tahun 2014 sebanyak 7.721 desa. Penerapan Combine Undersampling mampu meningkatkan ketepatan klasifikasi pada rata-rata sensitivititas secara siginifikan khususnya pada klasifikasi Regresi Logistik Ridge sebesar 42,4 kali dengan menggunakan semua variabel. Selain itu, hasil ketepatan klasifikasi terbaik menunjukkan nilai akurasi total dan AUC yang sama ketika menerapkan metode Combine Undersampling pada Klasifikasi Analisis Diskriminan Kernel yaitu 78.0 % sedangkan pada variabel signifikan metode Regresi Logistik Ridge menghasilkan ketepatan klasifikasi lebih baik dari metode lainnya yang memiliki nilai AUC sebesar 73,5% . ========================================================= Logistic regression has several advantages over other classification methods that is as a classifier with a fairly high accuracy and the use of appropriate algorithms will be able to produce faster calculation times, especially on large data. The existence of imbalanced data problems will affect the results of classification accuracy. In this research, the resampling method used is Combine Undersampling and the classifier method used are Logistic Regression, Ridge Logistic Regression, and Kernel Discriminant Analysis. The data studied is the status data of underdevelop villages in East Java in 2014 as many as 7,721 villages. The application of Combine Undersampling is able to increase the classification accuracy on the average sensitivity significantly in the Ridge Logistic Regression classification by 42.4 times using all the variables. In addition, the best classification accuracy results show the same total accuracy and AUC value when applying Combine Undersampling method in the Kernel Discriminant Classification Classification is 78.0% whereas in the significant variables the Ridge Logistic Regression method produces better classification accuracy than other methods which have AUC value of 73 , 5%

Scene illumination classification based on histogram quartering of CIE-Y component

Despite the rapidly expanding research into various aspects of illumination estimation methods, there are limited number of studies addressing illumination classification for different purposes. The increasing demand for color constancy process, wide application of it and high dependency of color constancy to illumination estimation makes this research topic challenging. Definitely, an accurate estimation of illumination in the image will provide a better platform for doing correction and finally will lead in better color constancy performance. The main purpose of any illumination estimation algorithm from any type and class is to estimate an accurate number as illumination. In scene illumination estimation dealing with large range of illumination and small variation of it is critical. Those algorithms which performed estimation carrying out lots of calculation that leads in expensive methods in terms of computing resources. There are several technical limitations in estimating an accurate number as illumination. In addition using light temperature in all previous studies leads to have complicated and computationally expensive methods. On the other hand classification is appropriate for applications like photography when most of the images have been captured in a small set of illuminants like scene illuminant. This study aims to develop a framework of image illumination classifier that is capable of classifying images under different illumination levels with an acceptable accuracy. The method will be tested on real scene images captured with illumination level is measured. This method is a combination of physic based methods and data driven (statistical) methods that categorize the images based on statistical features extracted from illumination histogram of image. The result of categorization will be validated using inherent illumination data of scene. Applying the improving algorithm for characterizing histograms (histogram quartering) handed out the advantages of high accuracy. A trained neural network which is the parameters are tuned for this specific application has taken into account in order to sort out the image into predefined groups. Finally, for performance and accuracy evaluation misclassification error percentages, Mean Square Error (MSE), regression analysis and response time are used. This developed method finally will result in a high accuracy and straightforward classification system especially for illumination concept. The results of this study strongly demonstrate that light intensity with the help of a perfectly tuned neural network can be used as the light property to establish a scene illumination classification system

On the predictability of U.S. stock market using machine learning and deep learning techniques

Conventional market theories are considered to be inconsistent approach in modern financial analysis. This thesis focuses mainly on the application of sophisticated machine learning and deep learning techniques in stock market statistical predictability and economic significance over the benchmark conventional efficient market hypothesis and econometric models. Five chapters and three publishable papers were proposed altogether, and each chapter is developed to solve specific identifiable problem(s). Chapter one gives the general introduction of the thesis. It presents the statement of the research problems identified in the relevant literature, the objective of the study and the significance of the study. Chapter two applies a plethora of machine learning techniques to forecast the direction of the U.S. stock market. The notable sophisticated techniques such as regularization, discriminant analysis, classification trees, Bayesian and neural networks were employed. The empirical findings revealed that the discriminant analysis classifiers, classification trees, Bayesian classifiers and penalized binary probit models demonstrate significant outperformance over the binary probit models both statistically and economically, proving significant alternatives to portfolio managers. Chapter three focuses mainly on the application of regression training (RT) techniques to forecast the U.S. equity premium. The RT models demonstrate significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. Chapter four investigates the statistical predictive power and economic significance of financial stock market data by deep learning techniques. Chapter five give the summary, conclusion and present area(s) of further research. The techniques are proven to be robust both statistically and economically when forecasting the equity premium out-of-sample using recursive window method. Overall, the deep learning techniques produced the best result in this thesis. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk

인체 동작 및 자세 분석을 위한 심화 학습 인공신경망 설계 및 적용

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업·조선공학부, 2020. 8. 윤명환.Ergonomic research is conducted through observation, measurement, and analysis. Ergonomic research has also been developed due to the development of technologies related to observation, measurement, and analysis. Deep-learning technology is a core technology for artificial intelligence development. Various attempts have been made to complement and replace human capabilities like observation, measurement, and analysis, using deep-learning technologies. This deep-learning technology can be applied to various stages of the ergonomic research process. Therefore, in this research, various attempts were made to prepare methods for applying deep-learning to ergonomic research. This thesis attempted to analysis via deep-learning to various kinds of data, such as numerical data, image data, and video data. Besides, to identify the characteristics of data that can be applied to deep-learning, different data collecting methods were applied. The data types were data collected for deep- learning, data collected without considering deep-learning, and data collected and released by the government. The first research is to detect sitting posture from body pressure distribution data. Back health is closely related to the users sitting posture, so it is crucial to have a good sitting posture when young. In a controlled environment, body pressure distribution image data for seven postures were collected from children. The deep-learning method used for posture classification is a convolutional neural network (CNN). The classification performance of logistic regression and CNN is compared. As a result, CNN showed a 20% improvement over logistic regression in the overall classification performance. The second research is to derive work risk assessments using assembly process videos. The data used in the study were those used in the work risk assessment. The performance was evaluated by applying LSTM, one of the deep- learning methods, to the work risk assessment methods OWAS, RULA, and REBA. As a result, when performing OWAS with deep-learning, it showed better performance than RULA and REBA. The third research estimates the stature from hand dimensions. The data used in this research were investigated and released by the government. In the previous study, the stature was estimated from hand dimensions using linear regression. Linear regression, RNN, and the recursive generalized linear model (RGLM) were applied to compare the performance of stature estimation. As a result, deep learning techniques RNN and RGLM performed better than linear regression. Through three research, it was confirmed that the deep-learning method could replace the existing research method. Although the absolute performance was not excellent, it showed relatively good performance than the existing method. The deep-learning method was different depending on the data format and condition. The performance difference also occurred according to the kind of deep-learning method. If the various cases were not learned, no results were obtained for the missing parts. Therefore, data selection and pre-processing must be preceded while applying deep-learning. In ergonomic research, deep-learning will make it easy to reflect the results of ergonomic research into reality. Deep-learning will not replace the researcher but will broaden the research subjects scope and make the research results widely available.인간공학 연구는 관찰, 측정, 분석을 통해 이루어진다. 관찰, 측정, 분석과 관련된 기술의 발달로 인해 인간공학 연구 역시 발달해 왔다. 딥러닝 기술은 인공지능 개발을 위한 핵심기술이다. 딥러닝을 활용하여 인간의 관찰, 측정, 분석 능력을 보 완하고, 대체 하려는 다양한 시도들이 이루어지고 있다. 이러한 딥러닝은 인간공학 연구 과정의 다양한 단계에 적용될 수 있다. 이에, 본 연구에서는 인간공학 연구에 딥러닝을 응용할 수 있는 방안을 마련하기 위해 다양한 시도를 하였다. 본 연구에서는 수치 데이터, 이미지 데이터, 영상 데이터와 같은 다양한 형태의 데이터를 대상으로 딥러닝을 적용하려는 시도를 하였다. 또한, 딥러닝을 적용할 수 있는 데이터의 특성을 파악하기 위해 데이터 수집형태를 달리 적용했다. 그 데이 터 형태는 딥러닝을 위해 수집된 데이터, 딥러닝을 고려하지 않고 수집된 데이터, 정부가 수집해 공개한 데이터이다. 첫 번째 연구는 체압분포 데이터로부터 앉은 자세를 감지하는 것이다. 허리 건강은 앉은 자세 습관과 밀접하므로, 어렸을 때 좋은 앉은 자세를 갖게 하는 것이 중요하다. 어린이를 대상으로 통제된 환경에서 7가지 자세에 따른 압력분포 이미 지 데이터가 수집되었다. 자세 분류에 사용한 딥러닝 방법은 합성곱 신경망(CNN) 이며, 로지스틱 회귀 (logistic regression)와 그 분류 성능을 비교하였다. 그 결과, 전체 분류 성능에서 CNN이 로지스틱 회귀보다 20%가량 향상을 보여주었다. 두 번째 연구는 조립 공정 영상으로부터 작업 위해도 평가 결과를 도출하는 것 이다. 딥러닝을 위해 준비된 데이터가 아닌, 작업 위해도 평가를 위해 촬영되었던 영상 데이터와 평가 결과를 대상으로 하였다. 작업 위해도 평가를 위해 사용되는 OWAS, RULA, REBA 세 가지 평가 방법에 딥러닝 방법인 LSTM을 적용하여 그 성능을 비교하였다. 그 결과, 딥러닝으로 OWAS 평가를 했을 때, RULA, REBA에 비해 좋은 성능을 보여주었다. 세 번째 연구는 손의 여러 치수로부터 키를 추정하는 것이다. 정부 단위로 조 사하여 공개한 데이터를 대상으로 하였다. 기존 연구에서는 선형회귀를 이용하여 손의 수치로부터 키를 추정하였다. 이에 본 연구에서는 딥러닝 방법인 RNN과 재 귀적 일반화 선형 모형 (RGLM)을 적용하여 그 추정 성능을 비교하였다. 그 결과, RGLM과 RNN은 선형회귀에 비해 좋은 성능을 보여주었다. 세 연구를 통해, 딥러닝 방법이 기존의 연구 방법을 대체할 수 있음을 확인하 였다. 절대적인 성능이 좋지는 않았지만, 기존 방법보다 상대적으로 좋은 성능을 보여주었다. 데이터 형식에 따라 적용할 수 있는 딥러닝 방법이 달랐으며, 딥러닝 방법에 따라서도 성능 차이가 발생했다. 다양한 케이스에 대해 학습이 되지 않은 경우, 누락된 부분에 대해서는 결과를 도출하지 못했다. 따라서, 딥러닝 적용에는 데이터 선별 및 가공이 선행되어야 한다. 인간공학 연구에 있어서, 인간공학 연구 결과물이 딥러닝을 통해 현실에 쉽게 반영될 수 있을 것이다. 딥러닝은 연구자를 대체하는 것이 아니라 연구 대상 범위와 활용 범위를 넓혀줄 것이다.Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Purpose of This Study 4 1.3 Organization of the thesis 5 Chapter 2 Literature Reviews 9 2.1 Sitting Posture 9 2.2 Working Posture Measurement 15 2.3 Anthropometric Dimension Estimation 20 2.4 Deep-learning Application 22 Chapter 3 An Ergonomic Analysis of Seated Posture using a Deep-learning Method 25 3.1 Overview 25 3.2 Data Characteristics 26 3.2.1 Body Pressure Distribution on Seat Cushion 26 3.2.2 Data Collection 27 3.2.3 Data Pre-processing 29 3.3 Data Analysis 32 3.3.1 Convolutional Neural Network 32 3.3.2 Performance Comparison Method 34 3.4 Results 36 3.4.1 Logistic Regression 36 3.4.2 Convolutional Neural Networks 39 3.4.3 Comparison of Logistic Regression Results and Convolutional Neural Networks Results 42 3.5 Discussion 44 Chapter 4 Applying Deep-learning Methods to Human Motion Analysis of Automobile Assembly Tasks 47 4.1 Overview 47 4.2 Data Characteristics 48 4.2.1 Work-related Musculoskeletal Disorders(WMSDs) in FactoryWorkers 48 4.2.2 Data Collection 49 4.2.3 Data Pre-processing 50 4.3 Data Analysis 52 4.4 Results 52 4.4.1 OWAS Prediction Model 52 4.4.2 RULA Prediction Model 53 4.4.3 REBA Prediction Model 54 4.5 Discussion 55 Chapter 5 Estimation of Hand Anthropometric Dimensions Using a Deep-learning Method 59 5.1 Overview 59 5.2 Data Characteristics 60 5.2.1 Size Korea; A National Anthropometric Survey of Korea 60 5.2.2 Hand Anthropometric Measurement Data 61 5.2.3 Data Selection and Hand Dimension 62 5.2.4 Training Data and Test Data 64 5.3 Data Analysis 65 5.4 Result 66 5.4.1 Comparison of Relative Absolute Error(RAE) 68 5.4.2 Comparison of Relative Squared Error(RSE) 70 5.4.3 Comparison of Mean Absolute Percentage Error(MAPE) 72 5.4.4 Comparison of Mean Absolute Scaled Error(MASE) 74 5.4.5 Comparison of Root Mean Square Error(RMSE) 76 5.4.6 Comparison of Mean Absolute Error(MAE) 78 5.4.7 Comparison of Mean Squared Error(MSE) 80 5.4.8 Clustering the Results Along with the Performance 82 5.5 Discussion 83 Chapter 6 Discussion and Conclusions 87 6.1 Summary of findings 87 6.2 Contributions of this study 89 6.3 Limitations and further studies 92 Bibliography 95 Appendix A Confusion Matrix from Chapter III 104 Appendix B Python Code for Chapter III 125 Appendix C Python Code for Chapter IV 129 Appendix D Python Code for Chapter V 141Docto

Crop identification and area estimation through the combined use of satellite and field data for county Durham, northern England

This thesis investigates the use of combined field and satellite data for crop identification and area estimation in County Durham, Northeast England. The satellite data were obtained by the Thematic Mapper (TM) sensor onboard Landsat-5 on 31 May 1985. The TM data were geometrically corrected to the British National Grid and the county boundaries were digitized in order to apply the methodology used in this study on a county basis. The field data were obtained by applying a stratified random sampling strategy. The area was subdivided into five main strata and forty four 1km(_^2) sample units were randomly chosen and fully surveyed by the author using a pre-prepared questionnaire. The field area measurements were taken and the final hectarage estimates were obtained for each crop. The research demonstrated the ability of Landsat-TM data to discriminate between agricultural crops in the study area. Results obtained emphasised that satellite data can be used for identification of agricultural crops over large geographic areas with small field sizes and different environmental and physical features. A land-cover classification system appropriate to the study area was designed. Using the Landsat-TM data, the study produced a classification map of thirteen land-cover types with more than 80% accuracy. The classification accuracy was assessed quantitatively by using the known land-use information obtained from the sample units visited during the field survey. The study analysed the factors which influenced the degree of separability between different agricultural crops since some crops were more clearly identified than others. Using a double sampling method based on the combination of both Landsat- TM and field data in regression analysis, a hectarage estimate was produced for each crop type in County Durham. The results obtained showed that the regression estimator was always more efficient than the field estimator. Crop area estimated by regression reduced the imprecision in all strata and was more efficient in some strata than others. This indicated that a gain in precision was achieved by using Landsat- TM in conjunction with the field data. The results illustrated that stratification based on an environmental criterion was an efficient approach as far as the the application of agricultural remote sensing in County Durham is concerned. The stratified approach allowed each stratum to be analysed separately, thereby lessening the reliance on cloud free imagery for the whole county on any given date. Furthermore, the results obtained by this study suggest that it is possibile to link remote sensing data with existing county based information systems on agricultural and land-use

Advanced imaging and data mining technologies for medical and food safety applications

As one of the most fast-developing research areas, biological imaging and image analysis receive more and more attentions, and have been already widely applied in many scientific fields including medical diagnosis and food safety inspection. To further investigate such a very interesting area, this research is mainly focused on advanced imaging and pattern recognition technologies in both medical and food safety applications, which include 1) noise reduction of ultra-low-dose multi-slice helical CT imaging for early lung cancer screening, and 2) automated discrimination between walnut shell and meat under hyperspectral florescence imaging. In the medical imaging and diagnosis area, because X-ray computed tomography (CT) has been applied to screen large populations for early lung cancer detection during the last decade, more and more attentions have been paid to studying low-dose, even ultra-low-dose X-ray CTs. However, reducing CT radiation exposure inevitably increases the noise level in the sinogram, thereby degrading the quality of reconstructed CT images. Thus, how to reduce the noise levels in the low-dose CT images becomes a meaningful topic. In this research, a nonparametric smoothing method with block based thin plate smoothing splines and the roughness penalty was introduced to restore the ultra-low-dose helical CT raw data, which was acquired under 120 kVp / 10 mAs protocol. The objective thorax image quality evaluation was first conducted to assess the image quality and noise level of proposed method. A web-based subjective evaluation system was also built for the total of 23 radiologists to compare proposed approach with traditional sinogram restoration method. Both objective and subjective evaluation studies showed the effectiveness of proposed thin-plate based nonparametric regression method in sinogram restoration of multi-slice helical ultra-low-dose CT. In food quality inspection area, automated discrimination between walnut shell and meat has become an imperative task in the walnut postharvest processing industry in the U.S. This research developed two hyperspectral fluorescence imaging based approaches, which were capable of differentiating walnut small shell fragments from meat. Firstly, a principal component analysis (PCA) and Gaussian mixture model (PCA-GMM)-based Bayesian classification method was introduced. PCA was used to extract features, and then the optimal number of components in PCA was selected by a cross-validation technique. The PCA-GMM-based Bayesian classifier was further applied to differentiate the walnut shell and meat according to the class-conditional probability and the prior estimated by the Gaussian mixture model. The experimental results showed the effectiveness of this PCA-GMM approach, and an overall 98.2% recognition rate was achieved. Secondly, Gaussian-kernel based Support Vector Machine (SVM) was presented for the walnut shell and meat discrimination in the hyperspectral florescence imagery. SVM was applied to seek an optimal low to high dimensional mapping such that the nonlinear separable input data in the original input data space became separable on the mapped high dimensional space, and hence fulfilled the classification between walnut shell and meat. An overall recognition rate of 98.7% was achieved by this method. Although the hyperspectral fluorescence imaging is capable of differentiating between walnut shell and meat, one persistent problem is how to deal with huge amount of data acquired by the hyperspectral imaging system, and hence improve the efficiency of application system. To solve this problem, an Independent Component Analysis with k-Nearest Neighbor Classifier (ICA-kNN) approach was presented in this research to reduce the data redundancy while not sacrifice the classification performance too much. An overall 90.6% detection rate was achieved given 10 optimal wavelengths, which constituted only 13% of the total acquired hyperspectral image data. In order to further evaluate the proposed method, the classification results of the ICA-kNN approach were also compared to the kNN classifier method alone. The experimental results showed that the ICA-kNN method with fewer wavelengths had the same performance as the kNN classifier alone using information from all 79 wavelengths. This demonstrated the effectiveness of the proposed ICA-kNN method for the hyperspectral band selection in the walnut shell and meat classification