Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models

Abstract Unless adequate measures are taken for waste litter, the ecological balance may deteriorate over time. The wastes disposed of the trash can be divided into two classes that are organic and recycling types. In recent years, artificial intelligence is frequently mentioned in all areas of technology. In this study, the dataset used for the classification of waste is reconstructed with the AutoEncoder network. The feature sets are then extracted using two datasets by Convolutional Neural Network (CNN) architectures and these feature sets are combined. The Ridge Regression (RR) method performed on the combined feature set reduced the number of features and also revealed the efficient features. Support Vector Machines (SVMs) were used as classifiers in all experiments. The most successful classification accuracy in the experiments was 99.95%. In this study, it is seen that the proposed approach is successful in the classification of waste types.

[1]  Burhan Ergen,et al.  Biyomedikal Görüntülerde Derin Öğrenme ile Mevcut Yöntemlerin Kıyaslanması , 2019 .

[2]  Mesut Toğaçar,et al.  BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. , 2019, Medical hypotheses.

[3]  Sebastian Bock,et al.  A Proof of Local Convergence for the Adam Optimizer , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[4]  Zafer Cömert,et al.  Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment , 2018, Comput. Biol. Medicine.

[5]  Richard C. Thompson,et al.  Plastics, the environment and human health: current consensus and future trends , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[6]  Lovekesh Vig,et al.  A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images , 2019, Front. Neuroinform..

[7]  Xiaogang Xiong,et al.  Multilayer Hybrid Deep-Learning Method for Waste Classification and Recycling , 2018, Comput. Intell. Neurosci..

[8]  Ethem Alpaydin,et al.  Unsupervised feature extraction with autoencoder trees , 2017, Neurocomputing.

[9]  Yang Wang,et al.  Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. , 2018, Cancer genomics & proteomics.

[10]  Richard K. G. Do,et al.  Convolutional neural networks: an overview and application in radiology , 2018, Insights into Imaging.

[11]  Mesut TOĞAÇAR,et al.  DEEP LEARNING APPROACH FOR CLASSIFICATION OF BREAST CANCER , 2018, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP).

[12]  Ming Xu,et al.  Laser stripe image denoising using convolutional autoencoder , 2018 .

[13]  Serdar Solak,et al.  Görüntü işleme teknikleri ve kümeleme yöntemleri kullanılarak fındık meyvesinin tespit ve sınıflandırılması , 2018 .

[14]  Luis Enrique González Jiménez,et al.  Intelligent Waste Separator , 2015, Computación y Sistemas.

[15]  G. Jin,et al.  Plastic solid waste identification system based on near infrared spectroscopy in combination with support vector machine , 2019, Advanced Industrial and Engineering Polymer Research.

[16]  Kemal Özkan,et al.  A new classification scheme of plastic wastes based upon recycling labels. , 2015, Waste management.

[17]  Arul Arulrajah,et al.  Practical recycling applications of crushed waste glass in construction materials: A review , 2017 .

[18]  Umit Budak,et al.  Efficient approach for digitization of the cardiotocography signals , 2020 .

[19]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Baoqi Li,et al.  An Improved ResNet Based on the Adjustable Shortcut Connections , 2018, IEEE Access.

[21]  Burhan Ergen,et al.  A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models , 2020, IRBM.

[22]  Takashi Morie,et al.  A shared synapse architecture for efficient FPGA implementation of autoencoders , 2018, PloS one.

[23]  Mesut Toğaçar,et al.  Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network , 2019, 2019 4th International Conference on Computer Science and Engineering (UBMK).

[24]  Zafer Cömert,et al.  Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach , 2019, Comput. Electron. Agric..

[25]  Yijun Wang,et al.  An Optimization Strategy Based on Hybrid Algorithm of Adam and SGD , 2018 .

[26]  N.J.G.J. Bandara,et al.  Environmental impacts with waste disposal practices in a suburban municipality in Sri Lanka , 2010 .

[27]  Ying Wang,et al.  Autonomous garbage detection for intelligent urban management , 2018 .

[28]  Mesut Toğaçar,et al.  Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders. , 2019, Medical hypotheses.

[29]  Burhan Ergen,et al.  Diagnosis of Eye Retinal Diseases Based on Convolutional Neural Networks Using Optical Coherence Images , 2019, 2019 23rd International Conference Electronics.

[30]  Sung Wook Baik,et al.  Action recognition using optimized deep autoencoder and CNN for surveillance data streams of non-stationary environments , 2019, Future Gener. Comput. Syst..

[31]  Zafer Cömert,et al.  Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks , 2020 .

[32]  P. J. García Nieto,et al.  Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers , 2016, Materials.

[33]  Cömert Zafer,et al.  Fusing fine-tuned deep features for recognizing different tympanic membranes , 2020 .

[34]  Eduardo A. Soares,et al.  Artificial Intelligence in Automated Sorting in Trash Recycling , 2018, Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018).

[35]  Davut Hanbay,et al.  Plant disease and pest detection using deep learning-based features , 2019, Turkish J. Electr. Eng. Comput. Sci..

[36]  Jih-Jeng Huang,et al.  A Hybrid Autoencoder Network for Unsupervised Image Clustering , 2019, Algorithms.

[37]  Costas Velis,et al.  Challenges and opportunities associated with waste management in India , 2017, Royal Society Open Science.

[38]  Zafer Cömert,et al.  Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach , 2018, CSOS.

[39]  V. Bansal,et al.  Statistical analysis strategies for association studies involving rare variants , 2010, Nature Reviews Genetics.

[40]  Cenk Bircanoglu,et al.  RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks , 2018, 2018 Innovations in Intelligent Systems and Applications (INISTA).

[41]  Tayfun Gokmen,et al.  Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices , 2017, Front. Neurosci..

[42]  R. Geyer,et al.  Production, use, and fate of all plastics ever made , 2017, Science Advances.

[43]  Chris Yakopcic,et al.  A State-of-the-Art Survey on Deep Learning Theory and Architectures , 2019, Electronics.

[44]  Yizhen Zhang,et al.  Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex , 2019, NeuroImage.

[45]  K. AgbaezeE.,et al.  Impact of Sustainable Solid Waste Management on Economic Development – Lessons from Enugu State Nigeria , 2014 .

[46]  Hussein I. Abdel-Shafy,et al.  Solid waste issue: Sources, composition, disposal, recycling, and valorization , 2018, Egyptian Journal of Petroleum.

[47]  Feng Zheng,et al.  Quasi‐linear SVM classifier with segmented local offsets for imbalanced data classification , 2018, IEEJ Transactions on Electrical and Electronic Engineering.

[48]  Weining Zhang,et al.  Robust Class-Specific Autoencoder for Data Cleaning and Classification in the Presence of Label Noise , 2018, Neural Processing Letters.

[49]  Mandar Satvilkar.  Image Based Trash Classification using Machine Learning Algorithms for Recyclability Status , 2018 .

[50]  Xin Liu,et al.  An Adaptive Moment estimation method for online AUC maximization , 2019, PloS one.

[51]  Zafer Cömert,et al.  BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer , 2020 .

[52]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[53]  Abdulkadir Sengur,et al.  Efficient deep features selections and classification for flower species recognition , 2019, Measurement.

[54]  Koichi Shinoda,et al.  Multi-Task Autoencoder for Noise-Robust Speech Recognition , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[55]  Hussam Jouhara,et al.  Municipal waste management systems for domestic use , 2017 .

[56]  Nurul Sima Mohamad Shariff,et al.  Ridge Regression for Solving the Multicollinearity Problem: Review of Methods and Models , 2015 .

[57]  Francisco Charte,et al.  A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines , 2018, Inf. Fusion.

[58]  Burhan Ergen,et al.  Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models , 2019, Elektronika ir Elektrotechnika.

[59]  Bin Fang,et al.  CNN-Based Broad Learning System , 2019, 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP).

[60]  Lamiaa A. Elrefaei,et al.  Convolutional Neural Network Based Feature Extraction for IRIS Recognition , 2018 .