Cell Cycle-Regulated Genes Classification using Machine Learning and Deep Learning Techniques on Processed Microarrays Images

Nowadays, machine learning and deep learning algorithms, are considered as new technologies increasingly used in the biomedical field. Machine learning is a branch of Artificial Intelligence that aims to automatically find patterns in existing data. A new Machine Learning subfield, the deep learning theory, has emerged. It deals with object recognition in images. In this paper, our goal is DNA Microarrays’analysis with these algorithms to classify two genes’ types. The first class represents cell cycle regulated genes and the second is non cell cycle regulated ones. In the current state of the art, the researchers are processing the numerical data associated to gene evolution to achieve this classification. Here, we propose a new and different approach, based on the microarrays images’ treatment. To classify images, we use three machine learning algorithms which are: Support Vector Machine, KNearest Neighbors and Random Forest Classifier. We also use the Convolutional Neural Network and the fully connected neural network algorithms. Experiments demonstrate that our approaches outperform the state of art by a margin of 14.73 per cent by using machine learning algorithms and a margin of 22.39 per cent by using deep learning models. Our models accomplish real time test accuracy of ~ 92.39 % at classifying using CNNand 94.73% using machine learning algorithms.

Download Full-text

Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms

Remote Sensing ◽

10.3390/rs12111838 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1838 ◽

Cited By ~ 8

Author(s):

Zhao Zhang ◽

Paulo Flores ◽

C. Igathinathane ◽

Dayakar L. Naik ◽

Ravi Kiran ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Standard Deviation ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Superior Performance ◽

Support Vector ◽

Unmanned Aerial Systems

The current mainstream approach of using manual measurements and visual inspections for crop lodging detection is inefficient, time-consuming, and subjective. An innovative method for wheat lodging detection that can overcome or alleviate these shortcomings would be welcomed. This study proposed a systematic approach for wheat lodging detection in research plots (372 experimental plots), which consisted of using unmanned aerial systems (UAS) for aerial imagery acquisition, manual field evaluation, and machine learning algorithms to detect the occurrence or not of lodging. UAS imagery was collected on three different dates (23 and 30 July 2019, and 8 August 2019) after lodging occurred. Traditional machine learning and deep learning were evaluated and compared in this study in terms of classification accuracy and standard deviation. For traditional machine learning, five types of features (i.e. gray level co-occurrence matrix, local binary pattern, Gabor, intensity, and Hu-moment) were extracted and fed into three traditional machine learning algorithms (i.e., random forest (RF), neural network, and support vector machine) for detecting lodged plots. For the datasets on each imagery collection date, the accuracies of the three algorithms were not significantly different from each other. For any of the three algorithms, accuracies on the first and last date datasets had the lowest and highest values, respectively. Incorporating standard deviation as a measurement of performance robustness, RF was determined as the most satisfactory. Regarding deep learning, three different convolutional neural networks (simple convolutional neural network, VGG-16, and GoogLeNet) were tested. For any of the single date datasets, GoogLeNet consistently had superior performance over the other two methods. Further comparisons between RF and GoogLeNet demonstrated that the detection accuracies of the two methods were not significantly different from each other (p > 0.05); hence, the choice of any of the two would not affect the final detection accuracies. However, considering the fact that the average accuracy of GoogLeNet (93%) was larger than RF (91%), it was recommended to use GoogLeNet for wheat lodging detection. This research demonstrated that UAS RGB imagery, coupled with the GoogLeNet machine learning algorithm, can be a novel, reliable, objective, simple, low-cost, and effective (accuracy > 90%) tool for wheat lodging detection.

Download Full-text

Mood Detection from Physical and Neurophysical Data Using Deep Learning Models

Complexity ◽

10.1155/2019/6434578 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15

Author(s):

Zeynep Hilal Kilimci ◽

Aykut Güven ◽

Mitat Uysal ◽

Selim Akyokus

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Behavioral Data ◽

Support Vector ◽

Learning Models ◽

Physical Data ◽

Conventional Machine

Nowadays, smart devices as a part of daily life collect data about their users with the help of sensors placed on them. Sensor data are usually physical data but mobile applications collect more than physical data like device usage habits and personal interests. Collected data are usually classified as personal, but they contain valuable information about their users when it is analyzed and interpreted. One of the main purposes of personal data analysis is to make predictions about users. Collected data can be divided into two major categories: physical and behavioral data. Behavioral data are also named as neurophysical data. Physical and neurophysical parameters are collected as a part of this study. Physical data contains measurements of the users like heartbeats, sleep quality, energy, movement/mobility parameters. Neurophysical data contain keystroke patterns like typing speed and typing errors. Users’ emotional/mood statuses are also investigated by asking daily questions. Six questions are asked to the users daily in order to determine the mood of them. These questions are emotion-attached questions, and depending on the answers, users’ emotional states are graded. Our aim is to show that there is a connection between users’ physical/neurophysical parameters and mood/emotional conditions. To prove our hypothesis, we collect and measure physical and neurophysical parameters of 15 users for 1 year. The novelty of this work to the literature is the usage of both combinations of physical and neurophysical parameters. Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. Multinomial Naïve Bayes (MNB), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Decision Integration Strategy (DIS) are evaluated as conventional machine learning algorithms. To the best of our knowledge, this is the very first attempt to analyze the neurophysical conditions of the users by evaluating deep learning models for mood analysis and enriching physical characteristics with neurophysical parameters. Experiment results demonstrate that the utilization of deep learning methodologies and the combination of both physical and neurophysical parameters enhances the classification success of the system to interpret the mood of the users. A wide range of comparative and extensive experiments shows that the proposed model exhibits noteworthy results compared to the state-of-art studies.

Download Full-text

Texture Recognition Based on Perception Data from a Bionic Tactile Sensor

Sensors ◽

10.3390/s21155224 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5224

Author(s):

Shiyao Huang ◽

Hao Wu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Learning Algorithms ◽

Tactile Sensor ◽

Machine Learning Algorithms ◽

Support Vector ◽

Test Accuracy ◽

Texture Recognition ◽

Vibration Data

Texture recognition is important for robots to discern the characteristics of the object surface and adjust grasping and manipulation strategies accordingly. It is still challenging to develop texture classification approaches that are accurate and do not require high computational costs. In this work, we adopt a bionic tactile sensor to collect vibration data while sliding against materials of interest. Under a fixed contact pressure and speed, a total of 1000 sets of vibration data from ten different materials were collected. With the tactile perception data, four types of texture recognition algorithms are proposed. Three machine learning algorithms, including support vector machine, random forest, and K-nearest neighbor, are established for texture recognition. The test accuracy of those three methods are 95%, 94%, 94%, respectively. In the detection process of machine learning algorithms, the asamoto and polyester are easy to be confused with each other. A convolutional neural network is established to further increase the test accuracy to 98.5%. The three machine learning models and convolutional neural network demonstrate high accuracy and excellent robustness.

Download Full-text

Adaptive Anomaly Detection Framework Model Objects in Cyberspace

Applied Bionics and Biomechanics ◽

10.1155/2020/6660489 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Hasan Alkahtani ◽

Theyazn H. H. Aldhyani ◽

Mohammed Al-Yaari

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Network Security ◽

Anomaly Detection ◽

Denial Of Service ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Ddos Attacks ◽

Framework Model

Telecommunication has registered strong and rapid growth in the past decade. Accordingly, the monitoring of computers and networks is too complicated for network administrators. Hence, network security represents one of the biggest serious challenges that can be faced by network security communities. Taking into consideration the fact that e-banking, e-commerce, and business data will be shared on the computer network, these data may face a threat from intrusion. The purpose of this research is to propose a methodology that will lead to a high level and sustainable protection against cyberattacks. In particular, an adaptive anomaly detection framework model was developed using deep and machine learning algorithms to manage automatically-configured application-level firewalls. The standard network datasets were used to evaluate the proposed model which is designed for improving the cybersecurity system. The deep learning based on Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) and machine learning algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithms were implemented to classify the Denial-of-Service attack (DoS) and Distributed Denial-of-Service (DDoS) attacks. The information gain method was applied to select the relevant features from the network dataset. These network features were significant to improve the classification algorithm. The system was used to classify DoS and DDoS attacks in four stand datasets namely KDD cup 199, NSL-KDD, ISCX, and ICI-ID2017. The empirical results indicate that the deep learning based on the LSTM-RNN algorithm has obtained the highest accuracy. The proposed system based on the LSTM-RNN algorithm produced the highest testing accuracy rate of 99.51% and 99.91% with respect to KDD Cup’99, NSL-KDD, ISCX, and ICI-Id2017 datasets, respectively. A comparative result analysis between the machine learning algorithms, namely SVM and KNN, and the deep learning algorithms based on the LSTM-RNN model is presented. Finally, it is concluded that the LSTM-RNN model is efficient and effective to improve the cybersecurity system for detecting anomaly-based cybersecurity.

Download Full-text

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Applied Sciences ◽

10.3390/app8081280 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1280 ◽

Cited By ~ 14

Author(s):

Yong Kim ◽

Youngdoo Son ◽

Wonjoon Kim ◽

Byungki Jin ◽

Myung Yun

Keyword(s):

Neural Network ◽

Machine Learning ◽

Monitoring System ◽

Multinomial Logistic Regression ◽

Learning Algorithms ◽

Feedback System ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Future Research ◽

Support Vector

Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children’s chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

Download Full-text

Deep Learning and Conventional Machine Learning for Image-Based in-Situ Fault Detection During Laser Welding: A Comparative Study

10.20944/preprints202105.0272.v1 ◽

2021 ◽

Author(s):

Christian Knaak ◽

Moritz Kröger ◽

Frederic Schulze ◽

Peter Abels ◽

Arnold Gillner

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Near Infrared ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

Detection Rates ◽

Feature Extraction And Selection ◽

Welding Defects

An effective process monitoring strategy is a requirement for meeting the challenges posed by increasingly complex products and manufacturing processes. To address these needs, this study investigates a comprehensive scheme based on classical machine learning methods, deep learning algorithms, and feature extraction and selection techniques. In a first step, a novel deep learning architecture based on convolutional neural networks (CNN) and gated recurrent units (GRU) is introduced to predict the local weld quality based on mid-wave infrared (MWIR) and near-infrared (NIR) image data. The developed technology is used to discover critical welding defects including lack of fusion (false friends), sagging and lack of penetration, and geometric deviations of the weld seam. Additional work is conducted to investigate the significance of various geometrical, statistical, and spatio-temporal features extracted from the keyhole and weld pool regions. Furthermore, the performance of the proposed deep learning architecture is compared to that of classical supervised machine learning algorithms, such as multi-layer perceptron (MLP), logistic regression (LogReg), support vector machines (SVM), decision trees (DT), random forest (RF) and k-Nearest Neighbors (kNN). Optimal hyperparameters for each algorithm are determined by an extensive grid search. Ultimately, the three best classification models are combined into an ensemble classifier that yields the highest detection rates and achieves the most robust estimation of welding defects among all classifiers studied, which is validated on previously unknown welding trials.

Download Full-text

INDIVIDUAL BANANA TREE CROWN DELINEATION USING UNMANNED AERIAL VEHICLE (UAV) IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-581-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 581-585

Author(s):

S. Kuikel ◽

B. Upadhyay ◽

D. Aryal ◽

S. Bista ◽

B. Awasthi ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Tree Crown ◽

Individual Tree ◽

Banana Tree ◽

Crown Delineation ◽

Uav Images

Abstract. Individual Tree Crown (ITC) delineation from aerial imageries plays an important role in forestry management and precision farming. Several conventional as well as machine learning and deep learning algorithms have been recently used in ITC detection purpose. In this paper, we present Convolutional Neural Network (CNN) and Support Vector Machine (SVM) as the deep learning and machine learning algorithms along with conventional methods of classification such as Object Based Image Analysis (OBIA) and Nearest Neighborhood (NN) classification for banana tree delineation. The comparison was done based by considering two cases; Firstly, every single classifier was compared by feeding the image with height information to see the effect of height in banana tree delineation. Secondly, individual classifiers were compared quantitatively and qualitatively based on five metrices i.e., Overall Accuracy, Recall, Precision, F-Score, and Intersection Over Union (IoU) and best classifier was determined. The result shows that there are no significant differences in the metrices when height information was fed as there were banana tree of almost similar height in the farm. The result as discussed in quantitative and qualitative analysis showed that the CNN algorithm out performed SVM, OBIA and NN techniques for crown delineation in term of performance measures.

Download Full-text

BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning

Frontiers in Oncology ◽

10.3389/fonc.2021.728224 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiejie Zhou ◽

Yan-Lin Liu ◽

Yang Zhang ◽

Jeon-Hor Chen ◽

Freddie J. Combs ◽

...

Keyword(s):

Machine Learning ◽

Differential Diagnosis ◽

Deep Learning ◽

Learning Algorithms ◽

Regional Distribution ◽

Machine Learning Algorithms ◽

Support Vector ◽

Mass Lesions ◽

Morphological Distribution ◽

Parametric Maps

BackgroundA wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.Materials and MethodsA total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing.ResultsThe diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A–5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets.ConclusionDiagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.

Download Full-text

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Sensors ◽

10.3390/s20061576 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1576 ◽

Cited By ~ 13

Author(s):

Li Zhu ◽

Lianghao Huang ◽

Linyu Fan ◽

Jinsong Huang ◽

Faming Huang ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Environmental Factors ◽

Recurrent Neural Network ◽

Landslide Susceptibility ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Predictive Rate

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

Download Full-text

Invisible experience to real-time assessment in elite tennis athlete training: Sport-specific movement classification based on wearable MEMS sensor data

Proceedings of the Institution of Mechanical Engineers Part P Journal of Sports Engineering and Technology ◽

10.1177/17543371211050312 ◽

2021 ◽

pp. 175433712110503

Author(s):

Mingyue Wu ◽

Ran Wang ◽

Yang Hu ◽

Mengjiao Fan ◽

Yufan Wang ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Test Accuracy ◽

Z Score ◽

Mems Sensor ◽

Score Normalization

This study examined the reliability of a tennis stroke classification and assessment platform consisting of a single low-cost MEMS sensor in a wrist-worn wearable device, smartphone, and computer. The data that was collected was transmitted via Bluetooth and analyzed by machine learning algorithms. Twelve right-handed male elite tennis athletes participated in the study, and each athlete performed 150 strokes. The results from three machine learning algorithms regarding their recognition and classification of the real-time data stream were compared. Stroke recognition and classification went through pre-processing, segmentation, feature extraction, and classification with Support Vector Machine (SVM), including SVM without normalization, SVM with Min–Max, SVM with Z-score normalization, K-nearest neighbor (K-NN), and Naive Bayes (NB) machine learning algorithms. During the data training process, 10-fold cross-validation was used to avoid overfitting and suitable parameters were found within the SVM classifiers. The best classifier was achieved when C = 1 using the RBF kernel function. Different machine learning algorithms’ classification of unique stroke types yielded highly reliable clusters within each stroke type with the highest test accuracy of 99% achieved by SVM with Min–Max normalization and 98.4% achieved using SVM with a Z-score normalization classifier.

Download Full-text