COVID-19 emerged in 2019 in china, the worldwide spread rapidly, and caused many injuries and deaths among humans. Accurate and early detection of COVID-19 can ensure the long-term survival of patients and help prohibit the spread of the epidemic. COVID-19 case classification techniques help health organizations quickly identify and treat severe cases. Algorithms of classification are one the essential matters for forecasting and making decisions to assist the diagnosis, early identification of COVID-19, and specify cases that require to intensive care unit to deliver the treatment at appropriate timing. This paper is intended to compare algorithms of classification of machine learning to diagnose COVID-19 cases and measure their performance with many metrics, and measure mislabeling (false-positive and false-negative) to specify the best algorithms for speed and accuracy diagnosis. In this paper, we focus onto classify the cases of COVID-19 using the algorithms of machine learning. we load the dataset and perform dataset preparation, pre-processing, analysis of data, selection of features, split of data, and use of classification algorithm. In the first using four classification algorithms, (Stochastic Gradient Descent, Logistic Regression, Random Forest, Naive Bayes), the outcome of algorithms accuracy respectively was 99.61%, 94.82% ,98.37%,96.57%, and the result of execution time for algorithms respectively were 0.01s, 0.7s, 0.20s, 0.04. The Stochastic Gradient Descent of mislabeling was better. Second, using four classification algorithms, (eXtreme-Gradient Boosting, Decision Tree, Support Vector Machines, K_Nearest Neighbors), the outcome of algorithms accuracy was 98.37%, 99%, 97%, 88.4%, and the result of execution time for algorithms respectively were 0.18s, 0.02s, 0.3s, 0.01s. The Decision Tree of mislabeling was better. Using machine learning helps improve allocate medical resources to maximize their utilization. Classification algorithm of clinical data for confirmed COVID-19 cases can help predict a patient's need to advance to the ICU or not need by using a global dataset of COVID-19 cases due to its accuracy and quality.
Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication; the BMI uses the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental tasks from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Two feature extraction algorithms using overlapped and non overlapped signal segments are analyzed. Principal component analysis is used for extracting features from the EEG signal segments. Classification performance of overlapping EEG signal segments is observed to be better in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications.
Data-intensive science is a critical science paradigm that interferes with all other sciences. Data mining (DM) is a powerful and useful technology with wide potential users focusing on important meaningful patterns and discovers a new knowledge from a collected dataset. Any predictive task in DM uses some attribute to classify an unknown class. Classification algorithms are a class of prominent mathematical techniques in DM. Constructing a model is the core aspect of such algorithms. However, their performance highly depends on the algorithm behavior upon manipulating data. Focusing on binarazaition as an approach for preprocessing, this paper analysis and evaluates different classification algorithms when construct a model based on accuracy in the classification task. The Mixed National Institute of Standards and Technology (MNIST) handwritten digits dataset provided by Yann LeCun has been used in evaluation. The paper focuses on machine learning approaches for handwritten digits detection. Machine learning establishes classification methods, such as K-Nearest Neighbor(KNN), Decision Tree (DT), and Neural Networks (NN). Results showed that the knowledge-based method, i.e. NN algorithm, is more accurate in determining the digits as it reduces the error rate. The implication of this evaluation is providing essential insights for computer scientists and practitioners for choosing the suitable DM technique that fit with their data.
Low-quality data can be dangerous for the machine learning models, especially in crucial situations. Some large-scale datasets have low-quality data and false labels, also, datasets with images type probably have artifacts and biases from measurement errors. So, automatic algorithms that are able to recognize low-quality data are needed. In this paper, Shapley Value is used, a metric for evaluation of data, to quantify the value of training data to the performance of a classification algorithm in a large ImageNet dataset. We specify the success of data Shapley in recognizing low-quality against precious data for classification. We figure out that model performance is increased when low Shapley values are removed, whilst classification model performance is declined when high Shapley values are removed. Moreover, there were more true labels in high-Shapley value data and more mislabeled samples in low-Shapley value. Results represent that mislabeled or poor-quality images are in low Shapley value and valuable data for classification are in high Shapley value.
Current automatic writing feedback systems cannot distinguish between different discourse elements in students' writing. This is a problem because, without this ability, the guidance provided by these systems is too general for what students want to achieve on arrival. This is cause for concern because automated writing feedback systems are a great tool for combating student writing declines. According to the National Assessment of Educational Progress, less than 30 percent of high school graduates are gifted writers. If we can improve the automatic writing feedback system, we can improve the quality of student writing and stop the decline of skilled writers among students. Solutions to this problem have been proposed, the most popular being the fine-tuning of bidirectional encoder representations from Transformers models that recognize various utterance elements in student written assignments. However, these methods have their drawbacks. For example, these methods do not compare the strengths and weaknesses of different models, and these solutions encourage training models over sequences (sentences) rather than entire articles. In this article, I'm redesigning the Persuasive Essays for Rating, Selecting, and Understanding Argumentative and Discourse Elements corpus so that models can be trained for the entire article, and I've included Transformers, the Long Document Transformer's bidirectional encoder representation, and the Generative Improving a pre trained Transformer 2 model for utterance classification in the context of a named entity recognition token classification problem. Overall, the bi-directional encoder representation of the Transformers model railway using my sequence-merging preprocessing method outperforms the standard model by 17% and 41% in overall accuracy. I also found that the Long Document Transformer model performed the best in utterance classification with an overall f-1 score of 54%. However, the increase in validation loss from 0.54 to 0.79 indicates that the model is overfitting. Some improvements can still be made due to model overfittings, such as B. Implementation of early stopping techniques and further examples of rare utterance elements during training.
The rapid progress in mobile computing necessitates energy efficient solutions to support substantially diverse and complex workloads. Heterogeneous many core platforms are progressively being adopted in contemporary embedded implementations for high performance at low power cost estimations. These implementations experience diverse workloads that offer drastic opportunities to improve energy efficiency. In this paper, we propose a novel per core power gating (PCPG) approach based on workload classifications (WLC) for drastic energy cost minimization in the dark silicon era. Core of our paradigm is to use an integrated sleep mode management based on workloads classification indicated by the performance counters. A number of real applications benchmark (PARSEC) are adopted as a practical example of diverse workloads, including memory- and CPU-intensive ones. In this paper, these applications are exercised on Samsung Exynos 5422 heterogeneous many core system showing up to 37% to 110% energy efficient when compared with our most recent published work, and ondemand governor, respectively. Furthermore, we illustrate low-complexity and low-cost runtime per core power gating algorithm that consistently maximize IPS/Watt at all state space.
In this paper, a new method is proposed for people tracking using the human skeleton provided by the Kinect sensor, Our method is based on skeleton data, which includes the coordinate value of each joint in the human body. For data classification, the Support Vector Machine (SVM) and Random Forest techniques are used. To achieve this goal, 14 classes of movements are defined, using the Kinect Sensor to extract data containing 46 features and then using them to train the classification models. The system was tested on 12 subjects, each of whom performed 14 movements in each experiment. Experiment results show that the best average accuracy is 90.2 % for the SVM model and 99 % for the Random forest model. From the experiments, we concluded that the best distance between the Kinect sensor and the human body is one meter.
Due to their vital applications in many real-world situations, researchers are still presenting bunches of methods for better analysis of motor imagery (MI) electroencephalograph (EEG) signals. However, in general, EEG signals are complex because of their nonstationary and high-dimensionality properties. Therefore, high consideration needs to be taken in both feature extraction and classification. In this paper, several hybrid classification models are built and their performance is compared. Three famous wavelet mother functions are used for generating scalograms from the raw signals. The scalograms are used for transfer learning of the well-known VGG-16 deep network. Then, one of six classifiers is used to determine the class of the input signal. The performance of different combinations of mother functions and classifiers are compared on two MI EEG datasets. Several evaluation metrics show that a model of VGG-16 feature extractor with a neural network classifier using the Amor mother wavelet function has outperformed the results of state-of-the-art studies.
Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people's health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people's lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.
Facial retouching, also referred to as digital retouching, is the process of modifying or enhancing facial characteristics in digital images or photographs. While it can be a valuable technique for fixing flaws or achieving a desired visual appeal, it also gives rise to ethical considerations. This study involves categorizing genuine and retouched facial images from the standard ND-IIITD retouched faces dataset using a transfer learning methodology. The impact of different primary optimization algorithms—specifically Adam, RMSprop, and Adadelta—utilized in conjunction with a fine-tuned ResNet50 model is examined to assess potential enhancements in classification effectiveness. Our proposed transfer learning ResNet50 model demonstrates superior performance compared to other existing approaches, particularly when the RMSprop and Adam optimizers are employed in the fine-tuning process. By training the transfer learning ResNet50 model on the ND-IIITD retouched faces dataset with the ”ImageNet” weight, we achieve a validation accuracy of 98.76%, a training accuracy of 98.32%, and an overall accuracy of 98.52% for classifying real and retouched faces in just 20 epochs. Comparative analysis indicates that the choice of optimizer during the fine-tuning of the transfer learning ResNet50 model can further enhance the classification accuracy.
In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The objective of this work is to achieve better classification with multiple parameters using K-Nearest Neighbor (K-NN) for different movements of a prosthetic arm. The proposed structure is simulated using MATLAB Ver. R2009a, and satisfied results are obtained by comparing with the conventional recognition method using Artificial Neural Network (ANN). Results show the proposed K-NN technique achieved a uniformly good performance with respect to ANN in terms of time, which is important in recognition systems, and better accuracy in recognition when applied to lower Signal-to-Noise Ratio (SNR) signals.
In recent years, the number of researches in the field of artificial limbs has increased significantly in order to improve the performance of the use of these limbs by amputees. During this period, High-Density surface Electromyography (HD-sEMG) signals have been employed for hand gesture identification, in which the performance of the classification process can be improved by using robust spatial features extracted from HD-sEMG signals. In this paper, several algorithms of spatial feature extraction have been proposed to increase the accuracy of the SVM classifier, while the histogram oriented gradient (HOG) has been used to achieve this mission. So, several feature sets have been extracted from HD-sEMG signals such as; features extracted based on HOG denoted by (H); features have been generated by combine intensity feature with H features denoted as (HI); features have been generated by combine average intensity with H features denoted as (AIH). The proposed system has been simulated by MATLAB to calculate the accuracy of the classification process, in addition, the proposed system is practically validated in order to show the ability to use this system by amputees. The results show the high accuracy of the classifier in real-time which leads to an increase in the possibility of using this system as an artificial hand.
Training the user in Brain-Computer Interface (BCI) systems based on brain signals that recorded using Electroencephalography Motor Imagery (EEG-MI) signal is a time-consuming process and causes tiredness to the trained subject, so transfer learning (subject to subject or session to session) is very useful methods of training that will decrease the number of recorded training trials for the target subject. To record the brain signals, channels or electrodes are used. Increasing channels could increase the classification accuracy but this solution costs a lot of money and there are no guarantees of high classification accuracy. This paper introduces a transfer learning method using only two channels and a few training trials for both feature extraction and classifier training. Our results show that the proposed method Independent Component Analysis with Regularized Common Spatial Pattern (ICA-RCSP) will produce about 70% accuracy for the session to session transfer learning using few training trails. When the proposed method used for transfer subject to subject the accuracy was lower than that for session to session but it still better than other methods.
The brain tumors are among the common deadly illness that requires early, reliable detection techniques, current identification, and imaging methods that depend on the decisions of neuro-specialists and radiologists who can make possible human error. This takes time to manually identify a brain tumor. This work aims to design an intelligent model capable of diagnosing and predicting the severity of magnetic resonance imaging (MRI) brain tumors to make an accurate decision. The main contribution is achieved by adopting a new multiclass classifier approach based on a collected real database with new proposed features that reflect the precise situation of the disease. In this work, two artificial neural networks (ANNs) methods namely, Feed Forward Back Propagation neural network (FFBPNN) and support vector machine (SVM), used to expectations the level of brain tumors. The results show that the prediction result by the (FFBPN) network will be better than the other method in time record to reach an automatic classification with classification accuracy was 97% for 3-class which is considered excellent accuracy. The software simulation and results of this work have been implemented via MATLAB (R2012b).
Detecting pulmonary cancers at early stages is difficult but crucial for patient survival. Therefore, it is essential to develop an intelligent, autonomous, and accurate lung cancer detection system that shows great reliability compared to previous systems and research. In this study, we have developed an innovative lung cancer detection system known as the Hybrid Lung Cancer Stage Classifier and Diagnosis Model (Hybrid-LCSCDM). This system simplifies the complex task of diagnosing lung cancer by categorizing patients into three classes: normal, benign, and malignant, by analyzing computed tomography (CT) scans using a two-part approach: First, feature extraction is conducted using a pre-trained model called VGG-16 for detecting key features in lung CT scans indicative of cancer. Second, these features are then classified using a machine learning technique called XGBoost, which sorts the scans into three categories. A dataset, IQ-OTH/NCCD - Lung Cancer, is used to train and evaluate the proposed model to show its effectiveness. The dataset consists of the three aforementioned classes containing 1190 images. Our suggested strategy achieved an overall accuracy of 98.54%, while the classification precision among the three classes was 98.63%. Considering the accuracy, recall, and precision as well as the F1-score evaluation metrics, the results indicated that when using solely computed tomography scans, the proposed (Hybrid-LCSCDM) model outperforms all previously published models.
Agriculture is the primary food source for humans and livestock in the world and the primary source for the economy of many countries. The majority of the country's population and the world depend on agriculture. Still, at present, farmers are facing difficulty in dealing with the requirements of agriculture. Due to many reasons, including different and extreme weather conditions, the abundance of water quality, etc. This paper applied the Internet of Things and deep learning system to establish a smart farming system to monitor the environmental conditions that affect tomato plants using a mobile phone. Through deep learning networks, trained the dataset taken from PlantVillage and collected from google images to classify tomato diseases, and obtained a test accuracy of 97%, which led to the publication of the model to the mobile application for classification for its high accuracy. Using the IoT, a monitoring system and automatic irrigation were built that were controlled through the mobile remote to monitor the environmental conditions surrounding the plant, such as air temperature and humidity, soil moisture, water quality, and carbon dioxide gas percentage. The designed system has proven its efficiency when tested in terms of disease classification, remote irrigation, and monitoring of the environmental conditions surrounding the plant. And giving alerts when the values of the sensors exceed the minimum or higher values causing damage to the plant. The farmer can take the appropriate action at the right time to prevent any damage to the plant and thus obtain a high-quality product.
Early in the 20th century, as a result of technological advancements, the importance of digital marketing significantly increased as the necessity for digital customer experience, promotion, and distribution emerged. Since the year 1988, in the case when the term ”Digital Marketing” first appeared, the business sector has undergone drastic growth, moving from small startups to massive corporations on a global scale. The marketer must navigate a chaotic environment caused by the vast volume of generated data. Decision-makers must contend with the fact that user data is dynamic and changes every day. Smart applications must be used within enterprises to better evaluate, classify, enhance, and target audiences. Customers who are tech-savvy are pushing businesses to make bigger financial investments and use cutting-edge technologies. It was only natural that marketing and trade could be one of the areas to move to such development, which helps to move to the speed of spread, advertisements, along with other things to facilitate things for reaching and winning customers. In this study, we utilized machine learning (ML) algorithms (Decision tree (DT), K-Nearest Neighbor (KNN), CatBoost, and Random Forest (RF) (for classifying data in customers to move to development. Improve the ability to forecast customer behavior so one can gain more business from them more quickly and easily. With the use of the aforementioned dataset, the suggested system was put to the test. The results show that the system can accurately predict if a customer will buy something or not; the random forest (RF) had an accuracy of 0.97, DT had an accuracy of 0. 95, KNN had an accuracy of 0. 91, while the CatBoost algorithm had the execution time 15.04 of seconds, and gave the best result of highest f1 score and accuracy (0.91, 0. 98) respectively. Finally, the study’s future goals involve being created a web page, thereby helping many banking institutions with speed and forecast accuracy. Using more techniques of feature selection in conjunction with the marketing dataset to improve diagnosis.
WiFi-based human activity and gesture recognition explore the interaction between the human hand or body movements and the reflected WiFi signals to identify various activities. This type of recognition has received much attention in recent years since it does not require wearing special sensors or installing cameras. This paper aims to investigate human activity and gesture recognition schemes that use Channel State Information (CSI) provided by WiFi devices. To achieve high accuracy in the measurement, deep learning models such as AlexNet, VGG 19, and SqueezeNet were used for classification and extracting features automatically. Firstly, outliers are removed from the amplitude of each CSI stream during the preprocessing stage by using the Hampel identifier algorithm. Next, the RGB images are created for each activity to feed as input to Deep Convolutional Neural Networks. After that, data augmentation is implemented to reduce the overfitting problems in deep learning models. Finally, the proposed method is evaluated on a publicly available dataset called WiAR, which contains 10 volunteers, each of whom executes 16 activities. The experiment results demonstrate that AlexNet, VGG19, and SqueezeNet all have high recognition accuracy of 99.17 %, 96.25%, and 100 %, respectively.
Real-time detection and recognition systems for vehicle license plates present a significant design and implementation challenge, arising from factors such as low image resolution, data noise, and various weather and lighting conditions.This study presents an efficient automated system for the identification and classification of vehicle license plates, utilizing deep learning techniques. The system is specifically designed for Iraqi vehicle license plates, adapting to various backgrounds, different font sizes, and non-standard formats. The proposed system has been designed to be integrated into an automated entrance gate security system. The system’s framework encompasses two primary phases: license plate detection (LPD) and character recognition (CR). The utilization of the advanced deep learning technique YOLOv4 has been implemented for both phases owing to its adeptness in real-time data processing and its remarkable precision in identifying diminutive entities like characters on license plates. In the LPD phase, the focal point is on the identification and isolation of license plates from images, whereas the CR phase is dedicated to the identification and extraction of characters from the identified license plates. A substantial dataset comprising Iraqi vehicle images captured under various lighting and weather circumstances has been amassed for the intention of both training and testing. The system attained a noteworthy accuracy level of 95.07%, coupled with an average processing time of 118.63 milliseconds for complete end-to-end operations on a specified dataset, thus highlighting its suitability for real-time applications. The results suggest that the proposed system has the capability to significantly enhance the efficiency and reliability of vehicle license plate recognition in various environmental conditions, thus making it suitable for implementation in security and traffic management contexts.
The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image’s features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola’s approach, T.R Singh’s approach, Niblack’s approaches, Bernsen’s approach Bruckstein’s and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method.
Advancements in internet accessibility and the affordability of digital picture sensors have led to the proliferation of extensive image databases utilized across a multitude of applications. Addressing the semantic gap between low- level attributes and human visual perception has become pivotal in refining Content Based Image Retrieval (CBIR) methodologies, especially within this context. As this field is intensely researched, numerous efficient algorithms for CBIR systems have surfaced, precipitating significant progress in the artificial intelligence field. In this study, we propose employing a hard voting ensemble approach on features derived from three robust deep learning architectures: Inception, Exception, and Mobilenet. This is aimed at bridging the divide between low-level image features and human visual perception. The Euclidean method is adopted to determine the similarity metric between the query image and the features database. The outcome was a noticeable improvement in image retrieval accuracy. We applied our approach to a practical dataset named CBIR 50, which encompasses categories such as mobile phones, cars, cameras, and cats. The effectiveness of our method was thereby validated. Our approach outshone existing CBIR algorithms with superior accuracy (ACC), precision (PREC), recall (REC), and F1-score (F1-S), proving to be a noteworthy addition to the field of CBIR. Our proposed methodology could be potentially extended to various other sectors, including medical imaging and surveillance systems, where image retrieval accuracy is of paramount importance.
The problem of outlier detection is one of the most important issues in the field of analysis due to its applicability in several famous problem domains, including intrusion detection, security, banks, fraud detection, and discovery of criminal activities in electronic commerce. Anomaly detection comprises two main approaches: supervised and unsupervised approach. The supervised approach requires pre-defined information, which is defined as the type of outliers, and is difficult to be defined in some applications. Meanwhile, the second approach determines the outliers without human interaction. A review of the unsupervised approach, which shows the main advantages and the limitations considering the studies performed in the supervised approach, is introduced in this paper. This study indicated that the unsupervised approach suffers from determining local and global outlier objects simultaneously as the main problem related to algorithm parameterization. Moreover, most algorithms do not rank or identify the degree of being an outlier or normal objects and required different parameter settings by the research. Examples of such parameters are the radius of neighborhood, number of neighbors within the radius, and number of clusters. A comprehensive and structured overview of a large set of interesting outlier algorithms, which emphasized the outlier detection limitation in the unsupervised approach, can be used as a guideline for researchers who are interested in this field.
In recent years, there has been a considerable rise in the applications in which object or image categorization is beneficial for example, analyzing medicinal images, assisting persons to organize their collections of photos, recognizing what is around self-driving vehicles, and many more. These applications necessitate accurately labeled datasets, in their majority involve an extensive diversity in the types of images, from cats or dogs to roads, landscapes, and so forth. The fundamental aim of image categorization is to predict the category or class for the input image by specifying to which it belongs. For human beings, this is not a considerable thing, however, learning computers to perceive represents a hard issue that has become a broad area of research interest, and both computer vision techniques and deep learning algorithms have evolved. Conventional techniques utilize local descriptors for finding likeness between images, however, nowadays; progress in technology has provided the utilization of deep learning algorithms, especially the Convolutional Neural Networks (CNNs) to auto-extract representative image patterns and features for classification The fundamental aim of this paper is to inspect and explain how to utilize the algorithms and technologies of deep learning to accurately classify a dataset of images into their respective categories and keep model structure complication to a minimum. To achieve this aim, must focus precisely and accurately on categorizing the objects or images into their respective categories with excellent results. And, specify the best deep learning-based models in image processing and categorization. The developed CNN-based models have been proposed and a lot of pre-training models such as (VGG19, DenseNet201, ResNet152V2, MobileNetV2, and InceptionV3) have been presented, and all these models are trained on the Caltech-101 and Caltech-256 datasets. Extensive and comparative experiments were conducted on this dataset, and the obtained results demonstrate the effectiveness of the proposed models. The obtained results demonstrate the effectiveness of the proposed models. The accuracy for Caltech-101 and Caltech-256 datasets was (98.06% and 90%) respectively.
Due to the changing flow conditions during the pipeline's operation, several locations of erosion, damage, and failure occur. Leak prevention and early leak detection techniques are the best pipeline risk mitigation measures. To reduce detection time, pipeline models that can simulate these breaches are essential. In this study, numerical modeling using COMSOL Multiphysics is suggested for different fluid types, velocities, pressure distributions, and temperature distributions. The system consists of 12 meters of 8-inch pipe. A movable ball with a diameter of 5 inches is placed within. The findings show that dead zones happen more often in oil than in gas. Pipe insulation is facilitated by the gas phase's thermal inefficiency (thermal conductivity). The fluid mixing is improved by 2.5 m/s when the temperature is the lowest. More than water and gas, oil viscosity and dead zones lower maximum pressure. Pressure decreases with maximum velocity and vice versa. The acquired oil data set is utilized to calibrate the Support Vector Machine and Decision Tree techniques using MATLAB R2021a, ensuring the precision of the measurement. The classification result reveals that the Support Vector Machine (SVM) and Decision Tree (DT) models have the best average accuracy, which is 98.8%, and 99.87 %, respectively.
In this paper, an Industrial machine vision system incorporating Optical Character Recognition (OCR) is employed to inspect the marking on the Integrated Circuit (IC) Chips. This inspection is carried out while the ICs are coming out from the manufacturing line. A TSSOP-DGG type of IC package from Texas Instrument is used in the investigation. The inspection has to identify the print errors such as illegible character, missing characters and up side down printing. The vision inspection of the printed markings on the IC chip is carried out in three phases namely image preprocessing, feature extraction and classification. Projection profile and Moments are employed for feature extraction. A neural network is used as a classifier to detect the defectively marked IC chips. Both feature extraction methods are compared in terms of marking inspection time.
Breast cancer is one of the most critical diseases suffered by many people around the world, making it the most common medical risk they will face. This disease is considered the leading cause of death around the world, and early detection is difficult. In the field of healthcare, where early diagnosis based on machine learning (ML) helps save patients’ lives from the risks of diseases, better-performing diagnostic procedures are crucial. ML models have been used to improve the effectiveness of early diagnosis. In this paper, we proposed a new feature selection method that combines two filter methods, Pearson correlation and mutual information (PC-MI), to analyse the correlation amongst features and then select important features before passing them to a classification model. Our method is capable of early breast cancer prediction and depends on a soft voting classifier that combines a certain set of ML models (decision tree, logistic regression and support vector machine) to produce one model that carries the strengths of the models that have been combined, yielding the best prediction accuracy. Our work is evaluated by using the Wisconsin Diagnostic Breast Cancer datasets. The proposed methodology outperforms previous work, achieving 99.3% accuracy, an F1 score of 0.9922, a recall of 0.9846, a precision of 1 and an AUC of 0.9923. Furthermore, the accuracy of 10-fold cross-validation is 98.2%.
In this paper a radial distribution feeder protection scheme against short circuit faults is introduced. It is based on utilizing the substation measured current signals in detecting faults and obtaining useful information about their types and locations. In order to facilitate important measurement signals features extraction such that better diagnosis of faults can be achieved, the discrete wavelet transform is exploited. The captured features are then utilized in detecting, identifying the faulted phases (fault type), and fault location. In case of a fault occurrence, the detection scheme will make a decision to trip out a circuit breaker residing at the feeder mains. This decision is made based on a criteria that is set to distinguish between the various system states in a reliable and accurate manner. After that, the fault type and location are predicted making use of the cascade forward neural networks learning and generalization capabilities. Useful information about the fault location can be obtained provided that the fault distance from source, as well as whether it resides on the main feeder or on one of the laterals can be predicted. By testing the functionality of the proposed scheme, it is found that the detection of faults is done fastly and reliably from the view point of power system protection relaying requirements. It also proves to overcome the complexities provided by the feeder structure to the accuracy of the identification process of fault types and locations. All the simulations and analysis are performed utilizing MATLAB R2016b version software package.
Novel Coronavirus (Covid-2019), which first appeared in December 2019 in the Chinese city of Wuhan. It is spreading rapidly in most parts of the world and becoming a global epidemic. It is devastating, affecting public health, daily life, and the global economy. According to the statistics of the World Health Organization on August 11, the number of cases of coronavirus (Covid-2019) reached nearly 17 million, and the number of infections globally distributed among most European countries and most countries of the Asian continent, and the number of deaths from the Corona virus reached 700 thousand people around the world. . It is necessary to detect positive cases as soon as possible in order to prevent the spread of this epidemic and quickly treat infected patients. In this paper, the current literature on the methods used to detect Covid is presented. In these studies, the research that used different techniques of artificial intelligence to detect COVID-19 was reviewed as the convolutionary neural network (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) were proposed for the identification of patients infected with coronavirus pneumonia using chest X-ray radiographs By using 5-fold cross validation, three separate binary classifications of four grades (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) were introduced. It has been shown that the pre-trained ResNet50 model offers the highest classification performance (96.1 percent accuracy for Dataset-1, 99.5 percent accuracy for Dataset-2 and 99.7 percent accuracy for Dataset-2) based on the performance results obtained.
Facial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) for classification purpose. The results obtained from the different groups are then fused using Naïve Bayes classifier to make the final decision regards the emotion class. Different tests were performed using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the achieved results showed that the system gives the desired accuracy (100%) when fusion decisions of the facial groups. The achieved result outperforms state-of-the-art results on the same database.
Kinship (Familial relationships) detection is crucial in many fields and has applications in biometric security, adoption, forensic investigations, and more. It is also essential during wars and natural disasters like earthquakes since it may aid in reunion, missing person searches, establishing emergency contacts, and providing psychological support. The most common method of determining kinship is DNA analysis which is highly accurate. Another approach, which is noninvasive, uses facial photos with computer vision and machine learning algorithms for kinship estimation. Each part of the Human -body has its own embedded information that can be extracted and adopted for identification, verification, or classification of that person. Kinship recognition is based on finding traits that are shared by every family. We investigate the use of hand geometry for kinship detection, which is a new approach. Because of the available hand image Datasets do not contain kinship ground truth; therefore, we created our own dataset. This paper describes the tools, methodology, and details of the collected MKH, which stands for the Mosul Kinship Hand, images dataset. The images of MKH dataset were collected using a mobile phone camera with a suitable setup and consisted of 648 images for 81 individuals from 14 families (8 hand situations per person). This paper also presents the use of this dataset in kinship prediction using machine learning. Google MdiaPipe was used for hand detection, segmentation, and geometrical key points finding. Handcraft feature extraction was used to extract 43 distinctive geometrical features from each image. A neural network classifier was designed and trained to predict kinship, yielding about 93% prediction accuracy. The results of this novel approach demonstrated that the hand possesses biometric characteristics that may be used to establish kinship, and that the suggested method is a promising way as a kinship indicator.
This paper focuses on designing distributed wireless sensor network gateways armed with Intrusion Detection System (IDS). The main contribution of this work is the attempt to insert IDS functionality into the gateway node (UBICOM IP2022 network processor chip) itself. This was achieved by building a light weight signature based IDS based on the famous open source SNORT IDS. Regarding gateway nodes, as they have limited processing and energy constrains, the addition of further tasks (the IDS program) may affects seriously on its performance, so that, the current design takes these constrains into consideration as a priority and use a special protocol to achieve this goal. In order to optimize the performance of the gateway nodes, some of the preprocessing tasks were offloaded from the gateway nodes to a suggested classification and processing server and a new searching algorithm was suggested. Different measures were taken to validate the design procedure and a detailed simulation model was built to discover the behavior of the system in different environments.
Image segmentation is a wide research topic; a huge amount of research has been performed in this context. Image segmentation is a crucial procedure for most object detection, image recognition, feature extraction, and classification tasks depend on the quality of the segmentation process. Image segmentation is the dividing of a specific image into a numeral of homogeneous segments; therefore, the representation of an image into simple and easy forms increases the effectiveness of pattern recognition. The effectiveness of approaches varies according to the conditions of objects arrangement, lighting, shadow and other factors. However, there is no generic approach for successfully segmenting all images, where some approaches have been proven to be more effective than others. The major goal of this study is to provide summarize of the disadvantages and the advantages of each of the reviewed approaches of image segmentation.
Iris pattern is one of the most important biological traits of humans. In last years, the iris pattern is used for human verification because of uniqueness of its texture. In this paper, biometric system based iris recognition is designed and implemented using two comparative approaches. The first approach is the Fourier descriptors, in this method the iris features have been extracted in frequency domain, where the low spectrums define the general description of iris pattern, while the high spectrums describes the fine detail. The second approach, the principle component analysis uses statistic technique to select the most important feature values by reducing its dimensionality. The biometric system is tested by applying one-to-one pattern matching procedure for 50 persons. The distance measurement method is applied for Manhattan, Euclidean, and Cosine classifiers for purpose of comparison. In all three classification methods, Fourier descriptors were always advanced principle component analysis in matching results. It satisfied 96%, 94%, and 86% correct matching against 94%, 92%, and 80% for principle component analysis using Manhattan, Euclidean, and Cosine classifiers respectively.