Classification of Mobile Usage Car Driving Activities Using Convolutional Neural Network

With the gradual improvement of people's living standards, the production and drinking of all kinds of food is increasing. People's disease rate has increased compared with before, which leads to the increasing number of medical image processing. Traditional technology cannot meet most of the needs of medicine. At present, convolutional neural network (CNN) algorithm using chaotic recursive diagonal model has great advantages in medical image processing and has become an indispensable part of most hospitals. This paper briefly introduces the use of medical science and technology in recent years. The hybrid algorithm of CNN in chaotic recursive diagonal model is mainly used for technical research, and the application of this technology in medical image processing is analysed. The CNN algorithm is optimized by using chaotic recursive diagonal model. The results show that the chaotic recursive diagonal model can improve the structure of traditional neural network and improve the efficiency and accuracy of the original CNN algorithm. Then, the application research and comparison of medical image processing are performed according to CNN algorithm and optimized CNN algorithm. The experimental results show that the CNN algorithm optimized by chaotic recursive diagonal model can help medical image automatic processing and patient condition analysis.

A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database

Hoax news is a serious problem in this era. Many people are easily led by opinions made by certain people without seeing the truth or looking for existing facts. To overcome this, many researchers have conducted hoax news detection using various algorithms. In some studies, it is said that Random Forest has better performance to overcome this hoax news problem. In other studies, it is also said that CNN has the same level of performance as the Random Forest algorithm. In addition, the problem that is often found is the error in prediction due to improper preprocessing methods. Therefore, in this research, the appropriate preprocessing method is searched by using several preprocessing scenarios for the Convolutional Neural Network (CNN) and Random Forest algorithms. Therefore, in addition to finding the right preprocessing method for each algorithm, a performance comparison is also carried out on the CNN and Random Forest algorithms using a dataset of 4000 news facts from Kompas.com and 4000 hoax news from the turnback.hoax site. the results obtained in this study are random forest has an average model accuracy value of 90% and the CNN algorithm has an average model accuracy value of 60% using the same extraction method, namely TFIDF combined with Ngrams worth one or unigram

The discussion of "can machines think?" which started with the invention of the modern computer, brought along the question of "can machines design?" by researchers in the design field. These developments in information technologies have also affected the architecture. Artificial intelligence applications are encountered in many areas such as pricing estimation, energy conservation security systems of buildings, ventilation systems, user-oriented interactive design solutions, computer-aided programs used in the plan production phase and design process. When the literature on artificial intelligence applications in the architecture is reviewed, it can be seen that it generally includes shape grammars, graph theory, decision trees, constraint-based models, machine learning methods, RNN (Recursive Neural Networks), CNN (Convolutional Neural Network) and GAN (Generative Adversarial Network) algorithms. In this study, the use of artificial intelligence algorithms in architecture was examined, and an example was designed to determine the architectural structures of different periods by using CNN (Convolutional Neural Network). In the study, the open source TensorFlow library developed by Google and the Python programming language were used. Employing a statistical approach and utilizing convolutional neural networks (CNNs), a study has successfully classified the current flow patterns of buildings based on datasets comprising facades of Gothic, Modern, and Deconstructivist architectural styles. The findings demonstrate the efficacy of CNNs in accurately distinguishing the intricate details of diverse architectural styles. Recognizing elements from different periods using the CNN algorithm can examine not only individual buildings but also the relationship of buildings with their environments. It can also gain an important place in the field of conservation of the architectural discipline. The historical processes, aesthetic features and changes of protected buildings can be learned with the CNN algorithm and can guide restoration decisions. As a result of the study, the employed CNN-based model can correctly classify structures with 84.66% accuracy rate.

With the development of the Internet of Things (IoT) and virtual reality (VR) technology, the demand for high-precision gesture intelligent analysis of a human–machine interaction module for IoT–VR systems is increasing. Therefore, random forest (RF) and convolution neural network (CNN) algorithms are used in this study to build an intelligent gesture recognition model. The experiments were conducted to test the application performance of the design model. The test results show that the qualification rate of the analytical model designed in this study is significantly higher than that of the comparative model. When the threshold is determined to be 43.26 mm, the analytical qualification rates of the RF-CNN (the method of combining RF with CNN algorithms), faster regions with CNN features (Faster-RCNN), and RF models are 82.41%, 76.10%, and 59.10%, respectively. The calculation time of the RF–CNN model is between the two comparative models. From the test data, it can be observed that the research results have certain significance for improving the accuracy of gesture machine recognition technology in China’s VR Internet of Things (IoT) system.

The aim of this study was to investigate the value of single-photon emission computed tomography (SPECT) based on the convolutional neural network (CNN) algorithm in thyroid diseases. Thirty-five patients with thyroid disease from the hospital were selected as the observation group, and another 35 healthy volunteers were selected as the control group. The constructed model of SPECT based on the CNN algorithm was compared with the backpropagation neural network (BPNN) algorithm, which was then applied to the SPECT of 35 patients with thyroid disease. It turned out that as the number of iterations increased, the parameter training of CNN was gradually sufficient, the network model was continuously optimized, and the accuracy gradually increased. From the data results, the Dice value of the proposed CNN algorithm was higher than that of the BPNN algorithm and the segmentation effect was relatively good. The visual index of the thyroid/neck of the observation group (2.68 ± 1.32) was remarkably inferior to that of the control group (12.347.54) ( P < 0.05 ). The visual index of the thyroid/submandibular gland in the observation group (1.02 ± 0.41) was remarkably inferior to that of the control group (8.89 ± 4.86) ( P < 0.05 ). The visual index of the thyroid/parotid gland in the observation group (1.04 ± 0.58) was remarkably inferior to that of the control group (8.53 ± 4.25) ( P < 0.05 ). In addition, 99mTcO4-SPECT had a sensitivity of 95.2%, a specificity of 90.3%, and an accuracy of 91.5% in the diagnosis of thyroid diseases. The area under the curve of the receiver operating characteristic curve for 99mTcO4-SPECT diagnosis of thyroid disease is 0.958, and the 95% confidence interval is 0.834∼1. In summary, the SPECT based on the CNN algorithm proposed in this study has a good segmentation effect and can accurately locate the anatomical information of thyroid diseases, which can replace the traditional diagnostic methods for the diagnosis of thyroid diseases.

In the field of medicine, with the introduction of computer systems that can collect and analyze massive amounts of data, many non-invasive diagnostic methods are being developed for a variety of conditions. In this study, our aim is to develop a non-invasive method of classifying respiratory sounds that are recorded by an electronic stethoscope and the audio recording software that uses various machine learning algorithms.In order to store respiratory sounds on a computer, we developed a cost-effective and easy-to-use electronic stethoscope that can be used with any device. Using this device, we recorded 17,930 lung sounds from 1630 subjects.We employed two types of machine learning algorithms; mel frequency cepstral coefficient (MFCC) features in a support vector machine (SVM) and spectrogram images in the convolutional neural network (CNN). Since using MFCC features with a SVM algorithm is a generally accepted classification method for audio, we utilized its results to benchmark the CNN algorithm. We prepared four data sets for each CNN and SVM algorithm to classify respiratory audio: (1) healthy versus pathological classification; (2) rale, rhonchus, and normal sound classification; (3) singular respiratory sound type classification; and (4) audio type classification with all sound types. Accuracy results of the experiments were; (1) CNN 86%, SVM 86%, (2) CNN 76%, SVM 75%, (3) CNN 80%, SVM 80%, and (4) CNN 62%, SVM 62%, respectively.As a result, we found out that spectrogram image classification with CNN algorithm works as well as the SVM algorithm, and given the large amount of data, CNN and SVM machine learning algorithms can accurately classify and pre-diagnose respiratory audio.

Facial recognition systems, which are a type of biometric systems, use the person’s facial features to identify people, and determine the information in the position between the eyes, nose, cheekbones, jawline, chin and the like and reveal a personal numerical model. The main purpose of this study is to determine the gender and age group of a selected image. By making necessary studies on the image, it is aimed to separate the gender of the person and to extract child or adult information. This study is proposed as research that performs noise reduction using the fuzzy logic fire filter algorithm and classifies the result by gender using a convolutional neural network (CNN), matrix completion and deep learning techniques. CNN algorithm, which is one of the deep facial recognition algorithms that can be used in the recognition of facial images, is the algorithm used in the study. In this study, the data obtained after the cleaning of the noises in the photographs were classified. The fuzzy logic fire filter algorithm is used to remove the noise in the image, and the image is classified according to gender and age range with the help of CNN and TensorFlow algorithms. The system has been trained using certain learning and test data sets and these algorithms. The image entered into the system is converted into a matrix expression and compared with the matrix expressions on the trained system. According to the comparison result, the image is classified as male, female, boy or girl, which matrix is closer to the value group. The data we obtained as a result of our work and the success rates show the applicability of the study. The main purpose of the study is to make gender and age group estimations according to the image with CNN classifications.

Retinal fundus images play a crucial role in the early detection of eye problems, aiding in timely diagnosis and treatment to prevent vision loss or blindness. With advancements in technology, Convolutional Neural Network (CNN) algorithms have emerged as effective tools for recognition, delineation, and classification tasks. This study proposes a comprehensive review of CNN algorithms used for retinal fundus image segmentation and classification. Our review follows a systematic approach, exploring diverse repositories to identify studies employing CNN to segment and classify retinal fundus images. Utilizing CNNs in the segmentation and classification of retinal fundus images can enhance the precision of segmentation outcomes and alleviate the sole dependence on human experts. This approach enables more accurate segmentation results, reducing the burden on human experts. A total of sixty-two studies are included in our review, analyzing aspects such as database usage and the advantages and disadvantages of the methods employed. The review provides valuable insights, limitations, observations, and future directions in the field. Despite certain limitations, the findings indicate that CNN algorithms consistently achieve high accuracies. The comprehensive examination of the included studies sheds light on the potential of CNN in retinal fundus image analysis.

This research aims to address challenges in classifying types of banana using the Convolutional Neural Network (CNN) algorithm. The research background reflects the need for an automatic classification system to enhance the efficiency of banana farmers, considering that the manual methods still widely used have weaknesses in consistency and accuracy. Previous studies have successfully employed CNN for classifying various objects, including fruits. The CNN method is implemented using the VGG16 model training approach and prepared training data. This study focuses on three types of bananas—male, "kepok," and "muli"—with a specific emphasis on seed classification. Testing evaluates the model's accuracy, revealing a 78% accuracy rate. The application of the CNN algorithm can improve efficiency in classifying banana types. Despite achieving a 78% accuracy rate, the test results also indicate good values for precision (81%) and recall (78%). Therefore, the CNN algorithm can be considered an effective solution for automatically addressing issues in classifying banana types, contributing positively to banana farmers' productivity in Indonesia, especially when examining Accuracy, Precision, and Recall in percentage form.

The article is devoted to the problem of comparing the effectiveness of feedforward (FF) and convolutional neural networks (CNN) algorithms in the problems of handwritten digit recognition and classification. In recent years, the attention of many researchers to the FF and CNN algorithms has given rise to many hybrid models focused on solving specific problems. At the same time, the efficiency of each algorithm in terms of accuracy and labour intensity remains unclear. It is shown that in classical problems, FFs can have advantages over CNN in terms of labour intensity with the same accuracy of results. Using the handwritten digits data from the MNIST database as an example, it is shown that FF algorithms provide greater accuracy and require less computation time than CNN.

The rapid development of Internet technology has led to increasingly complex web service systems. The resulting large number of component interactions pose a challenge to anomaly detection, which is realized primarily by operation and maintenance (OM) personnel through the deployment, management, and monitoring of various key performance indicators (KPIs). Anomalous behaviors during daily OM often cause problems in KPI data; these problems include high noise, high dimensionality, and large-scale data streams. In addition, anomalies in KPI data occur infrequently and are of various types. These factors are the cause of the very low accuracy currently observed in conventional machine learning methods for detecting anomalies in large OM systems. Hence, a convolutional long short-term memory (C-LSTM) neural network is presented in this study to detect anomalies in small datasets that contain a variety of anomalies. First, a sliding window is used to preprocess the KPI data. Then, a C-LSTM neural network, which combines the features of the convolutional neural network (CNN) and LSTM algorithms, is employed to effectively model the time and numerical information contained in the preprocessed KPI data. Finally, the C-LSTM algorithm is tested on the datasets used in the competition of the Artificial Intelligence for Information Technology Operations (AIOPs) Active Network Management (ANM) 2018 Fall Project. The results show that the C-LSTM prediction algorithm outperforms the conventional LSTM and CNN algorithms in terms of its capacity to detect anomalies in small datasets that contain various anomalies, with a 12.90% higher accuracy, 5.68% higher recall, and 9.58% higher F1-score.

Mangosteen contains a substance known as Xanthone, a phytochemical compound with the distinctive red component in mangosteen that is known for its properties as an anticancer, antibacterial, and anti-inflammatory agent. Additionally, Xanthone has the potential to strengthen the immune system, promote overall health, support mental well-being, maintain microbial balance in the body, and improve joint flexibility. The mangosteen fruit is consumable when it reaches maturity, displaying a dark purplish-black color. Besides the edible part of the fruit, the peel also possesses remarkable medicinal properties. To detect whether the fruit is ripe or not, this research employs image processing techniques. The study utilizes the HSV (Hue, Saturation, and value) color space method, CNN (Convolutional Neural Network) algorithm, and SVM (Support Vector Machine) algorithm. These methods and algorithms are chosen for their relatively high accuracy levels. The dataset used in this research is obtained from mangosteen datasets available on Kaggle. The results of this study indicate that the HSV method achieved an accuracy of 86.6%, SVM achieved an accuracy of 87%, and CNN achieved an accuracy of 91.25%. From the achieved accuracies, it is evident that the CNN algorithm yields higher accuracy compared to the others.

Controlling the spread of Coronavirus Disease 2019 (COVID-19) and reducing its impact on public health need prompt identification and treatment. To improve diagnostic accuracy, this study attempts to create and assess a Multi-Modality COVID-19 Diagnosis System that integrates X-ray, Electrocardiogram (ECG), and Computed Tomography (CT) images utilizing Convolutional Neural Network (CNN) algorithms. To increase the accuracy of COVID-19 diagnosis, the suggested system incorporates data from many imaging modalities in a novel way, including cardiac symptoms identified by ECG data. This approach has not been thoroughly studied in the literature to date. The system analyses CT, ECG, and X-ray images using CNN algorithms, including Visual Geometry Group 19 (VGG19) and Deep Convolutional Networks (DCNN). While ECG data helps detect related cardiac symptoms, CT and X-ray images offer precise insights into lung abnormalities indicative of COVID-19 pneumonia. Noise reduction and image smoothing are accomplished through the implementation of Gaussian filtering algorithms. After extracting characteristics suggestive of either bacterial or viral pneumonia, a deep neural network refines them for accurate COVID-19 identification. Python software is employed throughout the system's implementation. A thorough evaluation of the trained CNN model using separate datasets revealed an amazing 99.12% accuracy rate in COVID-19 detection from chest imaging data. The diagnostic accuracy of the suggested DCNN model was much higher than that of the current models, including Random Forest and Linear Ridge. The Multi-Modality COVID-19 Diagnosis System uses cutting-edge CNN algorithms to seamlessly combine ECG, X-ray, and CT imaging data to provide a highly accurate diagnosis tool. With the implementation of this approach, medical personnel could potentially be able to diagnose COVID-19 more quickly and accurately, which would improve the disease's treatment and control.

Insulators are one of the most used electrical equipment in the power system. Long-term exposure to the air environment can easily cause pollution flashovers and have a serious impact on the safe operation of the power transmission system. In this paper, after obtaining the discharge image of the insulator taken by the ultraviolet imager in the artificial pollution test, the image is preprocessed, and the convolutional neural network (CNN) algorithm is used to evaluate the pollution state of the insulator after reducing the image dimension. The results show that the pollution discharge status of insulators is positively correlated with their surface pollution and humidity. The average recognition accuracy of insulator pollution status obtained by the CNN algorithm can reach eighty-five percent, which can meet the requirements of engineering insulator pollution recognition.