Benchmarking Current and Emerging Approaches to Infrasound Signal Classification

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.

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

Weed is one of the main reason for getting less production in agriculture field. At present, farmers are using herbicides to control the weed but it's having negative impact on crop production. To increase the crop production, farmers want to reduce the usage of herbicides. One of the machine learning algorithm, convolution neural network used to classify the weed and crop with high accuracy. CNN gives the best performance in some critical situations also, such as collected images of different lighting conditions, identification of plant species, overlapped crop with weed and designing an autonomous patch sprayers. This review article presents a brief overview of some significant research efforts in weed detection system by using image processing techniques and convolution neural network in machine learning algorithms. Not only CNN, this article reviewed different successful machine learning techniques of supervised and unsupervised learning approaches such as support vector machines, random forest and artificial neural network. This paper aims at providing different challenges and imperatives of various convolution neural network algorithms used in weed detection. Finally, compared different convolution neural network architectures and finds the best CNN architecture used for weed identification system with respect to accuracy.

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.

We aimed to assess the ability of deep learning (DL) and support vector machine (SVM) to detect a nonperfusion area (NPA) caused by retinal vein occlusion (RVO) with optical coherence tomography angiography (OCTA) images. The study included 322 OCTA images (normal: 148; NPA owing to RVO: 174 [128 branch RVO images and 46 central RVO images]). Training to construct the DL model using deep convolutional neural network (DNN) algorithms was provided using OCTA images. The SVM used a scikit-learn library with a radial basis function kernel. The area under the curve (AUC), sensitivity and specificity for detecting an NPA were examined. We compared the diagnostic ability (sensitivity, specificity and average required time) between the DNN, SVM and seven ophthalmologists. Heat maps were generated. With regard to the DNN, the mean AUC, sensitivity, specificity and average required time for distinguishing RVO OCTA images with an NPA from normal OCTA images were 0.986, 93.7%, 97.3% and 176.9 s, respectively. With regard to SVM, the mean AUC, sensitivity, and specificity were 0.880, 79.3%, and 81.1%, respectively. With regard to the seven ophthalmologists, the mean AUC, sensitivity, specificity and average required time were 0.962, 90.8%, 89.2%, and 700.6 s, respectively. The DNN focused on the foveal avascular zone and NPA in heat maps. The performance of the DNN was significantly better than that of SVM in all parameters (p < 0.01, all) and that of the ophthalmologists in AUC and specificity (p < 0.01, all). The combination of DL and OCTA images had high accuracy for the detection of an NPA, and it might be useful in clinical practice and retinal screening.

To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports. A CNN with word embedding was applied to identify 10 incident types and 4 severity levels. Model training and validation used data sets (n_type = 2860, n_severity = 1160) collected from a statewide incident reporting system. Generalizability was evaluated using an independent hospital-level reporting system. CNN architectures were examined by varying layer size and hyperparameters. Performance was evaluated by F score, precision, recall, and compared to binary support vector machine (SVM) ensembles on 3 testing data sets (type/severity: n_benchmark = 286/116, n_original = 444/4837, n_independent = 6000/5950). A CNN with 6 layers was the most effective architecture, outperforming SVMs with better generalizability to identify incidents by type and severity. The CNN achieved high F scores (> 85%) across all test data sets when identifying common incident types including falls, medications, pressure injury, and aggression. When identifying common severity levels (medium/low), CNN outperformed SVMs, improving F scores by 11.9%-45.1% across all 3 test data sets. Automated identification of incident reports using machine learning is challenging because of a lack of large labelled training data sets and the unbalanced distribution of incident classes. The standard classification strategy is to build multiple binary classifiers and pool their predictions. CNNs can extract hierarchical features and assist in addressing class imbalance, which may explain their success in identifying incident report types. A CNN with word embedding was effective in identifying incidents by type and severity, providing better generalizability than SVMs.

Content filtering is gaining popularity due to easy exposure of explicit visual contents to the public. Excessive exposure of inappropriate visual contents can cause devastating effects such as the growth of improper mindset and rise of societal issues such as free sex, child abandonment and rape cases. At present, most of the broadcasting media sites are hiring censorship editors to label graphic contents manually. Nevertheless, the efficiency is limited by factors such as the attention span of humans and the training required for the editors. This paper proposes to study the effect of usage of Convolutional Neural Network (CNN) as feature extractor coupled with Support Vector Machine (SVM) as classifier in an automated pornographic detection system. Three CNN architectures: Mobile Net, Visual Geometry Group-19 (VGG-19) and Residual Network-50 Version 2 (ResNet50_V2), and two classifiers: CNN and SVM were utilized to explore the combination that produce the best result. Frames of films fed as input into the CNN were classified into two groups: porn or non-porn. The best accuracy was 92.80% obtained using fine-tuned ResNet50_V2 as feature extractor and SVM as classifier. Transfer learning and SVM have improved the CNN model by approximately 10%.

Mobile fitness applications provide the opportunity to show users real-time feedback on their current fitness activity. For such applications, it is essential to accurately track the user's current fitness activity using available mobile sensors, such as inertial measurement units (IMUs). Convolutional neural networks (CNNs) have been shown to produce strong results in different time series classification tasks, including the recognition of daily living activities. However, fitness activities can present unique challenges to the human activity recognition task (HAR), including greater similarity between individual activities and fewer available data for model training. In this paper, we evaluate the applicability of CNNs to the fitness activity recognition task (FAR) using IMU data and determine the impact of input data size and sensor count on performance. For this purpose, we adapted three existing CNN architectures to the FAR task and designed a fourth CNN variant, which we call the scaling fully convolutional network (Scaling-FCN). We designed a preprocessing pipeline and recorded a running exercise data set with 20 participants, in which we evaluated the respective recognition performances of the four networks, comparing them with three traditional machine learning (ML) methods commonly used in HAR. Although CNN architectures achieve at least 94% test accuracy in all scenarios, two traditional ML architectures surpass them in the default scenario, with support vector machines (SVMs) achieving 99.00 ± 0.34% test accuracy. The removal of all sensors except one foot sensor reduced the performance of traditional ML architectures but improved the performance of CNN architectures on our data set, with our Scaling-FCN reaching the highest accuracy of 99.86 ± 0.11% on the test set. Our results suggest that CNNs are generally well suited for fitness activity recognition, and noticeable performance improvements can be achieved if sensors are dropped selectively, although traditional ML architectures can still compete with or even surpass CNNs when favorable input data are utilized.

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.

Medical image processing has become an integral part of disease diagnosis, where technological advancements have brought significant changes to this approach. In this review, a comprehensive comparison between Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) in processing medical images is conducted. Automated medical analysis is becoming increasingly important due to issues of subjectivity in manual diagnosis and potential treatment delays. This research aims to compare the performance of Machine Learning (ML) in medical contexts using MRI, CT scan, and X-ray data. The comparison includes the accuracy rates of CNN and SVM algorithms, sourced from various studies conducted between 2018 and 2022. The results of the comparison show that CNN has higher average accuracy in processing MRI and X-ray data, with average values of 98.05% and 97.27%, respectively. On the other hand, SVM exhibits higher average accuracy for CT scan data, reaching 91.78%. However, overall, CNN achieves an average accuracy of 95.58%, while SVM's average accuracy is at 94.72%. These findings indicate that both algorithms perform well in processing medical data with high accuracy. Although based on these average accuracy rates, CNN demonstrates slightly better capabilities than SVM. Further research and development of more complex models are expected to continue improving the effectiveness of both approaches in disease diagnosis and patient care in the future.

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.

Background/Objectives:The emergence of monkeypox outside its endemic region in Africa has raised significant concerns within the public health community due to its rapid global dissemination. Early clinical differentiation of monkeypox from similar diseases, such as chickenpox and measles, presents a challenge. The Monkeypox Skin Lesion Dataset (MSLD) used in this study comprises monkeypox skin lesions, which were collected primarily from publicly accessible sources. The dataset contains 770 original images captured from 162 unique patients. The MSLD includes four distinct class labels: monkeypox, measles, chickenpox, and normal. Methods: This paper presents an ensemble model for classifying the monkeypox dataset, which includes transformer models and support vector machine (SVM). The model development process begins with an evaluation of seven convolutional neural network (CNN) architectures. The proposed model is developed by selecting the top four models based on evaluation metrics for performance. The top four CNN architectures, namely EfficientNetB0, ResNet50, MobileNet, and Xception, are used for feature extraction. The high-dimensional feature vectors extracted from each network are then concatenated and optimized before being inputted into the SVM classifier. Results: The proposed ensemble model, in conjunction with the SVM classifier, achieves an accuracy of 95.45b%. Furthermore, the model demonstrates high precision (95.51%), recall (95.45%), and F1 score (95.46%), indicating its effectiveness in identifying monkeypox lesions. Conclusions: The results of the study show that the proposed hybrid framework achieves robust diagnostic performance in monkeypox detection, offering potential utility for enhanced disease monitoring and outbreak management. The model’s high diagnostic accuracy and computational efficiency indicate that it can be used as an additional tool for clinical decision support.

In this paper, we study the performance of variants of well-known Convolutional Neural Network (CNN) architectures on different audio tasks. We show that tuning the Receptive Field (RF) of CNNs is crucial to their generalization. An insufficient RF limits the CNN's ability to fit the training data. In contrast, CNNs with an excessive RF tend to over-fit the training data and fail to generalize to unseen testing data. As state-of-the-art CNN architectures - in computer vision and other domains - tend to go deeper in terms of number of layers, their RF size increases and therefore they degrade in performance in several audio classification and tagging tasks. We study well-known CNN architectures and how their building blocks affect their receptive field. We propose several systematic approaches to control the RF of CNNs and systematically test the resulting architectures on different audio classification and tagging tasks and datasets. The experiments show that regularizing the RF of CNNs using our proposed approaches can drastically improve the generalization of models, out-performing complex architectures and pre-trained models on larger datasets. The proposed CNNs achieve state-of-the-art results in multiple tasks, from acoustic scene classification to emotion and theme detection in music to instrument recognition, as demonstrated by top ranks in several pertinent challenges (DCASE, MediaEval).

Every year, new Convolutional Neural Network (CNN) architectures appear to deal with different problems in the activity of image and video recognition. These architectures usually work along the ImageNet dataset for looking for the best performance of the CNNs without taking into account the video task where they are used. This can represent a problem if the task is Human Action Recognition (HAR) in video, since the CNN architectures are pre-trained with an image dataset that can practically contain any object, while HAR problem requires consecutive frames of people doing actions. To prove the idea that using CNNs pre-trained on an image dataset does not always achieve the best performance on a video dataset and that, therefore, it is worth comparing the performance of different CNNs under similar circumstances for the HAR problem, this work proposes an analysis between eight different CNN architectures. Each one of the CNN was exclusively trained with RGB images, which were extracted from the frames of the different classes of videos of HMDB51 dataset. To make the classification of an activity in video, we average the predictions taking into account the successes. We also made some ensembles with the best performance CNNs to measure the improvement in accuracy. Our results suggest that Xception is a strong baseline model that could be used by the community to make their comparisons of their proposals more robust.