Convolutional Neural Network Algorithm Research Articles

Sleeping Stage Classification Using CNN

Abstract: Maintaining proper health and mental stability is critical for overall health and well-being. Despite a good deal of research investment, sleep quality continues to be a crucial public challenge. Nowadays, people of all age groups are affected by improper sleep quality. Poor sleep can lead to a variety of neurological disorders. Sleep disorders are common in all subsets of the population, independently of gender. This public health challenge greatly affects quality of life in terms of both physical and mental health. Insomnia, parasomnias, sleep- related breathing difficulties, hypersomnia, bruxism, narcolepsy, and circadian rhythm disorders are some common examples of sleep-related disorders. Some of these disorders can be treated with proper analysis of early symptoms; in such cases, adequate sleep quality is essential for the patient’s recovery. Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleepdisorders, and population health. While still in its nascent stage, there are several challenges which preclude AI’s generalizability and wide-reaching clinical applications. Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms and deep learning algorithm prior to its inclusion in the sleep clinic. In this project, we can develop the framework for sleeping stage classification for both subjective data and ECG data and classify the data using Convolutional neural network algorithm to analyse the multiple sleeping stages.

Artwork Traceability and Anti-Counterfeiting Model Based on Block Chain Technology

The Chinese art market fostered the advancement of culture and spirituality. Nevertheless, false and counterfeit artwork is not uncommon. The art technology and security introduce the innovative method to determine Artwork Traceability and Anti-Counterfeiting. The aim of the work is to prevent the creation and circulation of fake or unauthorized copies of artworks. Because of the cryptographic security and immutability of block chains, it would be challenging to falsify artwork records or make accurate duplicates. This would foster confidence in the art market and shield collectors and artists from monetary losses and harm to their reputations. The data on mobile Application Data Center (ADC) platform and the cleansing the data using sub-aperture keystone transform matched filtering (SAKTMF) and the preprocessed data is fed into the Hierarchically Gated Recurrent Neural Network (HGRNN) classify the art work and Anti-Counterfeiting. The Bald Eagle Search Optimization (BES) Algorithm is used to optimize the weight parameter of HGRNN. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such as Dual-Branch Multi-Scale Feature Fusion network (DMF-Net), Region Convolutional Neural Network (R-CNN) and practical Byzantine fault tolerance (PBFT) algorithm. The gained results of the proposed HGRNN-BES method attains higher accuracy 97%, 96%, and 99%, higher precision 98%, 99%, and 97%.

Identifying People Wearing Masks in the Wild by Yolov7 Algorithm

Rapid face detection is an important matter at the present time, as face detection while wearing a mask has today become an important security matter in institutions, as well as protecting society from the spread of diseases, especially after the outbreak of infection with the Corona virus. Covid-19 virus. Due to the rapid spread of the Covid-19 pandemic, there is a need for society to adhere to wearing a mask in all public institutions, to prevent the spread of this disease. Researchers worked to find solutions to recognize faces and distinguish a person's identity. There was a problem in detecting faces and recognizing them easily, as researchers found many solutions to detect faces. So far, detecting faces while wearing a mask has problems with accuracy. In this research, a model of the deep learning algorithm, YOLOv7, will be used. It is a YOLO model that is characterized by accuracy and speed compared to previous YOLO models, and compared to a deep learning algorithm that performs two-stage detection, such as the CNN algorithm. Here the YOLOv7 model of the YOLO algorithm is proposed for face detection and recognition with and without mask. It is a model in which the structure of the algorithm has been modified, as it is distinguished by its speed in detecting faces compared to its predecessors. also reviewed most of the experiments with algorithm YOLO (You Only Look Once) detection algorithms and CNN (Convolutional Neural Network), and noticed that YOLOv7 is a better model than previous YOLO models in detecting faces while wearing a mask in terms of speed and accuracy. Face detection and discrimination has become very important at the present time from a security standpoint in all public places and requires accuracy and speed in detection.

FRUIT DISEASE DETECTION USING COLOR AND TEXTURE ANALYSIS BY CNN

Now-a-days as there is prohibitive demand for agricultural industry, effective growth and improved yield of fruit is necessary and important. For this purpose farmers need manual monitoring of fruits from harvest till its progress period. But manual monitoring will not give satisfactory result all the times and they always need satisfactory advice from expert. So it requires proposing an efficient smart farming technique which will help for better yield and growth with less human efforts. We introduce a technique which will diagnose and classify external disease within fruits. Whereas system that we have come up with, uses image processing techniques for implementation as image is easy way for conveying. In the proposed work, OpenCV library is applied for implementation, the images are catalogue and mapped to their respective disease categories on basis of four feature vectors colour, morphology, texture and structure of hole on the fruit. Convolutional Neural Network (CNN) concept is used for pattern matching and classification of diseases.. Keywords: CNN Algorithm ,OpenCV ,Image processing techniques

Pneumonia Detection Using Deep Learning

Lung disease called pneumonia is brought on by a bacterial infection. An effective treatment plan heavily depends on an early diagnosis. An expert radiologists can typically diagnose the condition with a chest x-ray. For several reasons, including the emergence of an illness that is not visible on a chest x-ray or the possibility that it is mistaken for another ailment, the diagnosis may be arbitrary. Thus, to assist clinicians, automated diagnostic technologies are required. In this work, a deep learning system for picture classification the Convolutional Neural Networks algorithm was constructed on a dataset. Chest radiographs are included in the data set, and the created model is assessed using a few statistical factors. Key Words: Diagnosis, computer-aided, deep learning, pneumonia, convolution neural networks, mass chest x-ray, chest x-ray14.

Enhancing Brain Tumor Classification: A CNN-Based Approach with InceptionV3 and Xception

Brain tumors are among the most aggressive and deadly diseases, with a very short life expectancy at the highest grade. To combat this, early detection and treatment are crucial.In this approach, MRI images are used to analyze brain abnormalities. The manual investigation of brain tumor classification is a time-consuming task, and there might be possibilities of human errors. Hence, accurate analysis in a short span of time is essential. This approach presents the automatic brain tumor classification algorithm using a highly accurate Convolutional Neural Network (CNN), InceptionV3 and Xception algorithm for classification of Glioma, Meningioma and No tumor. The brain part is initially segmented by a thresholding approach followed by a morphological operation. The brain MRI is classified using CNN, Inceptionv3, and Xceptionv3 algorithms. The system's performance is evaluated using precision, recall, F1 score, and accuracy parameters.

Identification of Paddy Stages from Images using Deep Learning

Rice, a crucial global staple, is integral to food security. Precise identification of paddy growth stages, booting, heading, anthesis, grain filling, and grain maturity is vital for agricultural decisions. However, a gap exists in recognizing these stages using red-green-blue (RGB) images. This study uses state-of-the-art computer vision and deep learning classification (Convolutional Neural Networks) algorithms to address this gap. Among the studied algorithms, EfficientNet_B0 achieved an impressive 82.8% overall accuracy. Notably, increasing image size from 64X64 pixels to 128X128 pixels significantly enhanced accuracy. A detailed assessment of growth stages revealed varying accuracy levels, with boot leaf being the most accurately detected (95.1%) and anthesis being the most challenging (72.28%). This work significantly advances automated monitoring, empowering researchers in real-time decision-making.

Enhancing Robotic Grasping Detection Accuracy With the R2CNN Algorithm and Force-Closure

Abstract This study aims to use an improved rotational region convolutional neural network (R2CNN) algorithm to detect the grasping bounding box for the robotic arm that reaches supermarket goods. This algorithm can calculate the final predicted grasping bounding box without any additional architecture, which significantly improves the speed of grasp inferences. In this study, we added the force-closure condition so that the final grasping bounding box could achieve grasping stability in a physical sense. We experimentally demonstrated that deep model-treated object detection and grasping detection are the same tasks. We used transfer learning to improve the prediction accuracy of the grasping bounding box. In particular, the ResNet-101 network weights, which were originally used in object detection, were used to continue training with the Cornell dataset. In terms of grasping detection, we used the trained model weights that were originally used in object detection as the features of the to-be-grasped objects and fed them to the network for continuous training. For 2828 test images, this method achieved nearly 98% accuracy and a speed of 14–17 frames per second.

Development of a Mobile-Based Application for Classifying Caladium Plants Using the CNN Algorithm

Caladium is a popular ornamental plant and has business potential. However, difficulties in recognizing the type of Caladium often occur because of the similarities in shape, pattern, and color of the leaves between the different kinds of Caladium. To overcome this problem, research will use machine learning with the Convolutional Neural Network (CNN) algorithm to build a mobile application that can accurately classify four types of Caladiums. The data set used is 1200 data with four classes; each class has 300 data. The best model is found with the parameter epoch 100, learning rate 0.001, and batch size 64. The model is then implemented in a mobile application with two menus, "Take a photo" and "Choose an image," which will display the classification output and confidence values of the four types of Caladiums. Testing with 30 test data per class achieves 0.975 accuracy on both menus. On the “Take a photo” menu, precision is 0.974, recall is 0.9725, and f1-score is 0.965. Meanwhile, on the “Choose an image” menu a precision and recall value is 0.975, and f1-score value of 0.97.

An Artificial Intelligence Driven Approach for Classification of Ophthalmic Images using Convolutional Neural Network: An Experimental Study.

Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning to expedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creation of deep learning systems that analyze clinical image data to detect disease-specific features with maximum sensitivity. Moreover, these systems hold promise of early accurate diagnosis and treatment of patients with common progressive diseases. DenseNet, ResNet, and VGG-16 are among a few of the deep learning Convolutional Neural Network (CNN) algorithms that have been introduced and are being investigated for potential application within ophthalmology. In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal color fundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) the relative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model to serve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse disease manifestations. We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted in significantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract, glaucoma, diabetic retinopathy. The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework.

The application of gated recurrent unit algorithm with fused attention mechanism in UWB indoor localization

Amidst the increasing reliance on indoor positioning in areas such as large shopping malls, airports, and train stations, the precision of ultra-wideband (UWB) technology stands out for its centimeter-level accuracy. However, non-line-of-sight (NLOS) errors caused by people’s movements significantly reduce indoor positioning accuracy. In response, this paper presents an innovative blend of gated recurrent unit algorithm with fused attention mechanism (GRU_Attention). This algorithm adeptly captures the dynamism in UWB signals and intelligently focuses on pivotal features to actualize accurate three-dimensional positioning while effectively reducing NLOS error. It has been demonstrated that GRU_Attention algorithm can reach 5.6 cm accuracy level through experiments, which improved by 74.47 %, 87.44 %, 14.46 %, 43.82 %, and 29.07 % respectively compared with the backpropagation (BP), least square (LS), GRU, convolutional neural network (CNN), and fused attention mechanism convolutional neural network (CNN_Attention) algorithms. This algorithm charts a new pathway to overcome NLOS errors in indoor personnel movement scenes.

Improved Convolutional Neural Network Algorithm for Student Behavior Detection in the Classroom

The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to improve the accuracy of student classroom behavior detection algorithm, this paper proposes a student classroom behavior detection method based on improved convolutional neural network algorithm. Firstly, the student behavior detection dataset is constructed, and the student classroom behavior detection technology scheme is designed; secondly, in order to improve the detection accuracy, the features are extracted by using the new jumping bi-directional paths, and the attention mechanism module is added at different positions to improve the path aggregation network; weekly, the embedding positions of the attention mechanism strategy are determined by analyzing multiple sets of experiments, and the proposed student classroom behavior detection algorithm's effectiveness and superiority.

Weed species classification with UAV imagery and standard CNN models: Assessing the frontiers of training and inference phases

Accurate weed species identification is crucial for effective site-specific weed management (SSWM), enabling targeted and timely control measures for each weed in crop field. This study advanced the current approach to species-level weed identification during the early growth stage by integrating unmanned aerial vehicles (UAVs) imagery with standard convolutional neural networks (CNNs) models such as VGG16, Resnet152 and Inception-Resnet-v2. For this, a robust dataset was created with 33,467 labels of weeds (Atriplex patula, Chenopodium album, Convolvulus arvensis, Cyperus rotundus, Lolium rigidum, Portulaca oleracea, Salsola kali, Solanum nigrum) and crops (maize, tomato), which was subjected to different training, validation and test scenarios. Model inputs were adjusted in order to align them with the information represented by the UAV images. Initially, models were developed in balanced scenarios, gradually increasing label numbers to assess their performance. Inception-ResNet-v2 achieved over 90% accuracy with 400 labels, while ResNet152 and VGG16 required 600 and 800 labels, respectively, for similar accuracy. In a more complex and realistic scenarios with unbalanced datasets, Inception-ResNet-v2 outperformed, likely due to its deeper architecture and enhanced capability to capture intricate features and patterns within UAV images. The study emphasized the importance of the minority-to-majority species ratio in unbalanced datasets, which affects minority species classification. To prevent misclassification, it is crucial to determine the right number of labels for CNN model training and validation. Weed maps were generated after species classification using the Faster R–CNN algorithm as an object detector. This advancement in methodology facilitates the precise and efficient implementation of SSWM techniques.

XAI-FusionNet: Diabetic foot ulcer detection based on multi-scale feature fusion with explainable artificial intelligence

Diabetic foot ulcer (DFU) poses a significant threat to individuals affected by diabetes, often leading to limb amputation. Early detection of DFU can greatly improve the chances of survival for diabetic patients. This work introduces FusionNet, a novel multi-scale feature fusion network designed to accurately differentiate DFU skin from healthy skin using multiple pre-trained convolutional neural network (CNN) algorithms. A dataset comprising 6963 skin images (3574 healthy and 3389 ulcer) from various patients was divided into training (6080 images), validation (672 images), and testing (211 images) sets. Initially, three image preprocessing techniques - Gaussian filter, median filter, and motion blur estimation - were applied to eliminate irrelevant, noisy, and blurry data. Subsequently, three pre-trained CNN algorithms -DenseNet201, VGG19, and NASNetMobile - were utilized to extract high-frequency features from the input images. These features were then inputted into a meta-tuner module to predict DFU by selecting the most discriminative features. Statistical tests, including Friedman and analysis of variance (ANOVA), were employed to identify significant differences between FusionNet and other sub-networks. Finally, three eXplainable Artificial Intelligence (XAI) algorithms - SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Grad-CAM (Gradient-weighted Class Activation Mapping) - were integrated into FusionNet to enhance transparency and explainability. The FusionNet classifier achieved exceptional classification results with 99.05% accuracy, 98.18% recall, 100.00% precision, 99.09% AUC, and 99.08% F1 score. We believe that our proposed FusionNet will be a valuable tool in the medical field to distinguish DFU from healthy skin.

Convolutional neural network-based wind pressure prediction on low-rise buildings

A new model of wind pressure prediction for low-rise buildings is built based on deep convolutional neural network (CNN), and the model is trained and tested by the aerodynamic data from international open database. The prediction accuracy is compared with that of the literature, and then comprehensive error analysis on all 335 predicted taps under 3 untrained wind directions are carried out for mean and RMS pressure coefficients by various error metrics. The study shows that the distributions of mean and RMS coefficients predicted by the CNN model are closer to the experimental results in comparison with those predicted by the traditional artificial neural network (ANN) model and the common deep neural network (DNN) model without the convolution and pooling layers. The relative error on the corner tap is 13.5%, 4.3% and 0.1% by the ANN, DNN, and CNN models respectively. The mean-square-error (MSE) of the corner bay and the whole roof are also eminently reduced by the CNN model. By introducing the convolutional and pooling layers, more prediction errors are confined within 5% and basically no errors exceed 20%. Consequently, the CNN algorithm can be applied in solving various wind pressure prediction problems on buildings in the future work.

Refining tomato disease recognition: hyperparameter tuning on ResNet-101 architecture for precise leaf-based classification

Tomatoes plants are widely recognized as versatile vegetables globally. This study aims to develop a high-precision web interface for classifying various leaf diseases in tomatoes. Utilizing a convolutional neural network (CNN) algorithm using the residual network-101 (ResNet-101) architecture, this tool aids farmers in accurately identifying leaf diseases in tomatoes, thereby reducing the risk of crop failure. The dataset comprises 6,800 images, categorized into four classes: early blight, spider mites two spotted, tomato yellow leaf curl virus, and healthy tomatoes, each containing 1,700 images. Hyperparameter tuning was conducted as part of an experiment aimed at enhancing the performance of the model. Experiments involved varying epoch values (10, 25, 30, 50, 60, 75, 100, and 110), a fixed batch size of 4, different learning rates (0.1, 0.01, 0.001, 0.0001), and num workers (4, 8, 16). The results demonstrated an accuracy of 99% with 100 epochs, a batch size of 4, a learning rate of 0.001, and 16 num workers. Consequently, this research contributes to a deeper understanding of disease management in tomato plants, ensuring optimal quality and quantity of the harvest.

Design of sports achievement prediction system based on U-net convolutional neural network in the context of machine learning

Sports plays a pivotal role in national development. To accurately predict college students' sports performance and motivate them to improve their physical fitness, this study constructs a sports achievement prediction system by using a U-Net Convolutional Neural Network (CNN) in machine learning. Firstly, the current state of physical education teachers’ instructional proficiency is investigated and analyzed to identify existing problems. Secondly, an improved U-Net-based sports achievement prediction system is proposed. This method enhances the utilization and propagation of network features by incorporating dense connections, thus addressing gradient disappearance issues. Simultaneously, an improved mixed loss function is introduced to alleviate class imbalance. Finally, the effectiveness of the proposed system is validated through testing, demonstrating that the improved U-Net CNN algorithm yields superior results. Specifically, the prediction accuracy of the improved network for sports performance surpasses that of the original U-Net by 4.22 % and exceeds that of DUNet by 5.22 %. Compared with other existing prediction networks, the improved U-Net CNN model exhibits a superior achievement prediction ability. Consequently, the proposed system enhances teaching and learning efficiency and offers insights into applying artificial intelligence technology to smart classroom development.

Detection of Diabetic Foot Ulcers Using Dense Net Framework

Diabetic Foot Ulcers (DFUs) pose a significant health care challenge due to their prevalence and potential complications. Early and accurate detection of DFUs is crucial for timely intervention and prevention of severe complications. This study proposes an innovative approach for automated DFU detection utilizing Convolutional Neural Networks (CNNs), a powerful class of deep learning algorithms widely recognized for their proficiency in image analysis tasks. The proposed CNN model is trained on a comprehensive dataset of foot images, encompassing a diverse range of DFU types, stages, and conditions. The training process involves learning intricate patterns and features indicative of DFUs, enabling the model to generalize well to unseen data. The CNN algorithm's effectiveness in feature extraction and spatial hierarchy learning is harnessed to identify subtle visual cues associated with DFUs, enhancing diagnostic accuracy. The proposed system is designed to operate on medical images, particularly those obtained through various imaging modalities such as digital photography or thermal imaging. Through rigorous validation and performance evaluation, the CNN model exhibits promising results, showcasing its potential as a reliable tool for automated DFU detection. The integration of this technology into clinical practice holds the promise of expediting the diagnostic process, facilitating timely medical interventions, and ultimately improving patient outcomes. This research contributes to the ongoing efforts in leveraging advanced technologies to address critical healthcare challenges, particularly in the realm of diabetic care and wound management.

Finding Missing Person Using Face Recognition

Abstract: This project presents a novel approach to enhance the efficiency and effectiveness of locating missing persons through the utilization of face recognition technology The main objective is to develop a robust system that can extract facial features, match them with a database, and notify the missing person's location. The system uses a special type of artificial intelligence called CNN to compare faces with the database and find the best matches. The face recognition system uses the CNN (Convolutional Neural Network) algorithm to compare the extracted faces with the database and find the best matches. The alert module sends notifications to the family members of the missing person with the location information if a match is found. The system also updates the database with the latest information about the missing person. The project leverages state-of-the-art machine learning algorithms to extract and compare facial features, it reduces the effort and time required for manual process. This project aims to find missing people faster and better by using facial recognition technology. The main goal is to build a strong system that can study facial details, match them with a database of known people, and help to find missing people quickly and correctly. By harnessing the power of artificial intelligence, this system offers a promising solution to the pressing issue of finding missing individuals, potentially helping to reunite families and improve public safety.

Enhancing Arabic Sign Language Recognition using Deep Learning

The present time, Sign language is very important for people who suffer from hearing loss or who cannot speak. Normal humans tend to disregard the significance of signal language, which is a mere supply of communique to mute and deaf societies. So, this study proposes a developed model for sign Language Recognition for Arabic using the Deep learning Convolutional Neural Network (CNN) algorithm. Then set the algorithm by developing programming on Open-CV, using Python language. The dataset contains 54049 snapshots of Arabic signal language alphabets. The 32 folders were created, and each one of them included 1500 images incorporating hand gestures in at-variance environments. The data set was divided into a training section with a percentage of 70 %, a section for testing with a percentage of 20 %, and a section for validation with a percentage of 10 %. The results show that the suggested model achieved an accuracy rate of 94.8%, and it has proven its effectiveness and success, especially after being tried and tested by several users and obtaining their comments and feedback.