CNN Approach Research Articles

Predicting the Wear Amount of Tire Tread Using 1D−CNN

Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to validate the method. First, driving tests were conducted with tires at various wear levels to measure internal accelerations. The acceleration signals were then screened using empirical functions to exclude atypical data before proceeding with the machine learning process. Finally, a tire wear prediction algorithm based on a 1D−CNN with bottleneck features was developed and evaluated. The developed algorithm showed an RMSE of 5.2% (or 0.42 mm) using only the acceleration signals. When tire pressure and vertical load were included, the prediction error was reduced by 11.5%, resulting in an RMSE of 4.6%. These findings suggest that the 1D−CNN approach is an efficient method for predicting tire wear states, requiring minimal input data. Additionally, it supports the potential usefulness of the intelligent tire technology framework proposed in the modeling study.

Advanced CNN Approach for Segmentation of Diseased Areas in Plant Images

Advanced CNN Approach for Segmentation of Diseased Areas in Plant Images

A unified CNN approach for guided wave-based damage detection, damage size estimation and reliability assessment demonstrated on a complex composite structure

Abstract This work presents a novel unified Convolutional Neural Network approach where broadband ultrasonic guided waves signals are processed in such a way that damage is first detected (binary classification) and then its severity assessed on continuous scale (multi-class classification) without resorting to different procedures. To test the learning approach and assess the classification procedures, a hyperparameter optimization is first carried out to determine the best data processing procedure. Then, the performance of the network is evaluated thoroughly. The results demonstrated the relationship between the model’s performance and SHM system parameters, including excitation signal, pre-processing approach and the number of paths utilized within a sparse distributed transducer network. Furthermore, the damage location is an important influence factor. In addition to that, ensemble voting is demonstrated to be the most accurate approach to achieve high reliability in damage detection and size assessment. The results show the capability of the proposed methodology (i) to detect early damage with highest possible accuracy (ii) to estimate the dimension of damage with limited error and reasonable accuracy, and (iii) to assess the reliability of the whole monitoring system through damage size estimation combined with a critical damage size approach.

Botnet Attack Identification and Mitigation condition Software-Defined Networks Utilizing CNN Algorithm

One new design that makes managing and communicating across large-scale networks easier and more flexible is software-defined networking, or SDN. It allows for the smooth and dynamic execution of complicated network choices via programmable and centralized interfaces. But SDN opens doors for people and companies to tailor network apps to their needs, allowing them to enhance services. On the other hand, it began to encounter a host of new privacy and security issues and brought the dangers of one point of failure all at once. In most cases, hackers use OpenFlow switches to conduct botnets or distributed Denial of Service (DDoS) assaults against the controller. Popular security apps that use deep learning (DL) to quickly identify and counteract attacks are on the rise. Here, we examine botnet-based DDoS attack detection using DL approaches in an SDN-supported context and demonstrate their performance. For the assessment, we utilize a dataset that we just created ourselves. In order to choose the most useful subset of characteristics, we used weighting of features and tuning techniques. Using both a synthetic dataset and actual testbed conditions, we validate the measurements or simulation results. The primary objective of this research is to identify botnet-based DDoS assaults using easily-obtained characteristics and data using a lightweight DL approach with baseline hyper-parameters. We found that the DL technique's performance is affected by the optimal subset of features, and that the accuracy of predictions of the same approach may be varied with a different collection of features. Lastly, our empirical findings show that the CNN approach works better than both the dataset and the actual testbed environments. With CNN, the detection rate for typical flows is 99% and for malicious flows it drops to 97%.

Standalone edge AI-based solution for Tomato diseases detection

Tomato yield is significantly affected by diseases, which are a continuous challenge for its production and pose threats to its global supply chain. Automatic and early detection of these diseases could help growers to swiftly adopt mitigation strategies to limit the disease spread, leading to improved production. Deep learning-based CNN approaches have been widely applied to detect tomato diseases. However, deep learning models are highly computationally demanding, resulting in a computational bottleneck for practical adaptation for agricultural applications such as disease detection and monitoring. Over the last few years, developments of open-source Edge systems have provided opportunities for low-cost and low-power consumption practical solutions for deep learning applications for agriculture. Therefore, the primary goal of this study was to evaluate the performance of standalone Edge-AI solutions for tomato leaf disease detection. To achieve this goal, firstly, this study employed lightweight deep learning networks to detect and differentiate tomato leaf diseases (bacterial spot, early blight, healthy, late blight, leaf mold, septoria leaf spot, two spotted spider mites, target spot, and yellow leaf curl virus). Then, these deep learning networks were deployed on low-cost and low-power consumption Edge devices to investigate their performance capabilities as standalone Edge-AI solutions for the early detection of tomato leaf diseases. Lightweight CNN based GoogleNet and MobileNetV2 deep learning networks achieved accuracies of up to 98.25 % compared to accuracies of 98.13 %, 98.13 %, 94.62 %, and 90.67 % of EfficientNetB0, ResNet-18, SqueezeNet, and NasNetMobile, respectively, in detecting tomato diseases. NVIDIA Jetson ORIN AGX and Nano significantly outperformed Raspberry Pi and Raspberry Pi with AI accelerator (Google Coral) in image classification, achieving classification times of 3.5 ms and 5.2 ms respectively, using SqueezeNet, compared to 15.3 ms and 10.5 ms on the Raspberry Pi devices. In addition, Raspberry Pi with Google Coral achieved the best cost/FPS performance of 0.14 compared to other Edge devices NVIDIA Jetson AGX Orin and NVIDIA Jetson Nano Orin with cost/FPS of 0.7 and 0.26, respectively. These results showed the potential of standalone Edge-AI solutions using low-cost and low-power consuming software and hardware resources for early tomato disease detections.

Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT.

COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19-affected regions in lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation using the newly proposed RESeg segmentation CNN in the second stage. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly COVID-19-infected regions. The evaluation of the proposed Residual-BRNet CNN in the classification stage demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg in the segmentation phase achieves an optimal segmentation performance with an IoU score of 98.43% and a dice similarity score of 95.96% of the lesion region. The framework's effectiveness in detecting and segmenting COVID-19 lesions highlights its potential for clinical applications.

STAB: An Enhanced Abstractive Text Summarization Employing Stacked Bi-GRU with the Attention CNN Approach

STAB: An Enhanced Abstractive Text Summarization Employing Stacked Bi-GRU with the Attention CNN Approach

Advancing brain tumour segmentation: A novel CNN approach with Resnet50 and DrvU-Net: A comparative study

Advancing brain tumour segmentation: A novel CNN approach with Resnet50 and DrvU-Net: A comparative study

Enhancing communication: Deep learning for Arabic sign language translation

Abstract This study explores the field of sign language recognition through machine learning, focusing on the development and comparative evaluation of various algorithms designed to interpret sign language. With the prevalence of hearing impairment affecting millions globally, efficient sign language recognition systems are increasingly critical for enhancing communication for the deaf and hard-of-hearing community. We review several studies, showcasing algorithms with accuracies ranging from 63.5 to 99.6%. Building on these works, we introduce a novel algorithm that has been rigorously tested and has demonstrated a perfect accuracy of 99.7%. Our proposed algorithm utilizes a sophisticated convolutional neural network architecture that outperforms existing models. This work details the methodology of the proposed system, which includes preprocessing, feature extraction, and a multi-layered CNN approach. The remarkable performance of our algorithm sets a new benchmark in the field and suggests significant potential for real-world application in assistive technologies. We conclude by discussing the impact of these findings and propose directions for future research to further improve the accessibility and effectiveness of sign language recognition systems.

Advancing Crowd Object Detection: A Review of YOLO, CNN, and Vision Transformers Hybrid Approach

Advancing Crowd Object Detection: A Review of YOLO, CNN, and Vision Transformers Hybrid Approach

A CNN and BiLSTM Fusion Approach Toward Precise Appliance Energy Forecasts

A CNN and BiLSTM Fusion Approach Toward Precise Appliance Energy Forecasts

Leveraging optimal UAV-lidar features for two CNN approaches to delineate mangrove tree crowns

Effective individual tree crown delineation plays a critical role for assessing mangrove quality and health. However, mangrove tree crowns often form large, interconnected clusters in dense coverage areas, making it difficult to separate them individually. Moreover, many mangrove trees have multiple large branches that result in irregular tree shapes and create internal gaps inside their crowns. Unmanned Aerial Vehicle (UAV) lidar data can potentially overcome these challenges, but a comprehensive assessment of UAV-lidar-derived features is still missing, as well as how to best use them for dense mangrove forests. In this study, we set forth two objectives: (1) to derive optimal features for mangrove individual tree crown delineation through an exclusive examination of all the UAV-lidar features; (2) to develop effective methods that can best incorporate these optimal UAV-lidar features. To achieve the first objective, we extracted 224 features from UAV-lidar data based on three groups of attributes: height, intensity, and point density. Seven spatial scales ranging from 0.2 to 0.5 m were tested in this process. For the second objective, we applied two state-of-the-art Convolutional Neural Networks: Mask Region-based Convolutional Network method (Mask R–CNN) and Ultralytics You Only Look Once version 8 (YOLOv8). At last, the derived three optimal features are: canopy height model at 0.20 m, coefficient of point cloud height variation at 0.25 m, and ground point percentage at 0.25 m. Comparing our methods with traditional methods that only use canopy height model, we found that integrating Convolutional Neural Networks and the optimal UAV-lidar features improved the accuracy by more than 13%. Mask R–CNN was better for dense mangroves, while YOLOv8 was excellent for sparse and short mangroves. The derived optimal UAV-lidar features further enhanced the detection of short trees, densely clumped trees and trees with irregular shapes. To conclude, we developed three novel features based on UAV-lidar data that are especially suitable for individual tree crown delineation in dense mangrove forests. Using these features, we demonstrated that Convolutional Neural Networks can achieve high performance. We hope that our methods will facilitate various mangrove forest studies that rely on accurate individual tree crown delineation.

Integrating Emotion Recognition in IoT-based Cameras: A Deep CNN Approach for Real-time Image Capture

Integrating Emotion Recognition in IoT-based Cameras: A Deep CNN Approach for Real-time Image Capture

Hierarchical Classification of Botnet Using Lightweight CNN

This paper addresses the persistent threat of botnet attacks on IoT devices, emphasizing their continued existence despite various conventional and deep learning methodologies developed for intrusion detection. Utilizing the Bot-IoT dataset, we propose a hierarchical CNN (HCNN) approach featuring three levels of classification. The HCNN approach, presented in this paper, consists of two networks: the non-hierarchical and the hierarchical network. The hierarchical network works by combining features obtained at a higher level with those of its descender. This combined information is subsequently fed into the following level to extract features for the descendant nodes. The overall network consists of 1790 parameters, with the hierarchical network introducing an additional 942 parameters to the existing backbone. The classification levels comprise a binary classification of normal vs attack in the first level, followed by 5 classes in the second level, and 11 classes in the third level. To assess the effectiveness of our proposed approach, we evaluate performance metrics such as Precision (P), Recall (R), F1 Score (F1), and Accuracy (Acc). Rigorous experiments are conducted to compare the performance of both the hierarchical and non-hierarchical models and existing state-of-the-art approaches, providing valuable insights into the efficiency of our proposed hierarchical CNN approach for addressing botnet attacks on IoT devices.

Binary and Ternary Classifiers to Detect COVID-19 Patients Using Chest X-ray Images: An Efficient Layered CNN Approach

Binary and Ternary Classifiers to Detect COVID-19 Patients Using Chest X-ray Images: An Efficient Layered CNN Approach

Deep Learning and Graphical User Interfaces for the Production of Custom Emojis

This study paper digs into the complex domain of human-PC association through looks, investigating different ways to deal with address difficulties in different situations. Presents a novel, right off the bat, multi-characteristic face-to-animation framework for web-based entertainment, creating comparing emoticon in light of facial credits like inclination and glasses. Then handles the impediments of customary CNNs in look acknowledgment by proposing a multi-facet highlight acknowledgment calculation utilizing a three-channel CNN. This calculation separates definite highlights from eyes/eyebrows, mouth, and the whole face, accomplishing higher acknowledgment rates with less organization layers. Perceiving feelings from to some extent concealed faces because of the Coronavirus pandemic is the another concentration, which proposes a SLPPE-based approach that concentrates highlights from the upper face to beat cover impediment. This strategy outflanks normal CNN approaches with great precision on benchmark datasets. A chart convolutional network (GCN)- based calculation for look acknowledgment in the wild is introduced, developing a face diagram in view of key activity units and using a consideration guide to feature essential highlights. This strategy demonstrates viable on both lab-controlled and wild datasets. At last, a two-layered milestone include map (LFM) is proposed for perceiving unobtrusive facial miniature articulations, exhibiting prevalence over customary strategies and demonstrating its freedom of demeanor power and pertinence to true situations. This overview paper exhaustively investigates these imaginative methods, offering significant bits of knowledge into the developing scene of human-PC cooperation through looks.

Genetic structure and landscape effects on gene flow in the Neotropical lizard Norops brasiliensis (Squamata: Dactyloidae)

One key research goal of evolutionary biology is to understand the origin and maintenance of genetic variation. In the Cerrado, the South American savanna located primarily in the Central Brazilian Plateau, many hypotheses have been proposed to explain how landscape features (e.g., geographic distance, river barriers, topographic compartmentalization, and historical climatic fluctuations) have promoted genetic structure by mediating gene flow. Here, we asked whether these landscape features have influenced the genetic structure and differentiation in the lizard species Norops brasiliensis (Squamata: Dactyloidae). To achieve our goal, we used a genetic clustering analysis and estimate an effective migration surface to assess genetic structure in the focal species. Optimized isolation-by-resistance models and a simulation-based approach combined with machine learning (convolutional neural network; CNN) were then used to infer current and historical effects on population genetic structure through 12 unique landscape models. We recovered five geographically distributed populations that are separated by regions of lower-than-expected gene flow. The results of the CNN showed that geographic distance is the sole predictor of genetic variation in N. brasiliensis, and that slope, rivers, and historical climate had no discernible influence on gene flow. Our novel CNN approach was accurate (89.5%) in differentiating each landscape model. CNN and other machine learning approaches are still largely unexplored in landscape genetics studies, representing promising avenues for future research with increasingly accessible genomic datasets.

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer's severity using multitask feature extraction.

Alzheimer's disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer's disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer's disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer's disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.

Ultrasound Image-Based Breast Cancer Detection

Breast cancer is the second most common cause of cancer-related death in women, the definition of breast cancer has drawn attention from the bioinformatics and healthcare communities. Only by removing a sample of breast tissue from the breast, examining, and analyzing it under a microscope would this type of research be possible. In the histopathology lab, issues are found by employing skilled pathologists to examine the specimens; these issues are then further investigated utilizing specialized procedures. However, because they have experience in this field, the ultrasonography may mistakenly reveal abnormal changes or diseases. Recent pattern recognition research has revealed several areas that could be improved, therefore there is now more of an emphasis on building strong image processing experiments to provide a highly-and enhancing current diagnosis. Let's employ deep learning approaches for the image feature and the image feature extraction methodology to detect the disease types of breast tissue using histology and image recognition techniques. Before using ultrasonic feature extraction and the final classification in ultrasound feature extraction, this image can be enlarged utilizing ultrasound processing and CNN approach. Index Terms— Machine learning algorithm, Ultra sound image, MRI.

A Lightweight Channel and Time Attention Enhanced 1D CNN Model for Environmental Sound Classification

One dimension convolutional neural networks (1D CNN) that directly take raw waveforms as input has less competition than 2D CNN recognizing environmental sound. In order to overcome its disadvantages, we propose a novel lightweight 1D CNN structure by employing attention mechanism, which has significant improvement in both accuracy and computational complexity. Concretely, (1) two attention modules are constructed along channel and time dimension separately, and combined to give an intermediate feature map, which focus on key frequency band and semantically related time frame information. (2) Without increasing training overhead, snapshot ensemble is employed to further improve performance. Results from two benchmarking datasets (UrbanSound8k, ESC-10) demonstrated that: by employing attention mechanism, our model outperforms all of the previously reported 1D CNN approaches in accuracy with less parameters. Meanwhile with improved performance gain, the proposed model is superior than most of the existing spectral-based 2D CNN approaches and competitive with SOTA performance, while with orders of magnitude parameters fewer. Overall, it indicates our model is compact and has good potential in practical resource-limited applications, such as sound recognition on embedded platform.