Convolutional Neural Network Model Research Articles

Web-based diagnostic platform for microorganism-induced deterioration on paper-based cultural relics with iterative training from human feedback

PurposePaper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms’ nutrient source. Mold not only can damage papers’ structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper.MethodsMolds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model.Results & ConclusionIn the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation.

Deep Learning Model for Cosmetic Gel Classification Based on a Short-Time Fourier Transform and Spectrogram.

Cosmetics and topical medications, such as gels, foams, creams, and lotions, are viscoelastic substances that are applied to the skin or mucous membranes. The human perception of these materials is complex and involves multiple sensory modalities. Traditional panel-based sensory evaluations have limitations due to individual differences in sensory receptors and factors such as age, race, and gender. Therefore, this study proposes a deep-learning-based method for systematically analyzing and effectively identifying the physical properties of cosmetic gels. Time-series friction signals generated by rubbing the gels were measured. These signals were preprocessed through short-time Fourier transform (STFT) and continuous wavelet transform (CWT), respectively, and the frequency factors that change over time were distinguished and analyzed. The deep learning model employed a ResNet-based convolution neural network (CNN) structure with optimization achieved through a learning rate scheduler. The optimized STFT-based 2D CNN model outperforms the CWT-based 2D and 1D CNN models. The optimized STFT-based 2D CNN model also demonstrated robustness and reliability through k-fold cross-validation. This study suggests the potential for an innovative approach to replace traditional expert panel evaluations and objectively assess the user experience of cosmetics.

Heart Signal Analysis Using Multistage Classification Denoising Model

Cardiovascular disease is a major cause of death worldwide, and the COVID-19 pandemic has only made the situation worse. The purpose of this work is to explore various time-frequency analysis methods that can be used to classify heart sound signals and identify multiple abnormalities in the heart, such as aortic stenosis, mitral stenosis, and mitral valve prolapse. The signal has been modified using three techniques—tunable quality wavelet transform (TQWT), discrete wavelet transform (DWT), and empirical mode decomposition—to detect heart signal abnormality. The proposed model detects heart signal abnormality at two stages, the user end and the clinical end. At the user end, binary classification of signals is performed, and if signals are abnormal then further classification is done at the clinic. The approach starts with signal preprocessing and uses the discrete wavelet transform (DWT) coefficients to train the hybrid model, which consists of one long short-term memory (LSTM) network layer and three convolutional neural network (CNN) layers. This method produced comparable results, with a 98.9% classification accuracy for signals, through the utilization of the CNN and LSTM model. Combining the CNN’s skill in feature extraction with the LSTM’s capacity to record time-dependent features improves the efficacy of the model. Identifying issues early and initiating appropriate medication can alleviate the burden associated with heart valve diseases.

Robust prostate disease classification using transformers with discrete representations.

Automated prostate disease classification on multi-parametric MRI has recently shown promising results with the use of convolutional neural networks (CNNs). The vision transformer (ViT) is a convolutional free architecture which only exploits the self-attention mechanism and has surpassed CNNs in some natural imaging classification tasks. However, these models are not very robust to textural shifts in the input space. In MRI, we often have to deal with textural shift arising from varying acquisition protocols. Here, we focus on the ability of models to generalise well to new magnet strengths for MRI. We propose a new framework to improve the robustness of vision transformer-based models for disease classification by constructing discrete representations of the data using vector quantisation. We sample a subset of the discrete representations to form the input into a transformer-based model. We use cross-attention in our transformer model to combine the discrete representations of T2-weighted and apparent diffusion coefficient (ADC) images. We analyse the robustness of our model by training on a 1.5 T scanner and test on a 3 T scanner and vice versa. Our approach achieves SOTA performance for classification of lesions on prostate MRI and outperforms various other CNN and transformer-based models in terms of robustness to domain shift and perturbations in the input space. We develop a method to improve the robustness of transformer-based disease classification of prostate lesions on MRI using discrete representations of the T2-weighted and ADC images.

Onboard Data Prioritization Using Multi-Class Image Segmentation for Nanosatellites

Nanosatellites are proliferating as low-cost, dedicated remote sensing opportunities for small nations. However, nanosatellites’ performance as remote sensing platforms is impaired by low downlink speeds, which typically range from 1200 to 9600 bps. Additionally, an estimated 67% of downloaded data are unusable for further applications due to excess cloud cover. To alleviate this issue, we propose an image segmentation and prioritization algorithm to classify and segment the contents of captured images onboard the nanosatellite. This algorithm prioritizes images with clear captures of water bodies and vegetated areas with high downlink priority. This in-orbit organization of images will aid ground station operators with downlinking images suitable for further ground-based remote sensing analysis. The proposed algorithm uses Convolutional Neural Network (CNN) models to classify and segment captured image data. In this study, we compare various model architectures and backbone designs for segmentation and assess their performance. The models are trained on a dataset that simulates captured data from nanosatellites and transferred to the satellite hardware to conduct inferences. Ground testing for the satellite has achieved a peak Mean IoU of 75% and an F1 Score of 0.85 for multi-class segmentation. The proposed algorithm is expected to improve data budget downlink efficiency by up to 42% based on validation testing.

Fault Diagnosis Method for Railway Signal Equipment Based on Data Enhancement and an Improved Attention Mechanism

Railway signals’ fault text data contain a substantial amount of expert maintenance experience. Extracting valuable information from these fault text data can enhance the efficiency of fault diagnosis for signal equipment, thereby contributing to the advancement of intelligent railway operations and maintenance technology. Considering that the characteristics of different signal equipment in actual operation can easily lead to a lack of fault data, a fault diagnosis method for railway signal equipment based on data augmentation and an improved attention mechanism (DEIAM) is proposed in this paper. Firstly, the original fault dataset is preprocessed based on data augmentation technology and retained noun and verb operations. Then, the neural network is constructed by integrating a bidirectional long short-term memory (BiLSTM) model with an attention mechanism and a convolutional neural network (CNN) model enhanced with a channel attention mechanism. The DEIAM method can more effectively capture the important text features and sequence features in fault text data, thereby facilitating the diagnosis and classification of such data. Consequently, it enhances onsite fault maintenance experience by providing more precise insights. An empirical study was conducted on a 10-year fault dataset of signal equipment produced by a railway bureau. The experimental results demonstrate that in comparison with the benchmark model, the DEIAM model exhibits enhanced performance in terms of accuracy, precision, recall, and F1.

InViTMixup: plant disease classification using convolutional vision transformer with Mixup augmentation

ABSTRACT Computer vision-based research is carried out to enhance the crop yield with automation solutions. In Tamil Nādu, tomato cultivation is carried out in many districts, including Coimbatore, and its contribution to the Indian economy, i.e. GDP growth, is significant. This paper proposes InViT Mixup, a novel approach toward image classification of diseased tomato leaves. The effectiveness of convolutional neural network (CNN) in obtaining spatial information and the attention mechanism of vision transformers, along with Mixup data augmentation, are combined, and the model’s generalization in predicting the diseased tomato based on leaf symptoms is evaluated. The proposed convolutional transformer model improvised the classification results of tomato disease in terms of accuracy and training speed compared to pure CNN or transformer-based models. In the proposed work, 10 tomato leaf disease classes from the PlantVillage dataset are used. Top-1 accuracy of 93.5% and Top-5 accuracy of 99.7% are achieved.

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems. However, a non-negligible limitation of machine learning methods used to train decision models is that adversarial attacks can easily fool them. Adversarial attacks are attack samples produced by carefully manipulating the samples at the test time to violate the model integrity by causing detection mistakes. In this paper, we analyse the performance of five realistic target-based adversarial attacks, namely Extend, Full DOS, Shift, FGSM padding + slack and GAMMA, against two machine learning models, namely MalConv and LGBM, learned to recognise Windows Portable Executable (PE) malware files. Specifically, MalConv is a Convolutional Neural Network (CNN) model learned from the raw bytes of Windows PE files. LGBM is a Gradient-Boosted Decision Tree model that is learned from features extracted through the static analysis of Windows PE files. Notably, the attack methods and machine learning models considered in this study are state-of-the-art methods broadly used in the machine learning literature for Windows PE malware detection tasks. In addition, we explore the effect of accounting for adversarial attacks on securing machine learning models through the adversarial training strategy. Therefore, the main contributions of this article are as follows: (1) We extend existing machine learning studies that commonly consider small datasets to explore the evasion ability of state-of-the-art Windows PE attack methods by increasing the size of the evaluation dataset. (2) To the best of our knowledge, we are the first to carry out an exploratory study to explain how the considered adversarial attack methods change Windows PE malware to fool an effective decision model. (3) We explore the performance of the adversarial training strategy as a means to secure effective decision models against adversarial Windows PE malware files generated with the considered attack methods. Hence, the study explains how GAMMA can actually be considered the most effective evasion method for the performed comparative analysis. On the other hand, the study shows that the adversarial training strategy can actually help in recognising adversarial PE malware generated with GAMMA by also explaining how it changes model decisions.

FOOD CLASSIFICATION AND CALORIE ESTIMATION USING CNN

This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

SURVEY ON FOOD CLASSIFICATION AND CALORIE ESTIMATION USING CNN

This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and Mask R-CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

Earthquake damage detection and level classification method for wooden houses based on convolutional neural networks and onsite photos

AbstractThe results of earthquake damage certification (EDC) surveys are the basis of support measures for improving the lives of disaster victims. To address issues such as a limited workforce to perform EDC surveys and difficulties in judging the level of damage, a damage detection and level classification method for wooden houses using multiple convolutional neural network models is proposed. The proposed method, including detection, filtering, and classification models, was trained and validated based on photographs collected from EDC surveys in Uki City, Kumamoto Prefecture. Then, a software system, which deployed these models, was developed for the onsite EDC surveyors to detect damages shown in the photographs of the surveyed house and classify damage levels. The test results based on 32 target buildings indicate that the detection model achieved high recall in detecting damage. Moreover, the redundant detected regions can be precisely filtered by the filtering model. Finally, the classification model achieved relatively high overall accuracy in classifying the damage level.

Artificial Intelligence in enhancing sustainable practices for infectious municipal waste classification

This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environmental risks. Leveraging advanced artificial intelligence techniques, we prioritize infectious waste categorization and optimization, integrating metaheuristics into optimization methods to create a robust dual-ensemble framework. Our model, the “Enhanced Artificial Intelligence for Infectious Municipal Waste Classification System,” combines ensemble image segmentation methods and diverse convolutional neural network models. Innovative geometric image augmentation enhances model robustness, diversifies training data, and improves accuracy across waste types. A pivotal aspect is the integration of a reinforcement learning-differential evolution algorithm as a decision fusion strategy, optimizing classification by harmonizing outputs from ensemble methods and convolutional neural network models. Computational results, using a newly collected dataset, demonstrate our model’s accuracy exceeding 96.54% while using the existing solid waste dataset we achieve the accuracy of 97.81%, outperforming advanced approaches by margins ranging from 2.02% to 8.80%. This research significantly advances sustainable waste management, showcasing artificial intelligence’s transformative potential in addressing intricate waste challenges. It establishes a foundational framework prioritizing efficiency, effectiveness, and sustainability for future waste management solutions. Acknowledging the importance of diverse datasets, customization for urban contexts, and practical integration into existing infrastructures, our work contributes to the broader discourse on the role of artificial intelligence in evolving waste management practices.

OPTIMIZATION OF CNN + MOBILENETV3 FOR INSECT IDENTIFICATION: TOWARD HIGH ACCURACY

Developments in the field of artificial intelligence and deep learning, particularly Convolutional Neural Networks (CNN) techniques, have expanded the research potential and applications in ecology, including efficient and accurate insect classification. However, there are challenges in achieving high levels of accuracy with similar computational efficiency. In response, the efficient MobileNetV3 architecture was investigated to improve the insect pest classification process. Through an analytical descriptive quantitative approach and insect datasets from Kaggle, this study tested the effectiveness of CNN models optimized with MobileNetV3. The results indicated that the optimized model achieved classification accuracy of up to 90%, with consistent performance between training and validation data and significant loss reduction. With high precision and processing efficiency, this discovery makes a substantial contribution to deep learning applications in the field of intelligent agriculture, promising methodological improvements for other classification problems. Despite offering a promising solution, this study recognizes the limitation in dataset diversity and suggests further exploration with more varied datasets to strengthen the model's application in actual agricultural practices.

DEPLOYMENT OF ENHANCED DEEP LEARNING MODEL WITH THE BEST ESTIMATORS ON OPTIMIZERS AND ACTIVATION FUNCTIONS FOR HEALTHCARE IN WEB APPLICATION

Breast cancer and brain tumor stand as leading global causes of mortality. Brain tumor uses Magnetic Resonance Imaging (MRI) which offers superior clarity in visualizing brain structures compared to other imaging modalities, while Breast cancer uses ultrasonography (US) serves as a common tool for detecting breast cancer despite its inherent limitations in image quality. Motion artifacts frequently hinder MRI scans, necessitating skilled radiologists for accurate interpretation. Computer-aided diagnosis (CAD) systems driven by artificial intelligence, present a promising solution by consistently assisting radiologists in analyzing US images. Convolutional neural networks (CNNs) leverage various optimizers like Adam and Stochastic Gradient Descent (SGD), RMSprop, Adagrad, and Adadelta as well as activation functions including PReLu, LeakyReLu, Elu, and ReLu for their construction and training. The comparative analysis highlights the importance of optimizers and activation functions in deep learning algorithms for predicting brain tumors and breast cancer. The Adam optimizer combined with the ReLU activation function achieved an accuracy of 85% for breast cancer prediction, while RMSprop combined with ReLU activation function achieved a higher accuracy of 93% for brain tumor classification. From this research, considerable deep learning configurations are identified for both breast cancer and brain tumor prediction, facilitating more precise and efficient diagnoses. The comparative analysis provides valuable insights for those involved in medical imaging applications. Furthermore, The CNN model is deployed in web interface using flask framework to streamline the integration of these models into healthcare systems. This interface simplifies the input of medical data including image data and provides real-time predictions. KEYWORDS—Convolutional neural networks, Comparative Analysis, Activation functions, Optimizers, Ultrasound images, MRI images, Breast cancer, Brain tumor, medical imaging applications, FLASK.

TEDNet: Twin Encoder Decoder Neural Network for 2D Camera and LiDAR Road Detection

Abstract Robust road surface estimation is required for autonomous ground vehicles to navigate safely. Despite it becoming one of the main targets for autonomous mobility researchers in recent years, it is still an open problem in which cameras and LiDAR sensors have demonstrated to be adequate to predict the position, size and shape of the road a vehicle is driving on in different environments. In this work, a novel Convolutional Neural Network model is proposed for the accurate estimation of the roadway surface. Furthermore, an ablation study has been conducted to investigate how different encoding strategies affect model performance, testing 6 slightly different neural network architectures. Our model is based on the use of a Twin Encoder–Decoder Neural Network (TEDNet) for independent camera and LiDAR feature extraction and has been trained and evaluated on the Kitti–Road dataset. Bird’s Eye View projections of the camera and LiDAR data are used in this model to perform semantic segmentation on whether each pixel belongs to the road surface. The proposed method performs among other state-of-the-art methods and operates at the same frame rate as the LiDAR and cameras, so it is adequate for its use in real-time applications.

Automatic classification and segmentation of blast cells using deep transfer learning and active contours.

Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, although widely practiced, suffers from significant limitations including labor-intensity and susceptibility to human error, particularly in distinguishing the subtle differences between normal and leukemic cells. To overcome these limitations, our research introduces the ALLDet classifier, an innovative tool employing deep transfer learning for the automated analysis and categorization of ALL from White Blood Cell (WBC) nuclei images. Our investigation encompassed the evaluation of nine state-of-the-art pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, ResNet50, ResNet101, DenseNet121, DenseNet201, Xception, MobileNet, and EfficientNetB3. We augmented this approach by incorporating a sophisticated contour-based segmentation technique, derived from the Chan-Vese model, aimed at the meticulous segmentation of blast cell nuclei in blood smear images, thereby enhancing the accuracy of our analysis. The empirical assessment of these methodologies underscored the superior performance of the EfficientNetB3 model, which demonstrated exceptional metrics: a recall specificity of 98.5%, precision of 95.86%, F1-score of 97.16%, and an overall accuracy rate of 97.13%. The Chan-Vese model's adaptability to the irregular shapes of blast cells and its noise-resistant segmentation capability were key to capturing the complex morphological changes essential for accurate segmentation. The combined application of the ALLDet classifier, powered by EfficientNetB3, with our advanced segmentation approach, emerges as a formidable advancement in the early detection and accurate diagnosis of ALL. This breakthrough not only signifies a pivotal leap in leukemia diagnostic methodologies but also holds the promise of significantly elevating the standards of patient care through the provision of timely and precise diagnoses. The implications of this study extend beyond immediate clinical utility, paving the way for future research to further refine and enhance the capabilities of artificial intelligence in medical diagnostics.

Deep learning-based auditory attention decoding in listeners with hearing impairment.

This study develops a deep learning method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment. It addresses three classification tasks: differentiating noise from speech-in-noise, classifying the direction of attended speech (left vs. right) and identifying the activation status of hearing aid noise reduction (NR) algorithms (OFF vs. ON). These tasks contribute to our understanding of how hearing technology influences auditory processing in the hearing-impaired population.
Method: Deep convolutional neural network (DCNN) models were designed for each task. Two training strategies were employed to clarify the impact of data splitting on AAD tasks: inter-trial, where the testing set used classification windows from trials that the training set hadn't seen, and intra-trial, where the testing set used unseen classification windows from trials where other segments were seen during training. The models were evaluated on EEG data from 31 participants with hearing impairment, listening to competing talkers amidst background noise.
Results: Using 1-second classification windows, DCNN models achieve accuracy (ACC) of 69.8\%, 73.3\% and 82.9\% and area-under-curve (AUC) of 77.2\%, 80.6\% and 92.1\% for the three tasks respectively on inter-trial strategy. In the intra-trial strategy, they achieved ACC of 87.9\%, 80.1\% and 97.5\%, along with AUC of 94.6\%, 89.1\%, and 99.8\%. Our DCNN models show good performance on short 1-second EEG samples, making them suitable for real-world applications.
Conclusion: Our DCNN models successfully addressed three tasks with short 1-second EEG windows from participants with hearing impairment, showcasing their potential. While the inter-trial strategy demonstrated promise for assessing AAD, the intra-trial approach yielded inflated results, underscoring the important role of proper data splitting in EEG-based AAD tasks.
Significance: Our findings showcase the promising potential of EEG-based tools for assessing auditory attention in clinical contexts and advancing hearing technology, while also promoting further exploration of alternative deep learning architectures and their potential constraints.

Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning

The analysis of body motion is a valuable tool in the assessment and diagnosis of gait impairments, particularly those related to neurological disorders. In this study, we propose a novel automated system leveraging artificial intelligence for efficiently analyzing gait impairment from video-recorded images. The proposed methodology encompasses three key aspects. First, we generate a novel one-dimensional representation of each silhouette image, termed a silhouette sinogram, by computing the distance and angle between the centroid and each detected boundary points. This process enables us to effectively utilize relative variations in motion at different angles to detect gait patterns. Second, a one-dimensional convolutional neural network (1D CNN) model is developed and trained by incorporating the consecutive silhouette sinogram signals of silhouette frames to capture spatiotemporal information via assisted knowledge learning. This process allows the network to capture a broader context and temporal dependencies within the gait cycle, enabling a more accurate diagnosis of gait abnormalities. This study conducts training and an evaluation utilizing the publicly accessible INIT GAIT database. Finally, two evaluation schemes are employed: one leveraging individual silhouette frames and the other operating at the subject level, utilizing a majority voting technique. The outcomes of the proposed method showed superior enhancements in gait impairment recognition, with overall F1-scores of 100%, 90.62%, and 77.32% when evaluated based on sinogram signals, and 100%, 100%, and 83.33% when evaluated based on the subject level, for cases involving two, four, and six gait abnormalities, respectively. In conclusion, by comparing the observed locomotor function to a conventional gait pattern often seen in healthy individuals, the recommended approach allows for a quantitative and non-invasive evaluation of locomotion.

Deep learning algorithm development for early detection of Botrytis cinerea infected strawberry fruit using hyperspectral fluorescence imaging

Botrytis cinerea is a strawberry disease that causes economic loss worldwide. If a disease outbreak occurs during storage or transportation, it can spread rapidly to neighboring objects; thus, there is a need to develop early diagnostic techniques to prevent it. In this study, we developed a method to rapidly and nondestructively determine the infection stage in strawberry fruit using hyperspectral fluorescence imaging. ‘Keumsil’ cultivar strawberries were used, and hyperspectral fluorescence images were acquired over 144 h in control and inoculation groups. Strawberries were categorized into four infection stages based on visible mold spores: healthy, asymptomatic, infected, and after-infected. Hyperspectral fluorescence spectra were extracted to develop a one-dimensional convolutional neural network (1D-CNN) model based on partial least squares-discriminant analysis (PLS-DA), VGG-19, and ResNet-50; data augmentation techniques and six spectral preprocessing techniques were applied to the datasets. The application of data augmentation techniques improved the performances of the PLS-DA and 1D-CNN models in determining the infection stage. The performance of the ResNet-50-based 1D-CNN model with mean normalization data and data augmentation technique was the best, with 96.88% precision, 96.87% recall, 96.85% F1-score, and 96.86% accuracy. The results of this study showed that it is possible to determine the infection stage of Botrytis cinerea on strawberry fruit using hyperspectral fluorescence imaging and 1D-CNN techniques. This technology is expected to be applied for the early detection of Botrytis cinerea in strawberry growth, postharvest sorting and packing, and distribution stages.

Lightweight Convolutional Network with Integrated Attention Mechanism for Missing Bolt Detection in Railways

Railway infrastructure safety is a paramount concern, with bolt integrity being a critical component. In the realm of railway maintenance, the detection of missing bolts is a vital task that ensures the stability and safety of tracks. Traditionally, this task has been approached through manual inspections or conventional automated methods, which are often time-consuming, costly, and prone to human error. Addressing these challenges, this paper presents a state-of-the-art solution with the development of a lightweight convolutional neural network (CNN) featuring an integrated attention mechanism. This novel model is engineered to be computationally efficient while maintaining high accuracy, making it particularly suitable for real-time analysis in resource-constrained environments commonly found in railway inspections. The proposed CNN utilises a distinctive architecture that synergises the speed of lightweight networks with the precision of attention-based mechanisms. By integrating an attention mechanism, the network selectively concentrates on regions of interest within the image, effectively enhancing the model’s capability to identify missing bolts with remarkable accuracy. Comprehensive testing showcases a remarkable 96.43% accuracy and an impressive 96 F1-score, substantially outperforming existing deep learning frameworks in the context of missing bolt detection. Key contributions of this research include the model’s innovative attention-integrated approach, which significantly reduces the model complexity without compromising detection performance. Additionally, the model offers scalability and adaptability to various railway settings, proving its efficacy not just in controlled environments but also in diverse real-world scenarios. Extensive experiments, rigorous evaluations, and real-time deployment results collectively underscore the transformative potential of the presented CNN model in advancing the domain of railway safety maintenance.