Raw Images Research Articles

Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage (ICH) in real-world settings. ML-based models were developed to predict in-hospital mortality in 527 patients with ICH using raw brain imaging data from brain computed tomography and clinical data. The models' performances were evaluated using the area under the receiver operating characteristic curves and calibration plots, comparing them with traditional risk scores such as the ICH score and ICH grading scale. Kaplan-Meier curves were used to examine the post-ICH survival rates, stratified by ML-based risk assessment. The net benefit of ML-based models was evaluated using decision curve analysis. The area under the receiver operating characteristic curves were 0.91 (95% CI, 0.86-0.95) for the ICH score, 0.93 (95% CI, 0.89-0.97) for the ICH grading scale, 0.83 (95% CI, 0.71-0.91) for the ML-based model fitted with raw image data only, and 0.87 (95% CI, 0.76-0.93) for the ML-based model fitted using clinical data without specialist expertise. The area under the receiver operating characteristic curve increased significantly to 0.97 (95% CI, 0.94-0.99) when the ML model was fitted using clinical and image data assessed by specialists. All ML-based models demonstrated good calibration, and the survival rates showed significant differences between risk groups. Decision curve analysis indicated the highest net benefit when utilizing the findings assessed by specialists. ML-based prediction models exhibit satisfactory performance in predicting post-ICH in-hospital mortality when utilizing raw imaging data or nonspecialist input. Nevertheless, incorporating specialist expertise notably improves performance.

ChromTR: chromosome detection in raw metaphase cell images via deformable transformers.

Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases, of which chromosome detection in raw metaphase cell images is the most critical and challenging step. In this work, focusing on the joint optimization of chromosome localization and classification, we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell images. ChromTR incorporates semantic feature learning and class distribution learning into a unified DETR-based detection framework. Specifically, we first propose a Semantic Feature Learning Network (SFLN) for semantic feature extraction and chromosome foreground region segmentation with object-wise supervision. Next, we construct a Semantic-Aware Transformer (SAT) with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic features. To provide a prediction with a precise chromosome number and category distribution, a Category Distribution Reasoning Module (CDRM) is built for foreground-background objects and chromosome class distribution reasoning. We evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset AutoKary2022. Our proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56% in R-band chromosome detection, surpassing the baseline method by 3.02%. In a clinical test, ChromTR is also confident in tackling normal and numerically abnormal karyotypes. When extended to the chromosome enumeration task, ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image datasets. Given these superior performances to other methods, our proposed method has been applied to assist clinical karyotype diagnosis.

Designing a novel framework of email spam detection using an improved heuristic algorithm and dual-scale feature fusion-based adaptive convolution neural network

ABSTRACT Nowadays, e-mail is becoming more prevalent among individuals. In recent days, it has been declared to be the least expensive and quickest mode of communicating. In recent years, e-mail spam has become a big problem, so the number of e-mail spam has also increased, and they are used for unethical and illegal conduct, scams, and theft. As a result, the main objective of the study is to detect e-mail spam using an adaptive deep-learning model with feature fusion. Initially, the input image and text are collected from benchmark datasets. Further, the raw text undergoes the pre-processing stage, and it is then followed by extracting the features using Bidirectional Encoder Representations from Transformers (BERT). Similarly, the Vision Transformer (ViT) is employed for extracting the features from raw images. Finally, these features are given in Dual Scale Feature Fusion-based Adaptive Convolutional Neural Network (DFF-ACNNet), and it is then fused for performing the classification in Convolutional Neural Network (CNN). Here, the hyper-parameters are tuned by using Modified Squid Game Optimizer (MSGO). Finally, the evaluation is done with various metrics to estimate the performance of the model and the accuracy of the developed system is 98.46, which is higher than the other conventional approaches.

An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection

Safety is crucial in the railway industry because railways transport millions of passengers and employees daily, making it paramount to prevent injuries and fatalities. In order to guarantee passenger safety, computer vision, unmanned aerial vehicles (UAV), and artificial intelligence will be essential tools in the near future for routinely evaluating the railway environment. An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection (UAV-RSOD) comprises high-resolution images captured by UAVs over various obstacles within railroad scenes, enabling automatic railroad extraction and obstacle detection. The dataset includes 315 raw images, along with 630 labeled and 630 masked images for railroad semantic segmentation. The dataset consists of 315 original images captured by the UAV for object detection and obstacle detection. To increase dataset diversity for training purposes, we applied data augmentation techniques, which expanded the dataset to 2002 augmented and annotated images for obstacle detection cover six different classes of obstacles on railroad lines. Additionally, we provide the original 315 images along with a script for augmentation, allowing users to generate their own augmented data as needed, offering a more sustainable and customizable option. Each image in the dataset is accurately annotated with bounding boxes and labeled under six categories, including person, boulder, barrel, branch, jerry can, and iron rod. This comprehensive classification and detailed annotation make the dataset an essential tool for researchers and developers working on computer vision applications in the railroad domain.

RETRACTION: High-density lipoprotein of patients with type 2 diabetes mellitus elevates the capability of promoting migration and invasion of breast cancer cells.

Retraction: B. Pan , H. Ren , Y. Ma , D. Liu , B. Yu , L. Ji , L. Pan , J. Li , L. Yang , X. Lv , X. Shen , B. Chen , Y. Zhang , B. Willard , Y. He , and L. Zheng , "High-Density Lipoprotein of Patients with Type 2 Diabetes Mellitus Elevates the Capability of Promoting Migration and Invasion of Breast Cancer Cells," International Journal of Cancer 131, no. 1 (2012): 70-82. https://doi.org/10.1002/ijc.26341. The above article, published online on 29 April 2011, in Wiley Online Library (wileyonlinelibrary.com), and has been retracted by agreement between the journal Editor-in-Chief, Prof. Christoph Plass; the Union for International Cancer Control; and John Wiley and Sons Ltd. A third party reported that they had detected image similarities between figures 3B and 3F. The publisher requested original raw data and images for all Western Blot figures included in the published article, and in response the authors shared what they reported as raw images. A detailed investigation by the editors revealed additional irregularities in the raw data, including the discovery that the data for the Akt and p-Akt bands in Figure 5A had been manipulated between the original images and those presented in the published article. The retraction has been agreed to as the results presented in the article can no longer be considered reliable. The authors disagree with the retraction.

MULTIMODAL BIOMETRIC ENROLMENT AND AUTHENTICATION SYSTEM (MBEAS) WITH MODIFIED SCORE-LEVEL FUSION AND TRIBLENDNN-BASED TEMPLATE MATCHING

This article proposed a multimodal biometric enrolment and authentication system (MBEAS) with modified score-level fusion and TriBlendNN-based template matching approach. It consists of four phases: (i) enrolment phase, (ii) security phase, (iii) storage phase, and (iv) authentication phase. Initially, the raw data of the iris, face, hand, speech, signature, handwriting, fingerprint, and keystroking are collected from the BiosecurID database, and then the raw images are pre-processed via resizing and cropping. Raw signal of speech is pre-processed via wavelet denoising and spectral subtraction. The raw data of key stroking is pre-processed via Z-score normalization. Then the pre-processed images of iris, face, signature, hand, handwriting, and fingerprints are segmented via optimized watershed segmentation. The pre-processed speech signal is segmented via VAD. From the segmented data, the optimal features are extracted for iris (LBP), face (IncepV3), signature and handwriting (shape features and GLCM), speech (MFCC), fingerprints (minutiae extraction), hand (palm print features), and keystroking. From the feature-extracted data, the feature fusion is then performed via modified score-level fusion. After the enrolment phase, the feature fused data is secured using watermarking. Then the watermarked data is stored in cloud storage. The final stage is the authentication stage, wherein the template matching is processed via the newly proposed TriBlendNN model. The proposed TriBlendNN model is a combination of the CNN, RNN, and Bi-LSTM. The final outcome comes from template matching. The proposed model is implemented in Python and its accuracy at learning rates of 70% and 80% are 96.89% and 97.76%, respectively.

Classification of Pneumonia, Tuberculosis and Covid-19 from Chest X-Ray Images Using Convolution Neural Network Model

Accurate and timely diagnosis of respiratory ailments like pneumonia, tuberculosis (TB), and COVID-19 is pivotal for effective patient care and public health interventions. Deep learning algorithms have emerged as potent tools in medical image classification, offering promise for automated diagnosis and screening. This study presents a deep learning-based approach for categorizing chest X-ray images into three classes: pneumonia, tuberculosis, and COVID-19. Utilizing convolutional neural networks (CNNs) as the primary architecture, owing to their ability to automatically extract relevant features from raw image data. The proposed model is trained on a sizable dataset of chest X-ray images annotated with ground truth labels for pneumonia, TB, and COVID-19. Extensive experiments are conducted to evaluate the model's performance in terms of classification accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Furthermore, we compare the performance of our deep learning model with traditional machine learning approaches and assess its generalization ability on an independent test set. Our findings demonstrate that the proposed deep learning model achieves high accuracy in classifying chest X-ray images of pneumonia, TB, and COVID-19, outperforming traditional methods and showing potential for clinical deployment as a screening tool, especially in resource-limited settings.

OmniSegger: A time-lapse image analysis pipeline for bacterial cells.

Time-lapse microscopy is a powerful tool for studying the cell biology of bacterial cells. The development of pipelines that facilitate the automated analysis of these datasets is a long-standing goal of the field. In this paper, we describe OmniSegger , an updated version of our SuperSegger pipeline, developed as an open-source, modular, and holistic suite of algorithms whose input is raw microscopy images and whose output is a wide range of quantitative cellular analyses, including dynamical cell cytometry data and cellular visualizations. The updated version described in this paper introduces two principal refinements: (i) robustness to cell morphologies and (ii) support for a range of common imaging modalities. To demonstrate robustness to cell morphology, we present an analysis of the proliferation dynamics of Escherichia coli treated with a drug that induces filamentation. To demonstrate extended support for new image modalities, we analyze cells imaged by five distinct modalities: phase-contrast, two brightfield modalities, and cytoplasmic and membrane fluorescence. Together, this pipeline should greatly increase the scope of tractable analyses for bacterial microscopists.

Estimation of the Proportion of Vulnerable Households and Its Determinants in Lampung Province in 2022

Aims: The purpose of this research to determine the profile of poverty vulnerability in Lampung Province and the factors that influence it. This is one of the efforts to achieve the ultimate goal of determining targeted policies as a preventive effort to prevent poverty from occurring in vulnerable households in Lampung Province in 2022. Study Design: Carrying out poverty grouping then carries out testing to determine poverty vulnerability. Place and Duration of Study: The data sources used in this study are secondary data in the form of raw data from the National Socio-Economic Survey (SUSENAS) KOR and Module for March 2022. In addition, this study also uses various other data sourced from the BPS Lampung Website and the Lampung Province Publication in Figures 2022. Methodology: The first step is to estimate poverty vulnerability using the Vulnerability as Expected Poverty (VEP) method. The study continued by analyzing the determinants of poverty vulnerability. The analysis of vulnerability determinants was carried out using the logit regression method. Results: Analysis of the level of vulnerability to poverty in the Lampung region shows that areas with high vulnerability, such as East Lampung, Tanggamus, and South Lampung, are influenced by various structural factors such as limited access to infrastructure and public services, minimal economic opportunities, and dependence on the agricultural sector. These factors are exacerbated by low levels of education and limited access to credit, which further worsen the economic conditions of the community. The number of household members, age of the head of household, education, gender, regional classification, access to credit, savings, employment status, and non-cash food assistance all contribute to the level of vulnerability; increasing the number of household members and the age of the head of household increase the likelihood of vulnerability, while higher education is also associated with increased risk. Male heads of households and urban areas are less likely to experience poverty vulnerability, while access to credit, savings, employment status, and non-cash food assistance can reduce the risk of poverty. Conclusion: The government needs to prioritize infrastructure development in vulnerable areas, such as East Lampung, Tanggamus, and South Lampung, to improve connectivity and public accessibility to basic services, in line with the Lampung Provincial Government's program to improve production efficiency and regional connectivity.

Enhancing Facial Recognition: A Comprehensive Review of Deep Learning Approaches and Future Perspectives

This paper presents a comprehensive review of the application of deep learning in facial recognition, covering fundamental aspects from neural network architectures to advanced training methods. It starts with an introduction to the basic architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), including variations like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. The review extends to sophisticated architectures like AlexNet, GoogleNet, VGGNet, and ResNet, which have significantly pushed the boundaries of accuracy in face recognition tasks. The paper details the process of data preprocessing in face recognition, which involves critical steps such as face detection, alignment, and normalization to ensure uniformity in the input data, enhancing the accuracy of feature extraction. Various feature extraction methods are discussed, including CNN-based and Generative Adversarial Network (GAN)-based techniques, which have shown considerable promise in deriving complex facial features from raw images. Loss functions such as Euclidean distance loss, angular/cosine margin loss, and softmax loss variants are explored to understand their impact on enhancing the discriminative power of the facial recognition systems. The paper highlights the evolution from traditional models using eigenfaces and feature descriptors like Local Binary Patterns (LBP) to cutting-edge deep learning models that utilize deep identity features (DeepID) and triplet loss functions to improve recognition accuracy.

An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography

ObjectiveThis study aimed to design and systematically evaluate an architecture, proposed as the Explainable Mandibular Third Molar Convolutional Neural Network (E-mTMCNN), for detecting the presence of mandibular third molars (m-M3) in panoramic radiography (PR). The proposed architecture seeks to enhance the accuracy of early detection and improve clinical decision-making and treatment planning in dentistry. MethodsA new dataset, named the Mandibular Third Molar (m-TM) dataset, was developed through expert labeling of raw PR images from the UESB dataset. This dataset was subsequently made publicly accessible to support further research. Several advanced image preprocessing techniques, including Gaussian filtering, gamma correction, and data augmentation, were applied to improve image quality. Various Deep learning (DL) based Convolutional Neural Network (CNN) architectures were trained and validated using Transfer Learning (TL) methodologies. Among these, the E-mTMCNN, leveraging the GoogLeNet architecture, achieved the highest performance metrics. To ensure transparency in the model’s decision-making process, Local Interpretable Model-Agnostic Explanations (LIME) were integrated as an eXplainable Artificial Intelligence (XAI) approach. Clinical reliability and applicability were assessed through an expert survey conducted among specialized dentists using a decision support system based on the E-mTMCNN. ResultsThe E-mTMCNN architecture demonstrated a classification accuracy of 87.02%, with a sensitivity of 75%, specificity of 94.73%, precision of 77.68%, an F1 score of 75.51%, and an area under the curve (AUC) of 87.01%. The integration of LIME provided visual explanations of the model’s decision-making rationale, reinforcing the robustness of the proposed architecture. Results from the expert survey indicated high clinical acceptance and confidence in the reliability of the system. ConclusionThe findings demonstrate that the E-mTMCNN architecture effectively detects the presence of m-M3 in PRs, outperforming current state-of-the-art methodologies. The proposed architecture shows considerable potential for integration into computer-aided diagnostic systems, advancing early detection capabilities and enhancing the precision of treatment planning in dental practice.

Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images

Viruses are submicroscopic agents that can infect other lifeforms and use their hosts’ cells to replicate themselves. Despite having simplistic genetic structures among all living beings, viruses are highly adaptable, resilient, and capable of causing severe complications in their hosts’ bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses pose the biggest biological threat both animal and plant species face. It is often challenging to promptly detect a virus in a host and accurately determine its type using manual examination techniques. However, computer-based automatic diagnosis methods, especially the ones using Transmission Electron Microscopy (TEM) images, have proven effective in instant virus identification. Using TEM images collected from a recent dataset, this article proposes a deep learning-based classification model to identify the virus type within those images. The methodology of this study includes two coherent image processing techniques to reduce the noise present in raw microscopy images and a functional Convolutional Neural Network (CNN) model for classification. Experimental results show that it can differentiate among 14 types of viruses with a maximum of 97.44% classification accuracy and F1-score, which asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart a fast and dependable virus identification scheme subsidiary to the thorough diagnostic procedures.

Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence

Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts early-stage ROP from neonatal fundus images using Machine Learning (ML) classifiers and Convolutional Neural Networks (CNN) based pre-trained networks. The characteristic demarcation lines and ridges in early stage ROP are segmented utilising a novel Swin U-Net. 2000 Scale Invariant Feature Transform (SIFT) descriptors were extracted from the segmented ridges and are dimensionally reduced to 50 features using Principal Component Analysis (PCA). Seven ROP-specific features, including six Gray Level Co-occurrence Matrix (GLCM) and ridge length features, are extracted from the segmented image and are fused with the PCA reduced 50 SIFT features. Finally, three ML classifiers, such as Support Vector Machine (SVM), Random Forest (RF), andk- Nearest Neighbor (k-NN), are used to classify the 50 features to predict the early-stage ROP from Normal images. On the other hand, the raw retinal images are classified directly into normal and early-stage ROP using six pre-trained classifiers, namely ResNet50, ShuffleNet V2, EfficientNet, MobileNet, VGG16, and DarkNet19. It is seen that the ResNet50 network outperformed all other networks in predicting early-stage ROP with 89.5% accuracy, 87.5% sensitivity, 91.5% specificity, 91.1% precision, 88% NPV and an Area Under the Curve (AUC) of 0.92. Swin U-Net Convolutional Neural Networks (CNN) segmented the ridges and demarcation lines with an accuracy of 89.7% with 80.5% precision, 92.6% recall, 75.76% IoU, and 0.86 as the Dice coefficient. The SVM classifier using the 57 features from the segmented images achieved a classification accuracy of 88.75%, sensitivity of 90%, specificity of 87.5%, and an AUC of 0.91. The system can be utilised as a point-of-care diagnostic tool for ROP diagnosis of neonates in remote areas.

Comparative Analysis of Power Consumption and Resource Utilization in Open-Source and Proprietary Media Players while using Raw Videos

This study evaluates and compares the power consumption and resource utilization of open-source and proprietary media players during the playback of a large raw video file. Using real-time monitoring tools like HWiNFO, key metrics such as GPU power consumption, CPU power consumption, memory usage, and CPU usage percentage were collected and analyzed. The experiment was conducted on a system powered by a 12th Gen Intel(R) Core(TM) i7-12700H processor, and the media players were tested with a 2-minute, 14-second raw video file in .MOV format. A statistical analysis using t-tests was performed to assess the significance of the differences between the two categories. The results indicated that open-source media players generally exhibit lower GPU and CPU power consumption, with a potential for saving energy. Long-term power consumption analysis further demonstrated that users could achieve significant energy savings by opting for open-source media players, making them more suitable for energy-conscious environments. These findings highlight the trade-offs between power efficiency and performance while playing raw videos.

Chest computed tomography for patients with sepsis in the emergency intensive care unit

Sepsis is a systemic inflammatory response syndrome (SIRS) caused by infection, which may lead to multiple organ dysfunction in susceptible patient. The most frequently involved organs/systems include the lung, kidney and circulation system. It is well established that sepsis is a risk factor for acute lung injury. While overt pulmonary infiltrates can be well captured by human operators, subtle structural changes of the lung might be ignored. Since the advantage of chest computed tomography (CT) is its capability of providing fine structural changes in high spatial resolution, the study of chest CT by means of computer science may provide further insights into the underlying pathophysiology. The integration of chest CT into the study of sepsis is limited partly due to the lack of well-curated database. The study aims to establish a database comprising detailed clinical tabular data, as well as the raw chest CT images. The database is intended to support a wide array of research studies involving radiomics in sepsis patients, helping to reduce barriers to the reproducibility of clinical research.

VLSI implementation of a modified min-max median filter using an area and power competent tritonic sorter for image denoising

The prominence of image processing in today’s cutting-edge technology is undeniable. Integrating software with hardware leverages both strengths, resulting in a real-time processing system that is efficient and streamlined. Raw images are usually affected by noise, which hinders the acquisition of good-quality and detailed images; hence, denoising becomes necessary. This paper proposes a modified min-max median (MMM) filter to remove impulse noise and a Tritonic sorter to localize corrupted pixels. The proposed denoising method focuses on localizing noisy pixels, unlike traditional denoising approaches, which focus only on noise detection and filtering. A min-max sheet provides the location of the corrupted pixels, and filtering is performed on them. The Tritonic Sorter, consisting of a max locator and a min locator, compares three input values and finds the minimum, maximum and median values among them. Compared to other state-of-the-art methods, the proposed method minimizes the number of comparators needed to carry out the sorting process. The proposed method was synthesized in the ZedBoard Zynq kit using the Vivado tool. The results show that the area improved by 27%, and the power improved by 16.23% compared with those of the existing method.

Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.

To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) in low-dose abdominal computed tomography (CT) imaging compared with hybrid iterative reconstruction (HIR) and conventional deep learning reconstruction (cDLR) algorithms. We retrospectively analyzed abdominal CT scans performed using a low-dose protocol. Three different image reconstruction algorithms-HIR, cDLR, and SR-DLR-were applied to the same raw image data. Objective evaluations included noise magnitude and contrast-to-noise ratio (CNR), as well as noise power spectrum (NPS) and edge rise slope (ERS). Subjective evaluations were performed by radiologists, who assessed image quality in terms of noise, artifacts, sharpness, and overall diagnostic utility. Raw CT image data were obtained from 35 patients (mean CTDIvol 11.0 mGy; mean DLP 344.8 mGy/cm). cDLR yielded the lowest noise levels and highest CNR (p < 0.001). However, SR-DLR outperformed cDLR in terms of noise texture and resolution, achieving the lowest NPS peak and highest ERS (p < 0.001 and p = 0.005, respectively). Subjectively, SR-DLR was rated highest across all categories, including noise, artifacts, sharpness, and overall image quality, with statistically significant differences compared to cDLR and HIR (p < 0.001). SR-DLR was the most effective reconstruction algorithm for low-dose abdominal CT imaging, offering superior image quality and noise reduction compared to cDLR and HIR. This suggests that SR-DLR can enhance the reliability and diagnostic accuracy of abdominal imaging, particularly in low-dose settings, making it a valuable tool in clinical practice.

Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device

Detecting glucose levels is crucial for diabetes patients as it enables timely and effective management, preventing complications and promoting overall health. In this endeavor, we have designed a novel, affordable point-of-care diagnostic device utilizing microfluidic principles, a smartphone camera, and established laboratory colorimetric methods for accurate glucose estimation. Our proposed microfluidic device comprises layers of adhesive poly-vinyl films stacked on a poly methyl methacrylate (PMMA) base sheet, with micro-channel contours precision-cut using a cutting printer. Employing the gold standard glucose-oxidase/peroxidase reaction on this microfluidic platform, we achieve enzymatic glucose determination. The resulting colored complex, formed by phenol and 4-aminoantipyrine in the presence of hydrogen peroxide generated during glucose oxidation, is captured at various glucose concentrations using a smartphone camera. Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 95% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms. Furthermore, the classifier exhibits outstanding precision, recall, and F1 score of 94%, 93%, and 93%, respectively, as validated through our study, showcasing its exceptional predictive capability. Next, a user-friendly smartphone application named “GLUCOLENS AI” was developed to capture images, perform image processing, and communicate with cloud server containing the CNN classifier. The developed CNN model can be successfully used as a pre-trained model for future glucose concentration predictions.

Photodissociation dynamics of H2S+ via the A2A1 (0, 8, 0) state.

The photodissociation dynamics of the hydrogen sulfide cation (H2S+) (X2B1) were investigated using the time-sliced velocity map ion imaging technique. S+ (4Su) product images were measured at four photolysis wavelengths around 393.70nm, corresponding to the excitation of the H2S+ (X2B1) cation to the A2A1 (0, 8, 0) state. The raw images and the derived total kinetic energy releases (TKERs) spectra exhibited partial rotational resolution for the H2 products. A sensitive dependence on the photolysis wavelength was observed in the TKER spectra and anisotropy parameters. Within a narrow excitation energy range of ∼12cm-1, the H2 products showed two distinct rotational excitations. Furthermore, clear differences in anisotropy parameters were observed. These phenomena indicate that the rotational excitation of the H2S+ ions plays a role in the non-adiabatic photodissociation dynamics.

Relationship between high-level color features and temperature mapping of magnesium alloy surface images based on the K-nearest neighbor algorithm

Numerous deformation and processing application scenarios of magnesium alloy sheets require high precision and efficiency in temperature detection. Traditional contact temperature measurement and infrared thermal imaging temperature measurement methods suffer from low accuracy and susceptibility to environmental influences. To this end, a real-time sensing method for determining the average surface temperature of magnesium alloys using visible light images is proposed in this paper. Firstly, real-time surface images of 3 mm thick magnesium alloy sheets were captured as the temperature decreased from 450 °C to 300 °C under varying light intensities. Then, calculate the high-level color features based on the grayscale frequency of the Red Green Blue color space of the image, and select the high-level color features with a strong correlation with temperature by Fisher criterion. Finally, a mapping relationship between the high-level color features of magnesium alloy images and temperature was established using the K-nearest neighbor algorithm. The results show that images in RAW format have a wider grayscale range compared to JPG, which is helpful in determining the mapping relationship between image color features and temperature. After extracting and selecting the high-level color features that have a strong correlation with temperature, the dimensions of input features are reduced. Using the K-nearest neighbor algorithm can fit the complex nonlinear relationship between high-level color features and temperature more accurately.