Classification Efficiency Research Articles

A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer.

As a heterogeneous malignancy, breast cancer (BRCA) shows high incidence and mortality. Discovering novel molecular markers and developing reliable prognostic models may improve the survival of BCRA. The RNA-seq data of BRCA patients were collected from the training set The Cancer Genome Atlas (TCGA)-BRCA and validation set GSE20685 in the Gene Expression Omnibus (GEO) databases. The "GSVA" R package was used to calculate the glycolysis score for each patient, based on which all the patients were divided into different glycolysis groups. The "limma" package was employed to perform differentially expression genes (DEGs) analysis. Key signature genes were selected by performing un/multivariate and least absolute shrinkage and selection operator (LASSO) C regression and used to develop a RiskScore model. The ESTIMATE and MCP-Counter algorithms were used for quantifying immune infiltration level. The functions of the genes were validated using Western blot, colony formation, transwell and wound-healing assay. The glycolysis score and prognostic analysis showed that high glycolysis score was related to tumorigenesis pathway and a poor prognosis in BRCA as overactive glycolysis inhibited the normal functions of immune cells. Subsequently, we screened five key prognostic genes using the LASSO Cox regression analysis and used them to establish a RiskScore with a high classification efficiency. Based on the results of the RiskScore, it was found that patients in the high-risk group had significantly unfavorable immune infiltration and prognostic outcomes. A nomogram integrating the RiskScore could well predict the prognosis for BRCA patients. Knockdown of PSCA suppressed cell proliferation, invasion and migration of BRCA cells. This study developed a glycolysis-related signature with five genes to distinguish between high-risk and low-risk BRCA patients. A nomogram developed on the basis of the RiskScore was reliable to predict BRCA survival. Our model provided clinical guidance for the treatment of BRCA patients.

The Influence of Parallel Computing on Building Deep Learning Model for the Classification of Bean Diseases

In recent years, the utilization of deep learning techniques for image classification has made significant strides in the field of agriculture. One of the key areas of interest in agriculture is the early detection and classification of diseases in crops, as this can have an insightful impact on crop revenue and quality. This research has investigated the influence of parallel computing on the performance of a deep learning-based classification model for diagnosing bean diseases. Specifically, we have explored the use of parallel computing frameworks to accelerate model training and inference, thereby enhancing the efficiency and effectiveness of disease classification. Our findings demonstrated the potential for parallel computing to accelerate model training. When training a bean disease classification model, we achieved an accuracy of 0.93 using parallel computing, compared to 0.83 with serial computing. Moreover, parallel computing significantly reduced training time, taking only 3 minutes compared to 51 minutes with serial computing.

Performance evaluation of efficient segmentation and classification based iris recognition using sheaf attention network

Iris recognition, a precise biometric identification technique, relies on the distinct epigenetic patterns within the iris. Existing methods often face challenges related to segmentation accuracy and classification efficiency. To improve the accuracy and efficiency of iris recognition systems, this research proposes an innovative approach for iris recognition, focusing on efficient segmentation and classification using Convolutional neural networks with Sheaf Attention Networks (CSAN). Main objective is to develop an integrated framework that optimizes iris segmentation and classification. Subsequently, dense extreme inception multipath guided up sampling network is employed for accurate segmentation. Finally, classifiers including convolutional neural network with sheaf attention networks are evaluated. The findings indicate that the proposed method achieves superior iris recognition accuracy and robustness, making it suitable for applications such as secure authentication and access control. By comparing with existing approaches CSAN obtains 99.98%, 99.35%, 99.45% and 99.65% accuracy for the four different proposed datasets respectively.

An application of machine learning to classify food waste interventions from a food supply chain perspective

PurposeThe purpose of this paper is to demonstrate the efficacy of machine learning (ML) in managing natural language processing tasks, specifically by developing two ML models to systematically classify a substantial number of food waste interventions.Design/methodology/approachA literature review was undertaken to gather global food waste interventions. Subsequently, two ML models were designed and trained to classify these interventions into predefined supply chain-related groups and intervention types. To demonstrate the use of the models, a meta-analysis was performed to uncover patterns amongst the interventions.FindingsThe performance of the two classification models underscores the capabilities of ML in natural language processing, significantly enhancing the efficiency of text classification. This facilitated the rapid and effective classification of a large dataset consisting of 2,469 food waste interventions into six distinct types and assigning them to seven involved supply chain stakeholder groups. The meta-analysis reveals the most dominant intervention types and the strategies most widely adopted: 672 interventions are related to “Process and Operations Optimisation”, 457 to “Awareness and Behaviour Interventions” and 403 to “Technological and Engineering Solutions”. Prominent stakeholder groups, including “Processing and Manufacturing”, “Retail” “Government and Local Authorities” and “NGOs, Charitable Organisations and Research and Advocacy Groups”, are actively involved in over a thousand interventions each.Originality/valueThis study bridges a notable gap in food waste intervention research, a domain previously characterised by fragmentation and incomprehensive classification of the full range of interventions along the whole food supply chain. To the best of the authors’ knowledge, this is the first study to systematically classify a broad spectrum of food waste interventions while demonstrating ML capabilities. The study provides a clear, systematic framework for interventions to reduce food waste, offering valuable insight for practitioners in the food system, policymakers and consumers. Additionally, it lays the foundation for future in-depth research in the food waste reduction domain.

PAFE: A lightweight visualization-based fast malware classification method

With the development of automated malware toolkits, cybersecurity faces evolving threats. Although visualization-based malware analysis has proven to be an effective method, existing approaches struggle with challenging malware samples due to alterations in the texture features of binary images during the visualization preprocessing stage, resulting in poor performance. Furthermore, to enhance classification accuracy, existing methods sacrifice prediction time by designing deeper neural network architectures. This paper proposes PAFE, a lightweight and visualization-based rapid malware classification method. It addresses the issue of texture feature variations in preprocessing through pixel-filling techniques and applies data augmentation to overcome the challenges of class imbalance in small sample datasets. PAFE combines multi-scale feature fusion and a channel attention mechanism, enhancing feature expression through modular design. Extensive experimental results demonstrate that PAFE outperforms the current state-of-the-art methods in both efficiency and effectiveness for malware variant classification, achieving an accuracy rate of 99.25 % with a prediction time of 10.04 ms.

X-Ray Source Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIA

X-ray observations play a crucial role in time-domain astronomy. The Einstein Probe (EP), a recently launched X-ray astronomical satellite, emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics. With a focus on systematic surveys in the soft X-ray band, EP aims to discover high-energy transients and monitor variable sources in the universe. To achieve these objectives, a quick and reliable classification of observed sources is essential. In this study, we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder—Lobster Eye Imager for Astronomy (LEIA) and EP-WXT simulations. The proposed Random Forest classifier, built on selected features derived from light curves, energy spectra, and location information, achieves an accuracy of approximately 95% on EP simulation data and 98% on LEIA observational data. The classifier is integrated into the LEIA data processing pipeline, serving as a tool for manual validation and rapid classification during observations. This paper presents an efficient method for the classification of X-ray sources based on single observations, along with implications of most effective features for the task. This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.

RSU-Net: A new method for fine classification of corn residue coverage in black soil area using Chinese GF-1B PMS image

Corn residue coverage (CRC) plays an important role in soil protection, with respect to both the restoration of soil fertility and the reduction of soil erosion, particularly in black soil are-as. Fine classification of areas with high and low coverage of crop residue is essential for accu-rately quantifying the soil protection level; it is insufficient to focus solely on whether the crop residue is covered. Remote sensing images from Chinese GaoFen-1 (GF-1) B, which can be acquired with a high spatial resolution of 2 m and a revisit period of 4d, have potential for the fine classification of high and low coverage of corn residues (HLCCR). To improve the efficiency and accuracy of the fine classification of HLCCR, a deep neural network RSU-Net was proposed in this study. RSU-Net combines residual units with spatial pyramid pooling technology to enhance the ability to extract the features of different coverage types from GF-1 B images. Comparison ex-periments were conducted to validate the effectiveness of RSU-Net and compare it with the classical U-Net. The results show that the overall accuracy, kappa, and mean intersection over union of RSU-Net in fine classification of HLCCR were 86.29%, 0.75, and 0.6844, respectively, and those of U-Net were 84.61%, 0.72, and 0.6474, respectively. Compared with the classical U-Net, the classification accuracy of RSU-Net was improved. These results suggest that RSU-Net has great po-tential for fine classification of HLCCR.

Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring.

A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms.

An Empirical Study on the Upcycling of Glass Bottles into Hydrocyclone Separators

Cyclones are pivotal in mechanical process engineering and crucial in the complex field of separation technology. Their robustness and compact spatial requirements render them universally applicable and versatile across various industrial domains. Depending on the utilized fluid and field of application, both gas-based cyclones and hydrocyclones (HCs) are well established. Regarding HC design, enduring elongated flat cones have seen minimal alterations in shape and structure since their introduction over more than a hundred years ago. Experimental investigations regarding unconventional cone designs within scientific studies remain the exception. Therefore, this study focuses on alternative geometric configurations of the separation chambers and highlights their impact on separation and energy efficiency. To achieve this objective, different geometric shapes are investigated and retrofitted into HCs. The geometric foundation is derived from upcycled glass bottles. The repurposed bottles with a volume of 750 mL are used in conjunction with an inlet part, following the established Rietema design. Experimental tests are conducted with dilute phase separation, using 0.1–200 µm test particles in water. Comparisons between a bottle-based HC and a conventional Rietema design were conducted, establishing a benchmark against the standard. The findings revealed a noticeable correlation between separation efficiency and cone geometry. Conical designs demonstrated enhanced separation, particularly at lower volume flows. At the highest volume flow of 75 L min−1, the best performing bottle cyclones showed separation efficiencies of 78.5%, 78.4% and 77.9% and therefore are in a competitive range with 78.0% efficiency, achieved using the commercial Rietema design. Minimal disparities in cut sizes were observed in terms of separation grade efficiency among the models tested. Variations in separation efficiency and fractional efficiency curves indicated nuanced differences in classification efficiency.

Parkinson's image detection and classification based on deep learning

ObjectiveThere are two major issues in the MRI image diagnosis task for Parkinson's disease. Firstly, there are slight differences in MRI images between healthy individuals and Parkinson's patients, and the medical field has not yet established precise lesion localization standards, which poses a huge challenge for the effective prediction of Parkinson's disease through MRI images. Secondly, the early diagnosis of Parkinson's disease traditionally relies on the subjective judgment of doctors, which leads to insufficient accuracy and consistency. This article proposes an improved YOLOv5 detection algorithm based on deep learning for predicting and classifying Parkinson's images.MethodsThis article improves the YOLOv5s network as the basic framework. Firstly, the CA attention mechanism was introduced to enable the model to dynamically adjust attention based on local features of the image, significantly enhancing the sensitivity of the model to PD related small pathological features; Secondly, replace the dynamic full dimensional convolution module to optimize the multi-level extraction of image features; Finally, the coupling head strategy is adopted to improve the execution efficiency of classification and localization tasks separately.ResultsWe validated the effectiveness of the proposed method using a dataset of 582 MRI images from 108 patients. The results show that the proposed method achieves 0.961, 0.974, and 0.986 in Precision, Recall, and mAP, respectively, and the experimental results are superior to other algorithms.ConslusionThe improved model has achieved high accuracy and detection accuracy, and can accurately detect and recognize complex Parkinson's MRI images.SignificanceThis algorithm has shown good performance in the early diagnosis of Parkinson's disease and can provide clinical assistance for doctors in early diagnosis. It compensates for the limitations of traditional methods.

Research on Model Selection-Based Weighted Averaged One-Dependence Estimators

The Averaged One-Dependence Estimators (AODE) is a popular and effective method of Bayesian classification. In AODE, selecting the optimal sub-model based on a cross-validated risk minimization strategy can further enhance classification performance. However, existing cross-validation risk minimization strategies do not consider the differences in attributes in classification decisions. Consequently, this paper introduces an algorithm for Model Selection-based Weighted AODE (SWAODE). To express the differences in attributes in classification decisions, the ODE corresponding to attributes are weighted, with mutual information commonly used in the field of machine learning adopted as weights. Then, these weighted sub-models are evaluated and selected using leave-one-out cross-validation (LOOCV) to determine the best model. The new method can improve the accuracy and robustness of the model and better adapt to different data features, thereby enhancing the performance of the classification algorithm. Experimental results indicate that the algorithm merges the benefits of weighting with model selection, markedly enhancing the classification efficiency of the AODE algorithm.

A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification.

Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC). However, limited training data and spectral uncertainty restrict the classification performance, and the computational demands of a graph convolution network (GCN) present challenges for real-time applications. To overcome these issues, a dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses an adaptive multi-scale superpixel segmentation method to build fusion adjacency matrices at various scales, which improves the graph convolution efficiency and node representations. Additionally, a spectral feature enhancement module (SFEM) enhances the transmission of crucial channel information between the two graph convolutions. Meanwhile, the CNN branch uses a convolutional network with an attention mechanism to focus on detailed features of local areas. By combining the multi-scale superpixel features from the GCN branch and the local pixel features from the CNN branch, this method leverages complementary features to fully learn rich spatial-spectral information. Our experimental results demonstrate that the proposed method outperforms existing advanced approaches in terms of classification efficiency and accuracy across three benchmark data sets.

ICS-ResNet: A Lightweight Network for Maize Leaf Disease Classification

The accurate identification of corn leaf diseases is crucial for preventing disease spread and improving corn yield. Plant leaf images are often affected by factors such as complex backgrounds, climate, light, and sample data imbalance. To address these issues, we propose a lightweight convolutional neural network, ICS-ResNet, based on ResNet50. This network incorporates improved spatial and channel attention modules as well as a deep separable residual structure to enhance recognition accuracy. (1) The residual connections in the ResNet network prevent gradient loss during deep network training. (2) The improved channel attention (ICA) and spatial attention (ISA) modules fully utilize semantic information from different feature layers to accurately localize key features of the network. (3) To reduce the number of parameters and lower computational costs, we replace traditional convolutional computation with a depth-separable residual structure. (4) We also employ cosine annealing to dynamically adjust the learning rate, enhancing the network’s training stability, improving model convergence, and preventing local optima. Experiments on the corn dataset in Plant Village compare the proposed ICS-ResNet with eight popular networks: CSPNet, InceptionNet_v3, EfficientNet, ShuffleNet, MobileNet, ResNet50, ResNet101 and ResNet152. The results show that the ICS-ResNet achieves an accuracy of 98.87%, which is 5.03%, 3.18%, 1.13%, 1.81%, 1.13%, 0.68%, 0.44% and 0.60% higher than the other networks, respectively. Furthermore, the number of parameters and computations are reduced by 69.21% and 54.88%, respectively, compared to the original ResNet50 network, significantly improving the efficiency of corn leaf disease classification. The study provides strong technical support for sustainable agriculture and the promotion of agricultural science and technology innovation.

Optimizing feedforward neural networks using a modified weighted mean of vectors: Case study chemical datasets

This paper proposes a modified version of the weighted mean of vectors algorithm (mINFO), which combines the strengths of the INFO algorithm with the Enhanced Solution Quality Operator (ESQ). The ESQ boosts the quality of the solutions by avoiding optimal local values, verifying that each solution moves towards a better position, and increasing the convergence speed. Furthermore, we employ the mINFO algorithm to optimize the connection weights and biases of feedforward neural networks (FNNs) to improve their accuracy. The efficacy of FNNs for classification tasks is mainly dependent on hyperparameter tuning, such as the number of layers and nodes. The mINFO was evaluated using the IEEE Congress on Evolutionary Computation held in 2020 (CEC’2020) for optimization tests, and ten chemical data sets were applied to validate the performance of the FNNs classifier. The proposed algorithm’s results have been evaluated with those of other well-known optimization methods, including Runge Kutta optimizer’s (RUN), particle swarm optimization (PSO), grey wolf optimization (GWO), Harris hawks optimization (HHO), whale optimization algorithm (WOA), slime mould algorithm (SMA) and the standard weighted mean of vectors (INFO). In addition, some improved metaheuristic algorithms. The experimental results indicate that the proposed mINFO algorithm can improve the convergence speed and generate effective search results without increasing computational costs. In addition, it has improved the FNN’s classification efficiency.

Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells.

This work investigated the high-throughput classification performance of microscopic images of mesenchymal stem cells (MSCs) using a hyperspectral imaging-based separable convolutional neural network (CNN) (H-SCNN) model. Human bone marrow mesenchymal stem cells (hBMSCs) were cultured, and microscopic images were acquired using a fully automated microscope. Flow cytometry (FCT) was employed for functional classification. Subsequently, the H-SCNN model was established. The hyperspectral microscopic (HSM) images were created, and the spatial-spectral combined distance (SSCD) was employed to derive the spatial-spectral neighbors (SSNs) for each pixel in the training set to determine the optimal parameters. Then, a separable CNN (SCNN) was adopted instead of the classic convolutional layer. Additionally, cultured cells were seeded into 96-well plates, and high-functioning hBMSCs were screened using both manual visual inspection (MV group) and the H-SCNN model (H-SCNN group), with each group consisting of 96 samples. FCT served as the benchmark to compare the area under the curve (AUC), F1 score, accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictive value (PPV), and negative predictive value (NPV) between the manual and model groups. The best classification Acc was 0.862 when using window size of 9 and 12 SSNs. The classification Acc of the SCNN model, ResNet model, and VGGNet model gradually increased with the increase in sample size, reaching 89.56 ± 3.09, 80.61 ± 2.83, and 80.06 ± 3.01%, respectively at the sample size of 100. The corresponding training time for the SCNN model was significantly shorter at 21.32 ± 1.09 min compared to ResNet (36.09 ± 3.11 min) and VGGNet models (34.73 ± 3.72 min) (P < 0.05). Furthermore, the classification AUC, F1 score, Acc, Sen, Spe, PPV, and NPV were all higher in the H-SCNN group, with significantly less time required (P < 0.05). Microscopic images based on the H-SCNN model proved to be effective for the classification assessment of hBMSCs, demonstrating excellent performance in classification Acc and efficiency, enabling its potential to be a powerful tool in future MSCs research.

Supersonic coaxial aerodynamic atomization dust removal technology

The high concentration of respirable dust pollution created during coal mining has long been known to be harmful to employee health. A water absorbing atomization device has certain advantages when spray trapping particle sizes of PM2.5–10. To further improve the PM0–2.5 trapping efficiency, supersonic coaxial pneumatic atomization dust removal technology has been developed. The effects of fog droplet characteristics on the instantaneous diffusion of dust were studied by numerical simulation and macro- and microexperiments for the first time, and the coupling characteristics of fog droplets and dust fields were obtained. The results show that with increasing aerodynamic pressure, the supersonic range extends along the axis toward the nozzle outlet, and the average velocity in the tube increases. Under different pressures, the droplet particle size in the supersonic coaxial atomizing nozzle is <6 μm and decreases with increasing aerodynamic pressure. When a droplet effuses from the nozzle, the transonic distribution of the flow field causes the droplet velocity to increase first and then decrease under the drag force. The droplet field characteristic distribution of supersonic coaxial atomization is determined by droplet evaporation, condensation, and migration. Under the same pressure, with increasing spray distance, the distribution of the droplet particle size increases in a stepwise manner, and the droplet velocity decreases according to an exponential function so that the droplet field produces a high-speed fine fog area. The range of droplets with higher velocities and smaller particle sizes is mainly influenced by migration and increases with increasing pressure. The coupling effect droplet field and dust field can be characterized by the instantaneous dispersion of dust and are determined by the distribution characteristics of the fog droplet field. The large range of high-speed fine fog produced by supersonic coaxial atomization technology easily traps respirable dust, and the classification efficiency of PM0–2.5 reaches 90%. In this study, a new dynamic microfog dust trapping technique was proposed, and a new method of instantaneous sampling combined with a microscopic dispersion test was adopted to characterize the temporal and spatial distributions of dust fields affected by fog droplet fields, reveal the generation of dynamic microfog and the mechanism of dust trapping, enrich the theory of fine dust trapping, and provide technical support for the safe production and utilization of coal.

Identification of disulfidptosis- and ferroptosis-related transcripts in periodontitis by bioinformatics analysis and experimental validation.

Disulfidptosis and ferroptosis are forms of programmed cell death that may be associated with the pathogenesis of periodontitis. Our study developed periodontitis-associated biomarkers combining disulfidptosis and ferroptosis, which provides a new perspective on the pathogenesis of periodontitis. Firstly, we obtained the periodontitis dataset from public databases and found disulfidptosis- and ferroptosis-related differentially expressed transcripts based on the disulfidptosis and ferroptosis transcript sets. After that, transcripts that are tissue biomarkers for periodontitis were found using three machine learning methods. We also generated transcript subclusters from two periodontitis microarray datasets: GSE16134 and GSE23586. Furthermore, three transcripts with the best classification efficiency were further screened. Their expression and classification efficacy were validated using qRT-PCR. Finally, periodontal clinical indicators of 32 clinical patients were collected, and the correlation between three transcripts above and periodontal clinical indicators was analyzed. We identified six transcripts that are tissue biomarkers for periodontitis, the top three transcripts with the best classification, and delineated two expression patterns in periodontitis. Our study found that disulfidptosis and ferroptosis were associated with immune responses and may involve periodontitis genesis.

Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms

This paper may be the first meta-analysis that presents a comprehensive synthesis of scientific works spanning the last five years, focusing on methodologies and results related to the analysis of nanocomposite using nanoparticles. The primary objective is to identify the optimal algorithm using software information and leading to better classification methodology. Specifically, this study comes up with the advantages and the drawbacks of the most used algorithms and proposes an enhancement and performance of Recurrent Neural Networks based on Long Short Term Memory (LSTM) neurons. Besides, a comparison of Deep Learning methods for the classification of polymeric nanoparticles, with polypropylene serving as a case study will be implemented. Experiment comparisons are conducted to assess with one physical property, later expanded to four properties and finally to eight properties. Neural networks, including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Recurrent Neural Networks-Monte Carlo, are employed for simulations. The evaluation criteria encompass accuracy, calculation time, mean square error (MSE) and other metrics. The findings contribute to the selection of an optimal algorithm for the analysis of polymeric nanoparticles, emphasizing the potential of Deep Learning methodologies, particularly Recurrent Neural Networks Monte Carlo, in advancing classification accuracy and efficiency.

Comparison on Environmental Benefits of Municipal Solid Waste Management in Suzhou Before and After Classification

In order to systematically understand the urban environmental benefit improvement of municipal solid waste （MSW） classification, based on the disposal data of MSW before and after the MSW classification in Suzhou from 2017 to 2021, the environmental impact potential （EIP） of the MSW collection-transportation-disposal process was calculated, and the environmental benefits of the MSW integrated management in Suzhou to 2035 were predicted. After the MSW classification in Suzhou at the end of 2019, the EIP （in terms of PET2000, the same below） of the per unit weight of MSW was reduced by 18.38% from 2.34×10-13 t-1 in 2017 to 1.91×10-13 t-1 in 2021. The environmental benefits of the MSW integrated management could be improved by classification. Based on the Suzhou MSW removal and transportation situation in 2021, different classification and disposal scenarios were established to calculate. It was found that after the classification effect showed gradient improvement, and the disposal capacity matched accordingly, the environmental benefits of MSW were further improved. Under the planning disposal capacity scenario of "zero waste to landfill", the EIP and the total carbon emissions of per unit weight of MSW should be reduced by 23.96% and 30.73%, respectively, compared with the actual situation in 2021. Based on the linear model of population and economic development level of Suzhou, it is expected that the annual production of MSW in Suzhou will be increased to 6.965 million tons in 2035. Under the background of continuous improvement of MSW classification and continuous optimization of city appearance and environment in Suzhou, based on the status quo of terminal disposal capacity in Suzhou, the EIP of per unit weight of MSW after improving the efficiency of classification by 2035 was predicted to be 1.54×10-13 t-1, the total EIP would be 1.05×10-6, and the total carbon emissions would increase to 3.80 million tons. Under the ideal scenario of expanding the scale of waste disposal, "zero landfill" of raw MSW, and full resource utilization of food waste, the EIP of per unit weight of MSW in 2035 was predicted to be 1.28×10-13 t-1, and the total EIP and the total carbon emissions would be 8.69×10-7 and 3.23 million tons, respectively, which was approximately 5.65% and 1.23% less than the actual scenario in 2021, respectively. The EIP and carbon emissions of MSW integrated management could be controlled better by the coordinated promotion of classified collection and transportation and quality disposal.

Transformer-based decoder of melanoma classification using hand-crafted texture feature fusion and Gray Wolf Optimization algorithm

Melanoma is a type of skin cancer that poses significant health risks and requires early detection for effective treatment. This study proposing a novel approach that integrates a transformer-based model with hand-crafted texture features and Gray Wolf Optimization, aiming to enhance efficiency of melanoma classification. Preprocessing involves standardizing image dimensions and enhancing image quality through median filtering techniques. Texture features, including GLCM and LBP, are extracted to capture spatial patterns indicative of melanoma. The GWO algorithm is applied to select the most discriminative features. A transformer-based decoder is then employed for classification, leveraging attention mechanisms to capture contextual dependencies. The experimental validation on the HAM10000 dataset and ISIC2019 dataset showcases the effectiveness of the proposed methodology. The transformer-based model, integrated with hand-crafted texture features and guided by Gray Wolf Optimization, achieves outstanding results. The results showed that the proposed method performed well in melanoma detection tasks, achieving an accuracy and F1-score of 99.54% and 99.11% on the HAM10000 dataset, and an accuracy of 99.47%, and F1-score of 99.25% on the ISIC2019 dataset.• We use the concepts of LBP and GLCM to extract features from the skin lesion images.• The Gray Wolf Optimization (GWO) algorithm is employed for feature selection.• A decoder based on Transformers is utilized for melanoma classification.