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In the domain of medical imaging, the advent of deep learning has marked a significant progression, particularly in the nuanced area of periodontal disease diagnosis. This study specifically targets the prevalent issue of scarce labeled data in medical imaging. We introduce a novel unsupervised few-shot learning algorithm, meticulously crafted for classifying periodontal diseases using a limited collection of dental panoramic radiographs. Our method leverages UNet architecture for generating regions of interest (RoI) from radiographs, which are then processed through a Convolutional Variational Autoencoder (CVAE). This approach is pivotal in extracting critical latent features, subsequently clustered using an advanced algorithm. This clustering is key in our methodology, enabling the assignment of labels to images indicative of periodontal diseases, thus circumventing the challenges posed by limited datasets. Our validation process, involving a comparative analysis with traditional supervised learning and standard autoencoder-based clustering, demonstrates a marked improvement in both diagnostic accuracy and efficiency. For three real-world validation datasets, our UNet-CVAE architecture achieved up to average 14% higher accuracy compared to state-of-the-art supervised models including the vision transformer model when trained with 100 labeled images. This study not only highlights the capability of unsupervised learning in overcoming data limitations but also sets a new benchmark for diagnostic methodologies in medical AI, potentially transforming practices in data-constrained scenarios. [ABSTRACT FROM AUTHOR]

It is an enormous challenge for imbalanced data learning in the field of machine learning. To construct balanced datasets, oversampling techniques have been studied extensively. However, many oversampling methods suffer from introducing noisy samples and blurring classification boundaries, leading to overfitting. To solve this problem, this paper proposes a new oversampling method, namely CS-SMOTE, for synthesizing minority class samples by three-point interpolation. CS-SMOTE is mainly based on the center offset factor and a synthesis strategy. First, the CS-SMOTE method removes noise samples, calculates the center offset factor, and selects sparsely distributed minority class samples by using the K-distance graph technique. Next, new samples are generated based on sparse minority samples, random minority samples, and sub-cluster centers located in the same sub-cluster samples. Finally, multiple comparative experiments on 18 well-known datasets demonstrate the effectiveness and general applicability of the proposed CS-SMOTE method for the imbalanced data classification. The experiments show that CS-SMOTE outperforms other competitors in terms of classification accuracy, while avoiding the issue of overfitting. [ABSTRACT FROM AUTHOR]

Growing economy has boomed tourism, but intelligent travel planning services restrict long-term and stable tourism development. Typically, travel planning requires substantial time and cost. And currently, less focus on user preferences in most tourist attraction recommendations also results in low efficiency. In this paper, firstly, the K-means algorithm is introduced for clustering analysis of user behavior or interests, so as to better understand user preferences. Gaussian kernel density estimation and similarity measurement are also adopted to improve the traditional K-means algorithm, which provides the foundation for a tourist attraction recommendation model. Then, to further improve transportation route planning, the study introduces the ant colony algorithm, adaptive crossover strategy and local search algorithm to enhance the traditional genetic algorithm for an optimized travel path planning model. The outcomes show that the improved clustering algorithm possesses the highest accuracy of 0.96 and 0.78 in Iris and Glass datasets respectively, along with a sum of squared errors of 96.73 and 476.48 respectively. The shortest running time in the Yeast data-set is 1.22 s. The improved clustering algorithm with 50 nearest neighbors has an average absolute error value of 0.749, and its longest running time does not exceed 1 s. In summary, the model developed in this study is highly applicable to personalized recommendation services and efficient travel routes. [ABSTRACT FROM AUTHOR]

Internet of Things development is of great significance for modern society progress. However, the limited information in some areas with incomplete infrastructure restricts Internet of Things development, so the long-distance information transmission task of sensor nodes needs to be put on the agenda. The research introduces beamforming technology for clustering wireless sensor nodes, and proposes a clustering algorithm based on wireless sensor node's energy consumption rate for nodes energy management to achieve remote information sharing and transmission. The results confirm that the success rate of clustering algorithm based on beamforming event triggering increases with node density increasing, and the success rate is infinitely close to 1. In addition, when the sensor node is 120, the average charging delay time based on machine learning energy consumption prediction is only 946 seconds, which is reduced by 521 seconds compared to the Mean-shift algorithm. When sensor node is 120, the algorithm has a successful access count of up to 1288 times. These two clustering algorithms have good clustering performance and significant practical application effects, providing reliable technical support for remote data transmission in the modern Internet of Things. [ABSTRACT FROM AUTHOR]

To solve some scheduling problems of batch processes based on timed Petri net models, timed extended reachability graphs (TERGs) and approximated TERGs can be used. Such graphs abstract temporal specifications and represent parts of timed languages. By exploring the feasible trajectories in a TERG, optimal schedules can be obtained with respect to the makespans of batch processes that are modeled by timed Petri nets. Nevertheless, the rapid growth of the number of states in a TERG makes the problem intractable for large systems. In this paper, we improve the existing clustering TERG approach, and we make it suitable for large sized batch processes. We also enlarge a systematic approach to model batch processes with timed Petri nets. Finally, a comprehensive example of scheduling problem is studied for an archetypal chemical production plant in order to illustrate the efficiency of the proposed approach. • The scheduling issue of batch chemical processes is formalized with timed Petri nets. • A graph named ClusTERG approximates the state space through hierarchical clustering. • The approximation error of this ClusTERG is far less than other approximations. • Such an approach is used to obtain near-optimal schedules of a batch process. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional lane line detection algorithm relies on artificial design features, which has poor robustness and cannot cope with the complex urban street background. With the rise of deep learning technology, the algorithm model with convolutional neural network as the mainstream is widely used in the field of computer vision, which provides a new idea for lane line detection. In order to improve the disadvantages of traditional lane line detection methods that are vulnerable to environmental impact and poor robustness, a nonlinear convolution neural network algorithm for driverless lane line detection is proposed. Firstly, the pretreatment method of extracting the region of interest and enhancing the contrast of lane lines is used to reduce the unnecessary image background and enhance the feature details of the image. Existing deep learning-based lane line detection algorithms still have difficulties. First, accumulated wear and tear will cause lane line to fade and fade; roadside trees and buildings can interfere with the performance of lane line detection algorithm. In addition, compared with the pixels of the whole picture, the lane line pixels are too few, and the deep convolution neural network of layer convolution is easy to lead to the loss of details. In addition, when the traffic flow is large, the lane line is easily blocked, which makes it more difficult to detect the lane line. Then the model is built based on the lane line image features extracted by CNN, and the DBSCAN clustering algorithm is used to post-process the lane line segmentation model; Finally, the least square method is used to fit the quadratic curve of the pixel peak points of the lane line, and the fitting results are regressed to the original image. The experimental results show that the accuracy and recall of the lane line detection model verification set are 91.3% and 90.6%, respectively, indicating that the model has a good segmentation effect. It is proved that the lane line detection method based on CNN combined with post-processing can effectively reduce the defects of artificial experience, and has better robustness and accuracy than the traditional lane line detection method. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSThe high specific strength and stiffness of carbon fiber-reinforced polymer (CFRP) stiffened plates make them popular in aerospace and other fields. Nevertheless, CFRP stiffened plates have unique characteristics and complicated and diversified failure patterns, so it is impossible to determine its failure law with a uniform failure criterion accurately. Its load-carrying capacity has always been calculated on the conservative side of empirical estimation. This work uses the stressing state theory to investigate the stressing state evolution of composite stiffened plates under axial loading. It conducts three CFRP stiffened plates of the same configuration from a thermodynamic perspective. By equating stiffened plates to thermodynamic systems, the measured strain data are modeled as state variables, and renormalization and clustering algorithms are applied to determine the phase transition loads of the structure, while the characteristic points before and after normalization are verified for their reasonableness and stability. Accordingly, this work reveals the failure laws of CFRP stiffened plates by applying a thermodynamic-based method. It characterizes the structural failure features by defining catastrophe points throughout the failure process, which provides a new reference for the failure analysis and strength design of CFRP stiffened plates.Three full-scale CFRP stiffened plates of the same configuration were tested by axial loading.A network-free renormalization was applied to construct modes and characteristic parameters of CFRP stiffened plates.A clustering algorithm was applied to reveal the phase transition loads of CFRP stiffened plates.The stability and accuracy of the phase transition loads of CFRP stiffened plates are verified. [ABSTRACT FROM AUTHOR]

A human posture interaction system guaranteed by algorithmic control is proposed to improve the convenience and control accuracy of wheelchairs. Initially, a system is designed based on microelectromechanical systems (MEMS) technology that incorporates a dual-inertial measurement unit to efficiently capture data on the user's body posture. Subsequently, a K-means clustering algorithm is implemented in the system to analyze and recognize the user's body position in real-time. This condition allows the system to deduce the user's intention and react accordingly. Finally, the control system is validated through identification control experiments. Experiment results demonstrate a success rate above 97 %, thereby suggesting the reliability of the posture recognition algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as a core method, combined with dilation and erosion theories, to expand the boundaries of point cloud clusters, merging adjacent point cloud elements into unified clusters. This method effectively identifies and connects spatially adjacent point cloud clusters while efficiently eliminating noise from target object point clouds, thereby achieving more precise recognition of unknown obstacles on the track. Furthermore, the effective integration of this algorithm with lightweight shared convolutional semantic segmentation algorithms enables accurate localization of obstacles. Experimental results using two combined public datasets demonstrate that the obstacle detection average recall rate of the proposed method reaches 90.3%, significantly enhancing system reliability. These findings indicate that the proposed detection strategy effectively improves the accuracy and real-time performance of obstacle recognition, thereby presenting important practical application value for ensuring the safe operation of railway tracks. [ABSTRACT FROM AUTHOR]

With the continuous development of information and communication network technology and data mining technology, an ICT routing algorithm based on improved k-means clustering algorithm and gray wolf optimization is proposed to improve the energy efficiency and extend the life cycle of wireless communication networks. Firstly, we model the atmospheric channel, design the topology of the tolerant network and optimize the routing algorithm to achieve the interoperability between multiple networks and break through the traditional network requirements for time and space constraints. Then we implement an IPv6-based soft router supporting Anycast routing protocol under Linux environment and propose a new Anycast routing implementation scheme. Finally, an ICT routing algorithm (KGRA algorithm) based on improved k-means clustering and gray wolf optimization is designed, which first clusters the sensor nodes in the monitoring area using the improved k-means clustering algorithm. The clusters formed by the improved k-means clustering algorithm are optimized, and then the grey wolf optimization algorithm with improved convergence factor is used to select the cluster heads in the optimized cluster area. Finally, the nodes in the monitoring area will send data to the aggregation nodes using single-hop intra-cluster and multi-hop inter-cluster transmission, thus increasing the survival period of the network and greatly reducing energy consumption while balancing the energy consumption of the network. [ABSTRACT FROM AUTHOR]

Since the nineteenth-century devolution of natural philosophy into disciplines, scientific activity has expanded from single-disciplined research to comprehensive research. The flow of information between different research teams makes scientific research and its places increasingly social, which is no longer limited to the closed laboratory workbench. Reviewing the literature, we found that the development of scientific research buildings experienced a transition from efficiency to openness. And the new organization of research communities and its demand for space put forward higher requirements for scientific research buildings. To deeply explore the design trends of scientific research buildings at this stage, the sample database is established using space syntax and K-means clustering algorithm software to quantitatively analyze spatial layouts of 42 scientific research buildings worldwide from the perspective of sociology and innovation, clustered into five categories. Each category shows a different tendency in terms of accessibility, visibility, and the proportion of public space, and the different spatial configurations and openness somehow reflect the attitude of the research organization or group towards the use of space. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] Aiming at the characteristics of various target scales, complex background and dense small targets in aerial images of unmanned aerial vehicles (UAC), a small target detection algorithm LM-YOLO based on YOLOV5 is proposed. [Method] Firstly, the number of small target detection head was increased and K-DBSCAN clustering algorithm was used to optimize the anchor frame, so as to generate an anchor frame more suitable for small target detection and improve the detection accuracy of the algorithm. Then, a more efficient MobileNetV3-CBAM was designed as a feature extraction network to reduce the size of the network model. Finally, the large kernel selective attention mechanism LSK was introduced into the feature fusion network to increase the resolution of the model to similar targets. [Result] The experimental results on the public data set Vis-Drone2019 show that the average detection accuracy of LM-YOLO for all targets is improved by 7.6% and the model size is reduced by 45% compared with the benchmark model YOLOV5. [Conclusion] Experiments show that the proposed algorithm can reduce the model size while maintaining good detection accuracy. [ABSTRACT FROM AUTHOR]

Time series anomaly detection is an important field of data science. Statistical, distance-based, clustering-based, or density-based approaches can detect anomalies. Generally, distance-based methods are relatively straightforward, but the method's effectiveness depends on how well they handle the distribution of data points. To address the challenge, a preprocessing step is used to convert the underlying time series into a more useful format. In this paper, a novel clustering-based representation of time series is proposed. This representation is then used to compute anomaly scores and detect anomalies. Experimental studies on synthetic and real datasets show that proposed method outperforms other methods by up to 75% for five standard performance metrics. [ABSTRACT FROM AUTHOR]

Abstract In the domain of medical imaging, the advent of deep learning has marked a significant progression, particularly in the nuanced area of periodontal disease diagnosis. This study specifically targets the prevalent issue of scarce labeled data in medical imaging. We introduce a novel unsupervised few-shot learning algorithm, meticulously crafted for classifying periodontal diseases using a limited collection of dental panoramic radiographs. Our method leverages UNet architecture for generating regions of interest (RoI) from radiographs, which are then processed through a Convolutional Variational Autoencoder (CVAE). This approach is pivotal in extracting critical latent features, subsequently clustered using an advanced algorithm. This clustering is key in our methodology, enabling the assignment of labels to images indicative of periodontal diseases, thus circumventing the challenges posed by limited datasets. Our validation process, involving a comparative analysis with traditional supervised learning and standard autoencoder-based clustering, demonstrates a marked improvement in both diagnostic accuracy and efficiency. For three real-world validation datasets, our UNet-CVAE architecture achieved up to average 14% higher accuracy compared to state-of-the-art supervised models including the vision transformer model when trained with 100 labeled images. This study not only highlights the capability of unsupervised learning in overcoming data limitations but also sets a new benchmark for diagnostic methodologies in medical AI, potentially transforming practices in data-constrained scenarios.

Aiming at the issues of ant colony algorithm in solving large-scale traveling salesman problems (TSP), such as easily falling into local optima and slow convergence speed, an ant colony algorithm integrating adaptive clustering and mother ant guidance strategy (AMACS) is proposed. In the adaptive clustering process, firstly, an improved clustering method is used, which leverages the concepts of maximum-minimum distance and class density to obtain the optimal clustering results through an adaptive clustering strategy, and quickly obtains the optimized solution for each cluster. Next, the neighboring clusters are fused using a spider web fusion principle, effectively enhancing the accuracy of the initial solution. Additionally, the initial solution is optimized through the mother ant guidance strategy, which includes two components: path guidance and pheromone optimization. Path guidance sets the initial solution as the solution for the first generation, improving the stability of the algorithm; pheromone optimization involves stimulating the pheromone along the initial solution’s path, thereby enhancing the solution’s accuracy. Finally, a random recombination strategy is employed to reorganize and randomly stimulate the pheromones, helping the algorithm to escape local optima and improving the solution’s accuracy. Experimental results show that the proposed algorithm not only ensures solution accuracy when solving large-scale TSPs but also improves the stability of the algorithm.

Behavioral reasoning of communication radiation sources refers to inferring their behavioral patterns and target intentions through observing and analyzing the signals emitted by communication radiation sources. Especially under non-cooperative conditions, the reasoning behind the behavior of communication radiation sources plays an essential role in the fields of electronic reconnaissance and signal intelligence. This article first clarifies and summarizes the definition and classification of the behavior of communication radiation sources. It then elucidates the meaning of behavioral reasoning of communication radiation sources and further distinguishes the differences and connections between behavioral analysis and behavioral recognition. Subsequently, it reviews the key technologies of behavioral reasoning for communication radiation sources and compares the latest research based on clustering algorithms, classification algorithms, deep learning, and behavioral characteristics. Finally, it analyzes and forecasts the challenges and development of the technology for behavioral reasoning of communication radiation sources.

The clustering algorithm based on the minimum spanning tree (MST) can identify clusters of different shapes. However, the minimum spanning tree clustering algorithm has a high computational cost due to multiple rounds of traversing required during the construction and edge-breaking clustering phases. To address this issue, a density peak fusion idea is proposed, which utilizes kd-trees to obtain neighborhoods and designs leader nodes to propose an algorithm that reduces the time cost of the minimum spanning tree. The main idea of the algorithm is that nodes with higher density than surrounding nodes are used to replace the nodes participating in the formation of the minimum spanning tree. By using the selection technique of density peaks, the number of points required for initial construction and subsequent traversal of the minimum spanning tree is reduced, thereby reducing the complexity of the minimum spanning tree and achieving faster clustering speed. The algorithm has been tested on various synthetic and real datasets. It has achieved significant improvements in speed and clustering performance. Compared with the most advanced mst-based clustering algorithm, the algorithm has a speed increase of at least 30% and a performance increase of about 5%. [ABSTRACT FROM AUTHOR]

Parasitic diseases caused by Cysticercus tenuicollis significantly impact the livestock industry. The quantification of such impacts presents challenges due to the handling of data obtained through modern molecular techniques. This investigation explores the application of neutrosophic cluster analysis to the study of Cysticercus tenuicollis DNA. Neutrosophic clustering and the use of linguistic terms help interpret analyses, employing a five-step neutrosophic clustering algorithm: selection of the clustering algorithm, definition of distance metrics, data preparation, algorithm execution, and examination of results. Samples from 20 sheep exhibiting liver cysts were collected in Cotopaxi Province, Ecuador. The analysis resulted in a neutrosophic cluster where one group displayed low truth and falsity values but a moderate level of indeterminacy. The stability of the clusters was evaluated using the Adjusted Rand Index (ARI) with 100 bootstrapping repetitions, yielding a moderate value of approximately 0.52, which implies that the detected cluster structures are robust and representative of the underlying patterns in the data. The clusters show a clear differentiation based on the initial DNA concentration. Samples with high DNA concentrations tend to group together, suggesting that the quality and quantity of DNA may be critical indicators of the samples' condition and possibly the presence and state of the parasite. [ABSTRACT FROM AUTHOR]

In R16, 3GPP proposed that user devices could utilize the 4G/5G cellular networks to report the received signal strength indicator (RSSI) of Wi-Fi signal based on the existing minimization of drive test (MDT) technology, making it possible to measure the coverage probability of indoor Wi-Fi networks. However, existing measurement methods of network coverage probability based on the MDT require the spatial coordinates provided by the GPS. As GPS has poor indoor localization accuracy, it is not able to be applied to indoor Wi-Fi networks. A measurement method for network coverage probability based on clustering without spatial coordinates was proposed. The proposed method could distinguish RSSI measurement reported on different locations, with the fact that their statistics were similar at similar locations. The coverage probability was accurately measured by utilizing the clustering results without knowing the spatial coordinates. Experimental results show that the coverage probability measured by the proposed method is very close to the probability measured by the known spatial coordinates, and the accuracy is much higher than existing methods.

Fast and accurate analysis of the short-circuit characteristics of large wind farms has important engineering application value, and the short-circuit characteristics of wind farms under the influence of the wake effect vary greatly. Therefore, it is necessary to establish a wind farm short-circuit fault time equivalence model. A wind farm short-circuit fault dynamic equivalence method considering the effect of wake effect is proposed. First, the wake effect factor is defined to reflect the degree of the unit affected by the wake effect. Then, the wake effect factor is used as the grouping basis to reduce the variability of operating state of the units within the group under the influence of the wake effect. A positive- negative- zero-sequence network equivalence method is analyzed to improve the effectiveness of the equivalence model in asymmetric short-circuit faults. An equivalence method suitable for zero-sequence network is proposed and a platform is built for verification. The simulation results show that the dynamic short-circuit fault equivalence model proposed can accurately reflect the active and reactive short-circuit output characteristics of wind farms under the influence of the wake effect.

The immersed tunnel is considered an effective solution for traffic problems across rivers and seas. The sand filling layer, as an important part of immersed tunnel foundation treatments, directly affects segment attitude stability. Due to difficulties in quality control of concealed construction and the complex hydrodynamic environment, the sand filling layer is prone to compaction defects, further leading to changes in segment attitude. However, limited by structural concealment and state complexity, most studies consider the sand filling layer part of the foundation to study its impact on settlement while neglecting its influence on segment attitude. This research proposes an evaluation method for the sand filling layer state based on elastic wave testing and the elastic wave characteristic parameters selected come from analysis of the time domain, frequency domain and time–frequency domain. By classifying the elastic wave characteristic parameters through the K-means clustering method, the relationship between the state of the sand filling layer and the elastic wave characteristic parameters is established. The state of the sand filling layer is divided into dense, incompact, and void. A numerical model is established based on the Guangzhou BI-UT immersed tunnel with incompact and void sand filling layer states to simulate deformation and torsion. The results indicate that the settlement of the tunnel segment is low in the eastern region and high in the western region due to the presence of a less dense sand filling layer, with a maximum differential settlement of 0.04 m. The evaluation method plays a crucial role in guiding the construction of immersed tube tunnels.

Abstract Background The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications. Methods In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index. Results The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains. Conclusions The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.

In order to improve the efficiency of logistics distribution in urban environment, taking delivery time and delivery cost as optimization objectives, a mathematical model of “vehicle-drone”route optimization was established, and a third-order algorithm based on clustering-Floyd-genetic algorithm was proposed. The experimental results show that the algorithm can effectively reduce the computational load through multi-order data processing, and overcome the slow convergence rate of genetic algorithm, easy to fall into the local optimal problem. The sensitivity analysis of drone capacity shows that the delivery capability of drone increases significantly with the increase of load, and the simultaneous increase of load and maximum range can bring the delivery capability of drone into full play. Compared with the vehicle-only mode, the total cost and time of “ vehicle-drone” mode decrease by 36. 1% and 34. 5% respectively. It is proved that the algorithm has some practical value in city logistics distribution. [ABSTRACT FROM AUTHOR]

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses. [ABSTRACT FROM AUTHOR]

The application of computer information management system (IMS for short here) in university management faces problems such as incomplete system software and complex system design. Applying clustering algorithms (CA for short here) to computer student IMS can help optimize the system's overall effectiveness. This article constructed a computer student IMS based on computer technology and applied it to the management of college students. This article also combined CA to conduct relevant effectiveness tests on the system, in order to optimize the overall effectiveness of the system. Under the algorithm in this article, the average connection speed for each user accessing the system was 9.17 Mbps. The average reaction time was 0.34 seconds, the average security level was 92.47%, and the highest memory usage rate of the system was 34.27%; Under the decision tree algorithm, the average connection speed of each user accessing the system was 8.82 Mbps, and the average reaction time reached 0.64 s. The average security level was 88.41%, and the highest memory usage rate was 42.58%. Under the artificial neural network algorithm, the average connection speed of the system was 8.47 Mbps, the average response time was 0.86 s, and the highest memory usage rate was 45.97%. Analyzing the data reveals that the algorithm introduced in this paper significantly enhances system connection speed and reduces reaction time. This improvement not only enhances security measures but also minimizes memory usage, effectively optimizing the overall efficiency of the system. [ABSTRACT FROM AUTHOR]

The feasibility of using deep learning in ultrasound imaging to predict the ambulatory status of patients with Duchenne muscular dystrophy (DMD) was previously explored for the first time. The present study further used clustering algorithms for the texture reconstruction of ultrasound images of DMD data sets and analyzed the difference in echo intensity between disease stages. k -means (Kms) and fuzzy c -means (FCM) clustering algorithms were used to reconstruct the DMD data-set textures. Each image was reconstructed using seven texture-feature categories, six of which were used as the primary analysis items. The task of automatically identifying the ambulatory function and DMD severity was performed by establishing a machine-learning model. The experimental results indicated that the Gaussian Naïve Bayes and k -nearest neighbors classification models achieved an accuracy of 86.78% in ambulatory function classification. The decision-tree model achieved an identification accuracy of 83.80% in severity classification. A deep convolutional neural network model was established as the main structure of the deep-learning model while automatic auxiliary interpretation tasks of ambulatory function and severity were performed, and data augmentation was used to improve the recognition performance of the trained model. Both the visual geometry group (VGG)-16 and VGG-19 models achieved 98.53% accuracy in ambulatory-function classification. The VGG-19 model achieved 92.64% accuracy in severity classification. Regarding the overall results, the Kms and FCM clustering algorithms were used in this study to reconstruct the characteristic texture of the gastrocnemius muscle group in DMD, which was indeed helpful in quantitatively analyzing the deterioration of the gastrocnemius muscle group in patients with DMD at different stages. Subsequent combination of machine-learning and deep-learning technologies can automatically and accurately assist in identifying DMD symptoms and tracking DMD deterioration for long-term observation. [ABSTRACT FROM AUTHOR]

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications, thereby enhancing the detection of potential threats and attacks. Traditional fingerprint recognition methods, which rely on preannotated feature matching, face inherent limitations due to the ever-evolving nature and diverse landscape of web applications. In response to these challenges, this work proposes an innovative web application fingerprint recognition method founded on clustering techniques. The method involves extensive data collection from the Tranco List, employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction. The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm, eliminating the need for preannotated labels. By transforming web applications into feature vectors and leveraging clustering algorithms, our approach accurately categorizes diverse web applications, providing comprehensive and precise fingerprint recognition. The experimental results,which are obtained on a dataset featuring various web application types, affirm the efficacy of the method, demonstrating its ability to achieve high accuracy and broad coverage. This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage, offering a robust solution to the challenges of web application fingerprint recognition. [ABSTRACT FROM AUTHOR]

Traditional big data filling algorithms often give inaccurate results due to vulnerabilities to different data types. To solve this problem, this study presents a new big data clustering algorithm powered by AI technology in a cloud environment. The study proposes an advanced Big Data clustering algorithm that leverages AI technology in a cloud environment. It optimizes clustering based on predicted strength using parallel processing. The research focuses on optimizing the clustering algorithm based on the predicted intensity through parallel processing. Experimental results demonstrate that image clustering stability is achieved when the number of clusters exceeds 4, indicating reduced sensitivity to random factors. Although it was not possible to precisely determine the optimal number of clusters, the use of an optimization algorithm showed that at four clusters the prediction intensity reached its peak, ensuring more accurate cluster identification. Through rigorous testing, the optimal number of clusters was determined to be 4. Clustering results show that visitors characterized by certain attributes show higher interest in most columns. This algorithm makes it easier to cluster incomplete large data, improves clustering speed, and improves the accuracy of filling in missing data. Compared to existing methods, this algorithm leverages AI technology in the cloud environment to optimize clustering based on prediction intensity, providing improved accuracy and efficiency during processing in big data management. [ABSTRACT FROM AUTHOR]

The deep‐learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering‐based code search model (C‐DCS). C‐DCS uses the K‐Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C‐DCS first matches the query vector with the K center vectors to get the best matching center vector. After matching the center vector, C‐DCS matches the query vector with code vectors in the cluster corresponding to the best matching center vector one by one and then gets the best matching code snippet vector. To verify the efficiency of C‐DCS in the code search task, experimental analysis was built on a large dataset. The experimental results showed that C‐DCS saves 92.2% of the search time compared to the baseline model while remaining the accuracy. In the experimental evaluation section, we optimized the K‐Means algorithm to improve the code search efficiency of C‐DCS further, reducing the search time to 93.8% of the baseline model. Hence, C‐DCS reduces the code search time greatly with not affecting the accuracy, improving the efficiency of software development. [ABSTRACT FROM AUTHOR]

Traditional methods have the limitations of low accuracy and inconvenient operation in analyzing students’ abnormal behavior. Hence, a more intuitive, flexible, and user-friendly visualization tool is needed to help better understand students’ behavior data. In this study a visual analysis and interactive interface of students’ abnormal behavior based on a clustering algorithm were examined and designed. Firstly, this paper discusses the development of traditional methods for analyzing students’ abnormal behavior and visualization technology and discusses its limitations. Then, the K-means clustering algorithm is selected as the solution to find potential abnormal patterns and groups from students’ behaviors. By collecting a large number of students’ behavior data and preprocessing them to extract relevant features, a K-means clustering algorithm is applied to cluster the data and obtain the clustering results of students’ abnormal behaviors. To visually display the clustering results and help users analyze students’ abnormal behaviors, a visual analysis method and an interactive interface are designed to present the clustering results to users. The interactive functions are provided, such as screening, zooming in and out, and correlation analysis, to support users’ in-depth exploration and analysis of data. Finally, the experimental evaluation is carried out, and the effectiveness and practicability of the proposed method are verified by using big data to obtain real student behavior data. The experimental results show that this method can accurately detect and visualize students’ abnormal behaviors and provide intuitive analysis results. This paper makes full use of the advantages of big data to understand students’ behavior patterns more comprehensively and provides a new solution for students’ management and behavior analysis in the field of education. Future research can further expand and improve this method to adapt to more complex students’ behavior data and needs. [ABSTRACT FROM AUTHOR]

Nowadays, cluster analyses are widely used in mental health research to categorize student stress levels. However, conventional clustering methods experience challenges with large datasets and complex issues, such as converging to local optima and sensitivity to initial random states. To address these limitations, this research work introduces an Improved Grey Wolf Clustering Algorithm (iGWCA). This improved approach aims to adjust the convergence rate and mitigate the risk of being trapped in local optima. The iGWCA algorithm provides a balanced technique for exploration and exploitation phases, alongside a local search mechanism around the optimal solution. To assess its efficiency, the proposed algorithm is verified on two different datasets. The dataset-I comprises 1100 individuals obtained from the Kaggle database, while dataset-II is based on 824 individuals obtained from the Mendeley database. The results demonstrate the competence of iGWCA in classifying student stress levels. The algorithm outperforms other methods in terms of lower intra-cluster distances, obtaining a reduction rate of 1.48% compared to Grey Wolf Optimization (GWO), 8.69% compared to Mayfly Optimization (MOA), 8.45% compared to the Firefly Algorithm (FFO), 2.45% Particle Swarm Optimization (PSO), 3.65%, Hybrid Sine Cosine with Cuckoo search (HSCCS), 8.20%, Hybrid Firefly and Genetic Algorithm (FAGA) and 8.68% Gravitational Search Algorithm (GSA). This demonstrates the effectiveness of the proposed algorithm in minimizing intra-cluster distances, making it a better choice for student stress classification. This research contributes to the advancement of understanding and managing student well-being within academic communities by providing a robust tool for stress level classification. [ABSTRACT FROM AUTHOR]

Indoor positioning is a key technology in today's intelligent environments, and it plays a crucial role in many application areas. This paper proposed an unscented Kalman filter (UKF) based on the maximum correntropy criterion (MCC) instead of the minimummean square error criterion (MMSE). This innovative approach is applied to the loose coupling of the Inertial Navigation System (INS) and Ultra-Wideband (UWB). By introducing the maximum correntropy criterion, the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise, thus enhancing its adaptability to diverse environmental localization requirements. Particularly in the presence of non-Gaussian noise, especially heavy-tailed noise, the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF. The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform (UT) and then recharacterizes the measurement information using a nonlinear regression method at the cost of theMCC. Subsequently, the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations. Moreover, to mitigate the influence of non-line-of-sight (NLOS) errors positioning accuracy, this paper proposes a k-medoid clustering algorithm based on bisection k-means (Bikmeans). This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation. Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems. [ABSTRACT FROM AUTHOR]

Rural house is a fundamental component of rural settlements, and understanding its construction and development characteristics is crucial for rural land use and development planning. This paper focuses on the spatiotemporal characteristics and influencing factors of Rural Houses Construction Development (RHCD) from 1980 to 2019 with a case study of Qingyuan Town in China. Based on the literature review and filed research, a set of evaluation indicators for RHCD was established. The article calculates RHCD indicators from temporal and spatial dimensions, uses the location entropy method to demonstrate the spatial distribution of indicators, and classifies the RHCD type of 14 villages in Qingyuan Town using clustering algorithms. It also analyzes the influencing factors of spatiotemporal distribution. The results show that the RHCD in Qingyuan Town exhibits typical characteristics of mountainous areas and aligns with the development trends of rural society in China. Population growth, geographical location, and economic development are the primary driving factors for the quantity indicator (Qi), while economic growth, construction technology, industrial development, and policy adjustments are the key factors influencing the form indicator (Fi). In future policy-making, greater emphasis should be placed on optimizing development strategies, improving data and monitoring systems, and integrating administrative strength with actual development needs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: With the installation of advanced metering infrastructures, the operation data of EVs in the distribution networks can be obtained with time intervals of seconds and minutes. Based on these operation data, the impacts of integrating EVs into the distribution networks can be calculated and discussed. Methods: In this paper, an improved clustering algorithm with a new distance index for the daily curves of different types of EVs was proposed. The different types of EVs can be classified into several typical groups and the required number of operation scenarios can be reduced. After reducing the large-scale database to typical clusters, research can be conducted on the characteristics of EVs specific to certain scenarios. Results and discussion: In this way, the capability of integrating different types of EVs into the distribution network, such as fast EV charging stations, slow EV charging stations, and EV bus charging stations, is assessed from the perspective of load capacity size. The proposed clustering algorithm was verified with practical operation data. [ABSTRACT FROM AUTHOR]

Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence. [ABSTRACT FROM AUTHOR]

Background: The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications. Methods: In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index. Results: The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains. Conclusions: The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks. [ABSTRACT FROM AUTHOR]

In the digital era, particularly with the rise of online teaching, traditional approaches to college physical education face challenges in adequately monitoring and enhancing students' physical fitness. This study introduces a novel approach that integrates blockchain technology with a Machine Learning-IoT framework to evaluate and improve students' physical performance. Utilizing the Apriori algorithm, enhanced with particle swarm optimization and an improved K-means methodology, this system offers a robust tool for correlating student behavior with sports performance in a secure and decentralized manner. The proposed system uses blockchain for safe data management and IoT for real-time data collection, ensuring privacy as well as efficiency. The algorithm's accuracy, recall, and F1 values on the Iris dataset are 0.947, 0.931, and 0.928, respectively, with a considerable Calinski Harabasz score of more than 240. When applied to university student behavior data, the blockchain-enhanced system successfully mined association rules with a maximum confidence level of 0.923. [ABSTRACT FROM AUTHOR]

Copyright of Detergent & Cosmetics is the property of Detergent & Cosmetics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)