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One of the important technologies in present days is Intrusion detection technology. By using the machine learning techniques, researchers were developed different intrusion systems. But, the designed models toughness is affected by the two parameters, in that first one is, high network traffic imbalance in several categories, and another is, non-identical distribution is present in between the test set and training set in feature space. An artificial neural network (ANN) multi-level intrusion detection model with semi-supervised hierarchical k -means method (HSK-means) is presented in this paper. Error rate of intrusion detection is reduced by the ANN's accurate learning so it uses the Grasshopper Optimization Algorithm (GOA) which is analysed in this paper. Based on selection of important and useful parameters as bias and weight, error rate of intrusion detection system is reduced in the GOA algorithm and this is the main objective of the proposed system. Cluster based method is used in the pattern discovery module in order to find the unknown patterns. Here the test sample is treated as unlabelled unknown pattern or the known pattern. Proposed approach performance is evaluated by using the dataset as KDDCUP99. It is evident from the experimental findings that the projected model of GOA based semi supervised learning approach is better in terms of sensitivity, specificity and overall accuracy than the intrusion systems which are existed previously. [ABSTRACT FROM AUTHOR]

Limitations in instrument resolution mean that soil samples analysis using energy-dispersive X-ray fluorescence (EDXRF) often produced overlapping spectral peaks of heavy metals with similar energies. This overlapping of spectral peaks posed a significant problem in the quantitative analysis of heavy metals in soil samples. Therefore, it was necessary to decompose the overlapping peaks. In this paper, a new method for decomposing overlapping peaks was presented in the context of Gaussian Mixture Model (GMM) parameter estimation, combining the k-means algorithm and the African Vulture Optimization Algorithm (AVOA). The data acquired from EDXRF instruments were preprocessed before being input to the k-means algorithm, which produces the output used to initialize the parameters of the GMM. Subsequently, AVOA was employed to optimize the GMM parameters based on a fitness function derived from the data. Ultimately, the optimized GMM parameters obtained through the AVOA process yield the optimal solution for the decomposition of overlapping peaks. This approach effectively addressed the challenge of peak overlap in the determination of heavy metals in soil by EDXRF. Soil samples were obtained from national standard samples, peach gardens, and vegetable-growing regions. The results showed that the maximum absolute errors for the peak channel and peak area were 1.46% and 6.37%, respectively. A comparison of the proposed model with others models indicated that the k-means-GMM-AVOA model exhibits the best performance in terms of speed, accuracy, and stability. [ABSTRACT FROM AUTHOR]

In this paper, we propose a lossless encryption method based on dual images. A cosine perturbation is added to the generalized voltage-controlled memristor, thus constituting a generalized voltage-controlled cosine memristor. And the characteristics of the memristor are analyzed and introduced into a four-dimensional cellular neural network, thus constituting the memristor-based four-dimensional cellular neural network (M4DCNN). The parameters of the M4DCNN are adjusted to achieve a hyperchaotic state for the diffusion and scrambling stages of the image. On the key, the partial key is obtained by K-means algorithm to make the key more random. In the scrambling phase, perturbed logistic chaotic map is introduced in this paper, which makes the image pixels better satisfy the avalanche effect. Through security analysis and comparison with other encryption methods, it is found that the encryption method shows to have good encryption effect in various indexes such as key sensitivity, key space, pixel correlation, information entropy, resistance to shear attack, resistance to noise attack, UACI and NPCR analysis. [ABSTRACT FROM AUTHOR]

Social media is a platform for sharing various hashtags, news, and posts within the community. Increasing text content and information to read and understand is difficult because of the vast definitions. At present, every business must analyze data in social media. Social media platforms are where people worldwide discuss and share social commentary. Automated text summarization is crucial for condensing lengthy contents into concise ones using learning concepts. The unstructured data in social media are significant phrases with support from sources for analyzing sentiments and extracting the importance of the content. Previously, all data were analyzed as related content similarity matches. However, the main drawback is that data needs to be examined to rephrase or summarize the essential terms of extraction. Due to improper content extraction, the accuracy gets poor in precision level to increase the false content rate. To tackle these problems, in this paper presents a Machine Learning (ML) intelligence algorithm with Diverse Beam Search-Based Maximum Mutual Information (DBSMMI) and Ant Colony Optimization (ACO)-optimized Deep Belief Network Based Text Summarization (DBNTS). Initially, the COVID-19 Twitter dataset is preprocessed to remove noise and create a Pheromone value set based on the k-means semantic similarity algorithm. Our work analyzes and clusters the data according to their theme (area). Data analysis is the central concept and is performed using WordNet keyword matching and semantic matching of the words. Then, the similar word is clustered using a semantic similarity-based k-means clustering algorithm. Using DBSMMI to make identical content phrases maximum supports term sentence extraction. The maximum support clustered group is optimized for the respective theme using ant colony optimization with the DBNTS algorithm. The algorithm's efficiency can be tested with an existing classifier algorithms. The ACO semantic recommender system is implemented in this article to recommend relevant news to the Twitter user. The proposed simulation attains the 92.35% of accuracy, and 90.29% of precision performance. The proposed method efficiently improves classification accuracy, and precision performance compared to other methods. [ABSTRACT FROM AUTHOR]

ABSTRACT The cooperative localization technology of multi‐AUV has been a research hotspot in the area of underwater localization in recent years. Aiming at the situation that the localization accuracy is reduced and diverging due to uncertain information such as outliers and unknown time‐varying noise in the measurement information in the dynamic topology environment, a new measurement filtering module is designed by using factor graphs. First, the K‐means algorithm is introduced based on the cooperative localization algorithm based on factor graphs in the dynamic environment, and an outlier filtering module is designed to filter measurement outliers. After outlier correction, the EM algorithm is introduced to resolve the issue of unknown measurement noise variance caused by unknown time‐varying noise and design an adaptive filtering module. Finally, to address the issue of degraded real‐time performance due to a large number of system communication nodes and diverse quality, the paper introduces the CRLB algorithm, distance evaluation factor, and measurement smoothing factor to design a node optimization module. Finally, these modules are integrated into the cooperative localization system through factor graphs. Through experimental analysis, the proposed algorithm effectively reduces outliers and unknown time‐varying noise in measurement information, as well as the impact of a large number of system nodes on localization accuracy and real‐time performance. In addition, the proposed algorithm also shows strong robustness in the face of extreme underwater environments. [ABSTRACT FROM AUTHOR]

Demand response (DR) can ensure electricity supply security by shifting or shedding loads, which plays an important role in a power system with a high proportion of renewable energy sources. Industrial loads are vital participants in DR, but it is difficult to assess DR potential because of many complex factors. In this paper, a new method based on fuzzy control is given to assess the DR potential of industrial loads. A complete assessment framework including four steps is presented. Firstly, the industrial load data are preprocessed to mitigate the influence of noisy and transmission losses, and then the K-means algorithm considering the optimal cluster number is used to calculate baseline load of industrial load. Subsequently, an open-loop fuzzy controller is designed to predict the response factor of different industrial loads. Three strongly correlated indicators, namely peak load rate, electricity intensity, and load flexibility, are selected as the input of fuzzy control, which represents response willingness. Finally, the baseline load of diverse clustering scenarios and the response factor are used to calculate the DR potential of different industrial loads. The proposed method takes into account both economic and technical factors comprehensively, and thus, the results better represent the available DR potential in real-world situations. To demonstrate the effectiveness of the proposed method, the case of a medium-sized city in China is studied. The simulation focuses on the top eight industrial types, and the results show they can contribute about 189 MW available DR potential. [ABSTRACT FROM AUTHOR]

Higher lighting intensity promotes workers' productivity but few studies focus on the problems caused by lower lighting intensities at tunnel construction sites without natural light. The purpose of this study is to explore the relationship between light intensity and workers' sleep quality, alertness, vitality, and work productivity through a theoretical structural equation model based on the literature review. Data were collected through a questionnaire survey, and 5792 valid responses were adopted for the analysis. First, the results showed that greater lighting intensity promoted workers' productivity directly and indirectly through three mediators: sleep quality, alertness, and vitality. Then, the whole sample was classified into four groups: high intensity/high comfort, moderate intensity/moderate comfort, moderate intensity/low comfort, and low intensity/low comfort. The clustered results showed that the lighting conditions of tunnel construction sites were generally poor and that lighting comfort promoted workers' productivity to some extent. Besides, the influence of lighting intensity on productivity declined with improved lighting conditions while the impact of lighting intensity on workers' physiological and psychological status showed differing trends as lighting conditions varied. However, the relationships between workers' physiological and psychological status and productivity remained stable regardless of the lighting conditions. The findings could provide a reference for developing corresponding measures to promote workers' productivity. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of objectively determining a website's personality by developing a methodology based on automated quantitative analysis, thus avoiding the biases inherent in human surveys. Utilizing a database of 3000 websites, data extraction tools gather relevant data, which are then analyzed using Artificial Intelligence (AI) techniques, including machine learning (ML) and natural language processing. Four ML algorithms—K-means, Expectation Maximization, Hierarchical Agglomerative Clustering, and DBSCAN—are implemented to assess and classify website personality traits. Each algorithm's strengths and weaknesses are evaluated in terms of data organization, cluster flexibility, and handling of outliers. A software tool is developed to facilitate the research process, from database creation and data extraction to ML application and results analysis. Experimental validation, conducted with identical training and testing datasets, achieves a success rate of up to 94% (with an Error of ≤ 50 % ) in accurately identifying website personality, which is validated by subsequent surveys. The research highlights significant relationships between website attributes and personality traits, offering practical applications for website developers. For instance, developers can use these insights to design websites that align with business goals, enhance customer engagement, and foster brand loyalty. Additionally, the methodology can be applied to creating culturally resonant websites, thus supporting New Zealand's cultural initiatives and promoting cross-cultural understanding. This research lays the groundwork for future studies and has broad applicability across various domains, demonstrating the potential for automated, unbiased website personality classification. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity of land surface temperature (LST) within cities is profoundly influenced by the built environment. Although significant progress has been made in the study of the urban thermal environment, there is still a lack of research on how the pattern and structural layout of the built environment affects the thermal environment. In this study, we take the Fifth Ring Road of Beijing as an example, invert the urban LST on the basis of multisource spatial data, characterize the built environment, and use k-means cluster analysis to investigate the main influencing factors of the LST in different functional areas and building patterns within the city, as well as the spatial relationship between the built environment and the urban LST. The results show the following: (1) The urban heat island (UHI) effect occurs to varying degrees over a large part of the study area, and these UHI areas are mainly concentrated in the southwestern part of the city, forming a large contiguous area between the second and fifth ring roads. (2) Class 1 is dominated by transport blocks, Class 3 is dominated by commercial blocks, and Class 5 is dominated by green space blocks, with a clustering index of 0.38. (3) The high-density, high-height class (HH-Class 2) has a greater number of blocks distributed in a ring shape around the periphery of the second ring road. The high-density, low-height class (HL-Class 2) has a relatively small number of blocks but a relatively large area, and the largest blocks are located in the western part of the study area. (4) In the HH and HL building patterns, extreme heat scenarios often occur; from the perspective of functional areas, the probability of extreme heat in the transport block is much higher than that of other functional areas, and except for the HH scenario, the green space functional area plays a very important role in reducing the temperature. This study explores the characteristics of the built environment that influence the urban LST from the perspective of different urban functional zones in cities to provide decision support for quantitative territorial spatial planning, optimization, and management. [ABSTRACT FROM AUTHOR]

Topic detection is an information processing technology designed to help people deal with the growing problem of data information on the Internet. In the research literature, topic detection methods are used for topic classification through word embedding, supervised-based and unsupervised-based approaches. However, most methods for topic detection only address the problem of clustering and do not focus on the problem of topic detection accuracy reduction due to the cohesiveness of topics. Also, the sequence of biterm during topic detection can cause substantial deviations in the detected topic content. To solve the above problems, this paper proposes a topic detection method based on KM-LSH fusion algorithm and improved BTM model. KM-LSH fusion algorithm is a fusion algorithm that combines K-means clustering and LSH refinement clustering. The proposed method can solve the problem of cohesiveness of topic detection, and the improved BTM model can solve the influence of the sequence of biterm on topic detection. First, the text vector is constructed by processing the collected set of microblog texts using text preprocessing methods. Secondly, the KM-LSH fusion algorithm is used to calculate text similarity and perform topic clustering and refinement. Finally, the improved BTM model is used to model the texts, which is combined with the word position and the improved TF-IDF weight calculation algorithm to adjust the microblogging texts in clustering. The experiment results indicate that the proposed KM-LSH-IBTM method improves the evaluation indexes compared with the other three topic detection methods. In conclusion, the proposed KM-LSH-IBTM method promotes the processing capability of topic detection in terms of cohesiveness and the sequence of biterm. [ABSTRACT FROM AUTHOR]

Conventional approaches fall short of managing the current scale of data, underscoring the significance of big data analytics in the field of data science. The study highlights the importance of big data analytics in data science, focusing on sentiment analysis and developing a machine learning framework for identifying sentiments in product reviews and assessing Massive Open Online Courses (MOOCs). Online education proved its value to the academic community during the COVID-19 pandemic, when much of the world was at a standstill. Since the coronavirus outbreak, Massive Open Online Courses (MOOCs) have taken over many public schools and undergraduate degree programs as the new norm. Nowadays, online courses are being incorporated into traditional educational programs. Therefore, it has become crucial to develop a framework that both government and nongovernment organizations could use to assess public opinion before implementing online education programs. This article offers a methodology for utilizing deep learning and natural language processing techniques to examine public sentiments toward online education and courses such as MOOCs. Public reviews from Coursera and Udemy are utilized to assess the quality of online courses. Using sentiment analysis, the proposed methodology employs the Bidirectional Encoder Representations from Transformers (BERT) model to classify public reviews into positive and negative categories. Positive and negative reviews are clustered using the k-means algorithm to evaluate the quality of MOOCs. The study's findings indicate that MOOCs with qualified professors are beneficial for learning basic concepts. But they may lack advanced knowledge, real-world experience, and enough examples. These courses are lengthy, and students request more questions after lessons to comprehend the concept. The proposed framework assists in assessing public opinion before implementing online education and may provide valuable insight to the developers of the courses. [ABSTRACT FROM AUTHOR]

This study presents short-term electricity load forecasting for the New England area by processing initial data through correlation assessment and data clustering. This method is combined with artificial neural networks to improve accuracy and forecast performance. Data preprocessing focuses on two main issues: identifying correlations between variables to eliminate less relevant factors and retaining highly correlated variables to reduce noise, as well as reducing the data sample size before training the neural network. This evaluation aims to determine the factors that have a significant impact on electricity load. These factors can include previous load values, weather conditions, time, types of electricity usage, and others. This technique ensures that reducing the size in both dimensions of the large dataset does not result in the loss of critical information, maintaining the accuracy of computational programs and the performance of neural network training at high levels. The neural network is trained to classify and cluster data based on previously identified correlated characteristics. As a result, the forecasting model can make more accurate predictions about future electricity loads. Experimental results show that the proposed method achieved more than 97 % accuracy, outperforming traditional methods in both speed and load forecasting accuracy. The new dataset had 63% fewer samples compared to the initial dataset. [ABSTRACT FROM AUTHOR]

The particle swarm optimization-K-Means algorithm is proposed by the related researchers to improve the clustering accuracy of the K-Means algorithm. However, the particle swarm optimization-K-Means algorithm brings more burden to the computation, and the computational efficiency is low when dealing with large data sets. To solve this problem, a parallel particle swarm K-Means algorithm based on MapReduce with multi-threading is proposed. The algorithm performs parallel computation by dividing the particle swarm into several equal-sized sub-populations based on the number of available nodes in the cluster and distributing them to each node. It uses a multi-threaded execution in the evaluation stage, which has the highest computational complexity in the evolutionary process. Experiments show that although splitting the population will affect the optimization effect to some extent, the proposed still can effectively optimize the clustering results of the K-Means algorithm, and the computational efficiency is significantly improved compared with serial particle swarm optimization k-means algorithm and MapReduce-based non-multithreaded particle swarm optimization k-means algorithm, in the experiment with the largest dataset and a configuration of 16 nodes, the proposed algorithm is 58 times faster than the serial algorithm. Furthermore, the computing efficiency can be improved in the clusters with more CPU cores. [ABSTRACT FROM AUTHOR]

Information plays a crucial role in the sustainability of company operations. The development of information technology, especially in the industry 4.0 era, affects various fields including economics, social, and education. The company faces challenges in declining motorcycle sales due to intense competition and ineffective customer segmentation. To address these issues, this study proposes the use of the K-Means algorithm with Python tools for better customer segmentation. The study aims to identify diverse customer groups and tailor marketing strategies accordingly. By utilizing the Elbow method and Silhouette score, the analysis of customer data is simplified. This study also employs data mining techniques to uncover hidden patterns in motorcycle sales data, aiding companies in improving operational efficiency and decision-making.

Accurate indoor robot navigation cannot be achieved without reliable indoor positioning techniques. Ultra-wideband (UWB) is among the most dependable techniques currently available. However, the complexity of indoor environments results in signal transmissions that are susceptible to interference from obstacles, which in turn reduces positioning accuracy. Inertial Navigation System (INS) is an autonomous navigation system that is free from interference in indoor environments and unaffected by non-line-of-sight (NLOS) conditions. This paper proposes a new joint INS and UWB positioning method utilizing the Maximum Correntropy Criterion Kalman Filter (MCCKF). This approach effectively cope with the interference of measurement outliers and extend the design of the adaptive mechanisms to enhance the performance of the localization system. For UWB positioning of tag nodes, an improved Particle Swarm Optimization combined with kmeans (PSO-kmeans) method is used to reduce the impact of NLOS errors on positioning. Finally, the INS is calibrated by AMCCKF fused positioning results. The results of simulations and experiments demonstrate that the proposed AMCCKF fusion algorithm effectively suppresses the impact of anomalous measurements, enhances positioning accuracy and robustness, thereby improving its practicality in real-world environments.

Abstract Background Over the last two decades, tumor-derived RNA expression signatures have been developed for the two most commonly diagnosed tumors worldwide, namely prostate and breast tumors, in order to improve both outcome prediction and treatment decision-making. In this context, molecular signatures gained by main components of the tumor microenvironment, such as cancer-associated fibroblasts (CAFs), have been explored as prognostic and therapeutic tools. Nevertheless, a deeper understanding of the significance of CAFs-related gene signatures in breast and prostate cancers still remains to be disclosed. Methods RNA sequencing technology (RNA-seq) was employed to profile and compare the transcriptome of CAFs isolated from patients affected by breast and prostate tumors. The differentially expressed genes (DEGs) characterizing breast and prostate CAFs were intersected with data from public datasets derived from bulk RNA-seq profiles of breast and prostate tumor patients. Pathway enrichment analyses allowed us to appreciate the biological significance of the DEGs. K-means clustering was applied to construct CAFs-related gene signatures specific for breast and prostate cancer and to stratify independent cohorts of patients into high and low gene expression clusters. Kaplan-Meier survival curves and log-rank tests were employed to predict differences in the outcome parameters of the clusters of patients. Decision-tree analysis was used to validate the clustering results and boosting calculations were then employed to improve the results obtained by the decision-tree algorithm. Results Data obtained in breast CAFs allowed us to assess a signature that includes 8 genes (ITGA11, THBS1, FN1, EMP1, ITGA2, FYN, SPP1, and EMP2) belonging to pro-metastatic signaling routes, such as the focal adhesion pathway. Survival analyses indicated that the cluster of breast cancer patients showing a high expression of the aforementioned genes displays worse clinical outcomes. Next, we identified a prostate CAFs-related signature that includes 11 genes (IL13RA2, GDF7, IL33, CXCL1, TNFRSF19, CXCL6, LIFR, CXCL5, IL7, TSLP, and TNFSF15) associated with immune responses. A low expression of these genes was predictive of poor survival rates in prostate cancer patients. The results obtained were significantly validated through a two-step approach, based on unsupervised (clustering) and supervised (classification) learning techniques, showing a high prediction accuracy (≥ 90%) in independent RNA-seq cohorts. Conclusion We identified a huge heterogeneity in the transcriptional profile of CAFs derived from breast and prostate tumors. Of note, the two novel CAFs-related gene signatures might be considered as reliable prognostic indicators and valuable biomarkers for a better management of breast and prostate cancer patients.

Due to the uncertainty and information delay of disaster situations, emergency relief material distribution and vehicle scheduling often face complex environments where demand point information dynamically changes during the distribution process. It is particularly important to quickly respond and accurately and efficiently achieve emergency material rescheduling in a dynamically changing environment. A dynamic emergency logistics model was established for emergency supplies in a complex environment with dynamic changes in demand point information, and a dynamic optimization algorithm was proposed based on cluster-distribution strategy. Corresponding route adjustment strategies were designed to ensure the feasibility of the plan. Finally, genetic algorithm was used to solve the problem. The numerical experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgroup, and BBC sports datasets, and then pre-processing is established by stemming and stop word removal to remove unwanted phrases or words. The data imbalance approach is established using an adaptive synthetic sampling algorithm (ADASYN), then term frequencyinverse document frequency (TD-IDF) and WordNet features are employed for extracting features. Finally, MOA with the K-means technique is utilized for TDC. The proposed approach achieves better accuracy of 99.75%, 99.54%, and 98.24% when compared to the existing techniques like fuzzy rough set-based robust nearest neighbor-convolutional neural network (FRS-RNN-CNN), TopicStriker, Modsup-based frequent itemset, and rider optimization-based moth search algorithm (Modsup-Rn-MSA), hierarchical dirichlet-multinomial mixture, and multi-view clustering via consistent and specific non-negative matrix (MCCS). [ABSTRACT FROM AUTHOR]

Data mining is vital in analyzing large volumes of data to extract functional patterns and knowledge hidden within the data. Data mining has practical applications in various scientific areas, such as social networks, healthcare, and finance. It is important to note that data mining also raises ethical concerns and privacy considerations. Organizations must handle data responsibly, ensuring compliance with legal and ethical guidelines. Privacy-Preserving Data Mining (PPDM) refers to conducting data mining tasks while protecting the privacy of sensitive data. PPDM techniques aim to strike a balance between privacy protection and data utility. By employing PPDM techniques, organizations can perform safe and private data analysis, protecting sensitive information while deriving valuable insights from the data. The current paper uses geometric transformation-based projection techniques such as perspective projection, isometric projection, cabinet projection, and cavalier projection to protect data privacy and improve data utility. The suggested technique's performance was assessed with the K-means clustering technique. The UCI repository's Bank Marketing dataset was used to verify the error rate of the proposed projection techniques. [ABSTRACT FROM AUTHOR]

The traditional k-means algorithm is widely used in large-scale data clustering because of its easy implementation and efficient process, but it also suffers from the disadvantages of local optimality and poor robustness. In this study, a Csk-means algorithm based on contour similarity is proposed to overcome the drawbacks of the traditional k-means algorithm. For the traditional k-means algorithm, which results in local optimality due to the influence of outliers or noisy data and random selection of the initial clustering centers, the Csk-means algorithm overcomes both drawbacks by combining data lattice transformation and dissimilar interpolation. In particular, the Csk-means algorithm employs Fisher optimal partitioning of the similarity vectors between samples for the process of determining the number of clusters. To improve the robustness of the k-means algorithm to the shape of the clusters, the Csk-means algorithm utilizes contour similarity to compute the similarity between samples during the clustering process. Experimental results show that the Csk-means algorithm provides better clustering results than the traditional k-means algorithm and other comparative algorithms. [ABSTRACT FROM AUTHOR]

Object tracking application is one of the important as well as challenging application of energy constrained Wireless Sensor Network (WSN).Timely and accurate action is required whenever the presence of object is sensed in the area of interest (AOI).This requires a lot of data collection from surrounding environment, routing of these data to base station (BS) and data processing. Large volume of data processing challenges the long survival of the WSN. In other way, expecting accurate tracking result with less data is quite impossible. Early exhaustion of node energy leads to early node death which affects the tracking quality. Therefore, it is required to maintain the accuracy in tracking result while not affecting the longevity of the network. Use of heterogeneous sensor nodes and cluster architecture of the network are two of the many factors that can enhance the lifetime of the network to some extent. Lifetime of the network can be further enhanced using optimum data routing procedure. This paper proposes a novel swarm intelligence based routing algorithm called HGWO-Firefly algorithm for routing of information to BS in a WSN with cluster architecture. Proposed algorithm consists of two broad steps. In first step, potential cluster head (CH) selection is done using hybrid K-means and Grey Wolf Optimization (GWO) algorithm. In second step, selection of energy efficient route in between BS and sensor node is done using Firefly algorithm. When the performance of the proposed algorithm is compared with existing PSO-based routing algorithm and FIGWO algorithm, it is found that the performance of the proposed algorithm is better. [ABSTRACT FROM AUTHOR]

The massive internet and web data are increasing day-by-day rapidly, so searching an important information from huge data is a tedious task. Relevant document retrieval for polysemy queries is a primary issue. This paper addressed the polysemy issue for information retrieval and proposed an intelligent FK Hybrid Model. The model used clustering as a tool to group search results into a coherent group that may help the user in effective and efficient search result during browsing. We utilize the traditional clustering algorithm's capabilities and integrate a nature-inspired approach. The FK Hybrid Model combines the Firefly algorithm and the K-means clustering algorithm. In a series of experiments performed on AMBIENT data, the suggested model significantly outperforms to the related methods.. The experimental results show that our model can locate appropriate clustering solutions with the correct number of clusters. [ABSTRACT FROM AUTHOR]

The high-speed serial interface chip is an important IP in high-performance interconnect network communication. This paper proposes a DSP design for 56 Gb/s high-speed Serdes receivers, in response to the problem of high bit error rate caused by severe channel attenuation over long transmission distances in high-performance interconnect network backplane communication using 56 Gb/s four pulse amplitude modulation (PAM4) signals. The DSP adopts a 64-channel parallel structure and processes the digitized signal from the receiver through a 16-Tap feed forward equalizer (FFE) and a decision feedback equalizer (DFE). By using the K-means clustering algorithm to generate dynamically changing DFE decision levels and combining it with the least mean square (LMS) algorithm, it can handle the equalization problem under different channel attenuation of 15-35 dB. To verify the performance of the algorithm, an experimental verification platform based on analog frontend chips and field programmable gate arrays (FPGA) was constructed. The experimental results indicate that the channel attenuation is 15-35 dB@14 GHz at a speed of 56 Gb/s, the error rate is less than 5e-10. [ABSTRACT FROM AUTHOR]

Internet of things technology can not only improve people's lives, but also bring great changes and innovations to the industry and promote the rapid development of economy. The future communication system will be a system of everything connected with IoT. There will be various types of user equipment in the system. The standards adopted by different types of equipment are different, and the required business levels are also different. This will bring great challenges to the resource allocation of the system. Artificial intelligence algorithms provide technical support for future intelligent wireless systems. The system can select appropriate artificial intelligence algorithms according to different application scenarios and establish more accurate mathematical models to serve the network. According to the business priority analysis of user equipment under the wide area Internet of things, considering the user scheduling as the constraint condition, a high-altitude platform user equipment scheduling model based on artificial intelligence k-means algorithm under the user scheduling constraint condition is established. Based on the user equipment scheduling model of high-altitude platform based on artificial intelligence k-means algorithm, a two-stage K-means improved algorithm is proposed to cluster the user equipment of wide area Internet of things, which is divided into preprocessing training stage and K-means algorithm clustering stage. The initial center value is not randomly selected. In the preprocessing training stage, the scheduling priority of each user is obtained according to the channel environment and packet length of the user equipment. The first k user equipment with the same scheduling priority is used as the initial clustering points to complete the preprocessing training. Then, K-means algorithm is used to cluster the new user equipment to be scheduled until convergence. The intelligent scheduling of user equipment under the wide area Internet of things is realized. [ABSTRACT FROM AUTHOR]

The online collaborative learning platform for physical education is an interactive and open physical education teaching mode. To improve students' learning interest and efficiency, the online sports collaborative learning platform is designed. From the perspective of person-post matching, the role in the group is designed and the improved clustering algorithm is used to realize the grouping. The combination of the k-mean algorithm and the firefly algorithm is used to enhance the real-time and accuracy of learning resource recommendation. The outcomes demonstrated that the Firefly algorithm had obvious advantages in convergence speed and other aspects. Relative to the classical K-means algorithm and the Firefly algorithm, the average clustering accuracy of the presented algorithm was improved by 7.23 % as well as 2.18 %, and the average processing time was improved by 4.35 % and 2.26 %, respectively. In the dataset Iris, the average clustering accuracy and processing time were 91.29 and 8.65, respectively. The optimal, worst, and average values of the online collaborative learning platform on the ground of the firefly-optimized K-means algorithm were 0.3006, 3.2176, and 1.5234, respectively. The fusion algorithm proposed in this study can optimize the recommendation of teaching resources on sports online collaborative learning platforms, improve learners' learning passion, learning efficiency, and satisfaction, and relieve teachers' teaching pressure. [ABSTRACT FROM AUTHOR]

Topic extraction and categorization is an important task because by doing that it is easy to find out which are the topics most discussed by the users in their tweets or opinions and need to be analyzed. In this work, topics are extracted from positive and negative opinions and then categorized into different groups. For performing this, first a collection of opinions is divided into two sets- positive opinions and negative opinions by using a sentiment analyzer. Then a method is proposed to find out the most discussed topics in the set of positive opinions and negative opinions. For extracting the topics from a set of opinions the noun words are extracted from the set of the opinions. After extracting the topics, the similar topics have been combined by using synonymy relation. Then the frequent topic words are represented with the help of GloVe embedding technique. Finally, the topics are categorized by using a clustering algorithm by applying it on the frequent topic words. For the evaluation of the proposed method, tweets from a Twitter User dataset are used. The results obtained from the experiments by applying the proposed method on the dataset give promising result and provide interesting and meaningful clusters of topics. Moreover, an analysis of the result obtained for both positive and negative opinions is also presente [ABSTRACT FROM AUTHOR]

Objectives: The side-scan sonar shipwreck detection method based on the YOLOv3 model has the problems of high miss-alarm rate of small targets, heavy model weight, and slow detection speed that fails to meet real-time requirements. Methods: This paper introduces the YOLOv5 algorithm and proposes an improved YOLOv5 model according to the characteristics of the side-scan sonar shipwreck dateset. We test YOLOv5a, YOLOv5b, YOLOv5c, YOLOv5d, YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x under the basic framework of YOLOv5 with eight different depth and width model structures. Then we choose the best structure by using genetic algorithm and K-means algorithm to optimize the detection frame, and to improve the loss function through complete intersection over union. Results: The results show that under the different range of intersection over union as 0.5 and 0.5-0.95,the average precisions of the improved YOLOv5a model are increased by about 0.3% and 0.6% than that of the original model,respectively. Compared with the YOLOv3 model, the average precisions of the improved YOLOv5a model are increased by 4.2% and 6.1%, respectively, and the detection speed reaches 426 frames per second which is almost doubled that of YOLOv3. Conclusions: The proposed method is more conducive to practical applications and engineering deployment. [ABSTRACT FROM AUTHOR]

This article presents techniques used to observe the shear behaviour of modified Z-shaped concrete specimens, experimental results, and a method proposed to classify the shear cracking pattern of a concrete bridge based on acoustic emission (AE). Z-shaped specimens under shear were designed with steel plate reinforcement. A finite element (FE) model for this specimen was developed to validate the concrete crack propagation. AE parameters, including the peak amplitude, counts, cumulative energy, average frequency (AF) and rise angle (RA), were found to successfully correlate with the observed cracking process in the specimens. A cluster analysis based on the K-means algorithm was proposed to automatically classify the AE signal into tensile and shear clusters. An existing bridge underwent reinforcement and then testing under two test scenarios. AE signals were detected at the web of the bridge under heavy truck loads or full random traffic flows. Using the proposed crack classification showed that the signals from the bridge belonged to shear crack clusters characterized by lower AE energy. Thus, this work developed an AE method to classify cracks in concrete shear tests and extended the method to classify the shear cracks in a bridge to provide an early warning of future failure. [ABSTRACT FROM AUTHOR]

Energy consumption in 5G base stations remains consistently high, even during periods of low traffic loads, thereby resulting in unnecessary inefficiencies. To address this problem, this paper presents a novel approach by proposing a convolutional neural network (CNN)-long short-term memory (LSTM) traffic prediction model with a dual attention mechanism, coupled with the particle swarm optimization k-means algorithm for intelligent switch timing. The proposed CNN-LSTM model leverages a dual channel attention mechanism to bolster key feature information for long-term traffic data predictions. Specifically, a temporal attention mechanism is added to the LSTM to enhance the importance of temporal information. Moreover, the particle swarm optimization K-Means algorithm is proposed in order to cluster the traffic prediction results, output the corresponding time points of the lower traffic value and to obtain the optimal switch-off periods of the base station. Extensive experiments across multiple base stations over an extended period of time have validated our approach. The results show that this method offers accurate traffic prediction with minimal average errors in traffic prediction and the on/off timings of the base stations are in line with the "tide effect" of traffic, thereby achieving the goal of energy savings. [ABSTRACT FROM AUTHOR]

Our study aims to develop more energy-efficient mobile communication systems through the exploration of the 6th generation (6G) technology that is expected to be implemented in 2033. We focus on the impact of device-todevice (D2D) communication on power efficiency, which is a crucial need in this domain. To achieve this, we conducted a pioneering experiment using an in-house testbed and K-means clustering to classify locations as D2D enabled or disabled. Our findings show that there is a dynamic clustering mechanism that enables certain nodes to sustain D2D functionality around temporary base stations, resulting in a remarkable 5% improvement in network lifetime per second. This research not only enhances our understanding of 6G networks but also provides a practical methodology for optimizing energy consumption, which holds significant implications for society in advancing sustainable and efficient communication. [ABSTRACT FROM AUTHOR]

In this paper, a method and fresh results associated with medical image segmentation of brain Magnetic Resonance Imaging (MRI) scans are presented. Gray-converted segmentation and Genetic Algorithm (GA) are utilized along with unsupervised k-means classification. The image segmentation employed indicates the tissue type or the anatomical structure of each pixel. The cluster centroid initialization is performed by GA. GA offers efficient search processes (selection, crossover, and mutation), suited to determine global optima regarding centroid problems. As a result, this research offers more accurate, reliable, and efficient image segmentation for MRI, by improving the k-means algorithm with GA. The results indicate that the accuracy obtained from the proposed method is at least 3.5% higher than the PSO algorithm in this matter. [ABSTRACT FROM AUTHOR]

A reduction in the James–Stein shrinkage estimator might significantly increase the accuracy of cluster analysis of k-means for a relatively broad range of data. The efficiency of using the James–Stein shrinkage estimator for automatically grouping industrial products in homogeneous production batches is considered. Tests are conducted for batches of integrated circuits by comparing the shrinkage results with those obtained using the traditional k-means algorithm. The dataset is normalized according to the values of the acceptable drift, acceptable parameters, and standard deviation. As established using the Rand index, clustering is far more accurate in the automatic grouping of industrial products in homogeneous production batches, when average values of inconclusive parameters drop to zero. It is established that the reduction of the James–Stein shrinkage estimator decreases the influence of inconclusive parameters of standard data to acceptable values. [ABSTRACT FROM AUTHOR]

The bread industry faces significant risks of losses in case of excess inventory. The initial stage in the K-Means Clustering Algorithm involves forming two clusters: C1 for slow-moving product data and C2 for fast-moving. Clustering products using the K-Means Algorithm resulted in Group 1 as slow-moving products with 44 types of items and Group 2 as fast-moving products with 15 types of items. It can be concluded that the bakery is experiencing losses due to an excess of overstocked products. After categorizing data into slow-moving and fast-moving groups, the subsequent phase involves employing the FP (Frequent Pattern)-Growth association rule algorithm to recognize consumer purchasing patterns. This algorithm aims to uncover relationships between items in a dataset and assess the probability of a person purchasing bread concurrently. By establishing a minimum support of 3% and a minimum confidence level of 30%, a total of 13 rules were generated, meeting the criteria for strong association rules. With this data, the store owner can specifically enhance inventory planning for fast-moving products by analyzing demand data and market trends. For slow-moving products, the store owner can adjust item placement or create product bundling with best seller items. [ABSTRACT FROM AUTHOR]

In the higher education system, college counselors play a crucial role. They are not only guides for student growth, but also the backbone of student ideological and political education and daily management. However, with the changes of the times and the diversification of student needs, the competence of college counselors is facing unprecedented challenges. Among them, student evaluation, as a direct way to reflect the eff ectiveness of counselor work has important reference value for improving the competence of counselors. This article analyzes in depth the importance and practical significance of student evaluation in enhancing the competence strategy of college counselors from the perspective of competence. Study the application algorithm of k-means algorithm as the research model. Collect basic information, work attitudes, and student management skills of counselors through a questionnaire survey. Then, using data mining techniques to process and analyze the collected data, key indicators and influencing factors of the counselor's ability are extracted. Through a review of a large number of relevant literature and field research, this article reveals the core role of college counselors in student education and management, as well as the key role of competence in their work. At the same time, this article elaborates on student evaluation as an important means of measuring the competence of counselors. It not only helps counselors to comprehensively understand their own work performance, but also provides targeted improvement directions for counselors. [ABSTRACT FROM AUTHOR]

Rapid and accurate detection of bamboo aphids can help prevent large-scale aphid infestations from occurring, which is of great significance for increasing bamboo shoot production and economic benefits. Herein, a lightweight and accurate model, SCA-YOLOv5s, was established by integrating ShuffleNetv2 and Coordinate Attention with the YOLOv5s model to detect Takecallis taiwanus on the yellow sticky traps. Specifically, we first replaced the backbone network of YOLOv5s with ShuffleNetv2 to reduce the number of parameters and computational complexity of the model. Second, an anchor optimization method was proposed by combining linear scaling and k-means algorithm to generate appropriate anchor boxes for detecting small-sized alate aphids. Third, the coordinate attention mechanism was added to the neck network to improve the feature extraction ability. To verify the performance of the proposed SCA-YOLOv5s model, eight detection models were constructed with existing deep learning methods, including SSD300, YOLOv3, Faster R-CNN, YOLOv4, YOLOv4-Tiny, YOLOX-Tiny, YOLOv7-Tiny, and YOLOv5s. Results reveal that the SCA-YOLOv5s model achieved higher detection accuracy than the other eight models. Its mean average precision reached 92.2 %. The proposed model has a size of only 6.7 MB, its floating-point operations (FLOPs) is 7.4 × 109, its inference time is 6.6 ms, and compared with YOLOv5s, it is 53.47 % smaller in model size, 55.15 % lower in FLOPs, and 0.8 ms faster in inference time. The results indicate that the proposed model can maintain high detection accuracy while minimizing computation and inference time, which is crucial for deployment in remote areas with low information technology. This study provides valuable technical support for the control of aphids in bamboo forests.

BackgroundIntegrating technology with rural development is essential for addressing the unique challenges faced by aging populations in rural areas. China’s national rural revitalization strategy emphasizes the importance of developing characteristic towns that focus on health and wellness demands, particularly for the older adults. However, there is a gap in the literature regarding the systematic use of technology to support the health and daily living needs of this demographic.ObjectiveThis study aims to bridge this gap by proposing a wireless sensor network (WSN)-based system that integrates health monitoring and smart home assistance, specifically tailored for older adults residents in sports and wellness towns.MethodsThe system’s design involves collecting and analyzing historical activity data and basic physiological parameters from older adults residents’ homes. The data are cleaned, combined and stored to prepare it for behavior analysis, which is vital for controlling smart home equipment. Preliminary health assessments are conducted using the collected physiological data. A hybrid network leveraging Sub-G and Wi-Fi technologies is optimized for the purposes of collecting and uploading data, based on a comparative analysis of wireless communication options.SignificanceThis study significantly contributes to the advancement of sports and wellness towns by promoting healthy aging through cutting-edge technological solutions.