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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]

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]

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]

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]

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]

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]

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]

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics 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.)

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]

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]

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]

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]

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]

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]

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]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University 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.)

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]

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]

Objectives: The traditional understanding of craniocervical alignment emphasizes specific anatomical landmarks. However, recent research has challenged the reliance on forward head posture as the primary diagnostic criterion for neck pain. An advanced relationship exists between neck pain and craniocervical alignment, which requires a deeper exploration of diverse postures and movement patterns using advanced techniques, such as clustering analysis. We aimed to explore the complex relationship between craniocervical alignment, and neck pain and to categorize alignment patterns in individuals with nonspecific neck pain using the K-means algorithm. Methods: This study included 229 office workers with nonspecific neck pain who applied unsupervised machine learning techniques. The craniocervical angles (CCA) during rest, protraction, and retraction were measured using two-dimensional video analysis, and neck pain severity was assessed using the Northwick Park Neck Pain Questionnaire (NPQ). CCA during sitting upright in a comfortable position was assessed to evaluate the resting CCA. The average of midpoints between repeated protraction and retraction measures was considered as the midpoint CCA. The K-means algorithm helped categorize participants into alignment clusters based on age, sex and CCA data. Results: We found no significant correlation between NPQ scores and CCA data, challenging the traditional understanding of neck pain and alignment. We observed a significant difference in age (F = 140.14, p < 0.001), NPQ total score (F = 115.83, p < 0.001), resting CCA (F = 79.22, p < 0.001), CCA during protraction (F = 33.98, p < 0.001), CCA during retraction (F = 40.40, p < 0.001), and midpoint CCA (F = 66.92, p < 0.001) among the three clusters and healthy controls. Cluster 1 was characterized by the lowest resting and midpoint CCA, and CCA during pro- and -retraction, indicating a significant forward head posture and a pattern of retraction restriction. Cluster 2, the oldest group, showed CCA measurements similar to healthy controls, yet reported the highest NPQ scores. Cluster 3 exhibited the highest CCA during protraction and retraction, suggesting a limitation in protraction movement. Discussion: Analyzing 229 office workers, three distinct alignment patterns were identified, each with unique postural characteristics; therefore, treatments addressing posture should be individualized and not generalized across the population. [ABSTRACT FROM AUTHOR]

TC11 impeller blades are typical complex curved surface components. Due to the complex machining load variations, these types of components have always been difficult to effectively solve issues such as significant machining deformation and large machining errors. This article characterizes complex surface features based on Gaussian curvature and mean curvature. Different machining methods are employed for different regions of the TC11 material after partitioning, and a comparative experiment is conducted between traditional methods and surface slicing methods for machining. Different machining methods are applied to process the different regions of TC11 material after surface partitioning, and a comparative experiment is conducted between traditional methods and surface slicing methods for machining. [ABSTRACT FROM AUTHOR]

While the classical K-means algorithm has been widely used in many fields, it still has some defects. Therefore, this paper proposes a scheme to improve the clustering quality of K-means algorithm. The farthest initial center selection and the min–max rule are used to improve the random initialization of K-means algorithm, which can avoid the empty clusters in the clustering results. For high-dimensional data sets, standardized feature scaling makes the data subject to normal distribution, and supervised linear discriminant analysis (LDA) is used to effectively reduce the data dimension and facilitate visualization. The empirical rule is used to estimate the range of the number of clusters. Within this range, the number of clusters of data is visually estimated by searching the elbow of the sum-of-squared-errors (SSE) curve. Further, a novel clustering validity function f(K) is proposed to determine the optimal number of clusters for complex real-world data sets. Through silhouette analysis, the clustering quality can be intuitively evaluated by calculating the silhouette coefficient of cluster and observing its size. The simulation results of different types of data sets show that this scheme can not only improve the clustering quality of K-means algorithm, but also provide a visual cluster analysis method for high-dimensional data sets. [ABSTRACT FROM AUTHOR]

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Intermittent joints are widely found in slopes and the rock section (locking section) in intermittent joints is an important structure that prevents joints from interconnecting and maintains slope stability, but the exact shape of the locking section is not yet known. In this paper, based on the finite difference method (FDM), k-means algorithm, and alpha shape algorithm, a method is proposed to calculate the shape of locking sections and to quantitatively analyze the controlling effect of locking sections and non-locking sections areas on the stability of slopes. In addition, for slopes with high accuracy in the shape of the locking section, the original mesh in the FDM is reconstructed and a network of discrete points is arranged at the locking section at customised intervals and the maximum principal stresses at each discrete point are calculated by the inverse transformation method of the isoparametric elements. This method greatly reduces the influence of grid size on the shape of the locking section. Finally, eight working conditions were designed to investigate the effect of the position of the weak interlayer on the shape and area of the locking section. The results show that the shape of the locking section does not change much as the angle of the line connecting the end points of the weak interlayer increases, and the shape basically remains elliptical, while the area of the locking section changes by increasing and then decreasing. [ABSTRACT FROM AUTHOR]

A healthcare chain (HC) involves interrelated activities inclusive of medicine manufacturing, storage, and last-mile distribution to drug retailers and users. Major decisionmakers in the HC are also interrelated, in which proper management and planning for logistics activities are required to enhance efficiency and effectiveness. In this study, we first investigate the optimal locations of central hospitals using the K-means algorithm at the midstream of the healthcare chain for long-term planning. Next, we assess the distribution plan of medical supplies by integrating the capacitated vehicle routing problem (CVRP) model with a limited time planning horizon, in which the total economic aspect is evaluated for the shortterm plan. Then, our integrated framework is applied to a case study of existing hospitals in Thailand to verify and validate model functionalities. We then examine both locational and distribution plans and present our findings using the geographic information system (GIS). The sensitivity analysis is further performed to evaluate the clustering classification scheme for central hospitals and to evaluate the impact on healthcare logistics plans. [ABSTRACT FROM AUTHOR]

Criminality is an act of violating the values and norms of society that causes a lot of harm. Much of the criminal data is often just a collection of data that has no information. Analysis of crime data is key in efforts to reduce crime rates that provide an overview of the incidence of crime, patterns, levels of vulnerability, and the level of security of an area. This research proposes data analysis that provides an understanding of crime using data mining techniques, especially the K-means cluster method, both traditional and with principal component analysis (PCA) dimension reduction. Before the PCA process, the values are transformed first with Z score normalization. From the processing through the davies bouldin index (DBI) performance test with 3 clusters, it is concluded that traditional K-means produces a DBI Index value of 0.019 and K-means PCA of 0.299. Meanwhile, to see the optimal cluster, several iterations were performed and resulted in the most optimal DBI index of 4 clusters in K-means of 0.014 and K-means PCA of 0.172. From the performance test value, it means that in the context of clustering the traditional criminal K-means data is declared more optimal than K-means PCA. [ABSTRACT FROM AUTHOR]

Clustering is an essential unsupervised technique when category information is not available. Although K-means and Max-min distance K-means clustering algorithms are widely used, they have some disadvantages such as dependence on the initial centers, sensitivity to outliers caused by using only distance as the clustering criterion. To overcome the problems, this paper proposes SMM-K-means algorithm which overcomes the dependence on the initial cluster centers and the initial number of clusters and the sensitivity to the outliers. First, the initial value K of the optimal cluster number is determined by the elbow method, and K-means is used for initial clustering. A new inter-cluster separation measure is then constructed based on the idea of q-nearest neighbors, which is constructed by comprehensive considering the separation between clusters and the distribution compactness of clusters themselves. Finally, the two sample points with highest degree of separation are brought into Max-min distance K-means algorithm as new initial centers for clustering. The definite determining method of cluster centers eliminates the complicated iterative calculation, and the construction of inter-cluster separation measure overcomes the sensitivity of clustering results to noise points and isolated points, and has good applicability and generalization. In addition, this algorithm is not limited by the shape and size of the clusters and has better flexibility. The experimental results show that the SMM-K-means algorithm has higher CH values, resulting in a better clustering effect and stability. [ABSTRACT FROM AUTHOR]

Remote sensing technology is an essential tool for tracking human-induced alterations on the water cycle, among which irrigation prevails. The possibility of obtaining detailed and accurate information on the actual irrigation extent through remote-sensing-based approaches is of paramount importance for water resources management. In this study, an update of the TSIMAP (Temporal-Stability-derived Irrigation MAPping) method, originally developed with satellite soil moisture as an input, is proposed. To demonstrate that the flexibility of the approach does not affect its main strength point (i.e., good accuracy in the face of high simplicity for users), a dual analysis relying on 1 km NDVI (Normalised Difference Vegetation Index) instead of soil moisture is carried out over the Ebro basin (Spain); data delivered by the Copernicus Global Land Service (CGLS) are used. First, results of this work are compared with outcomes from the method's original implementation obtained over a focus area (denominated "Ebro_CATAR") through satellite soil moisture. In the proposed configuration relying on NDVI, an overall accuracy (OA) up to 93% is found. Results highlight an increase in OA ranging from +2% to +6% depending on the validation strategy with respect to the TSIMAP implementation relying on soil moisture. Then, a basin-scale application is performed, providing performances still satisfactory (OA = 75%) notwithstanding a higher degree of heterogeneity. [ABSTRACT FROM AUTHOR]

The distraction affects driving performance and induces serious safety issues. To better understand distracted driving, this study examines the influence of distracted driving on overall driving performance. This paper analyzes the distraction behavior (mobile phone use, entertainment activities, and passenger interference) under three driving tasks. The statistical results show that viewing or sending messages is common during driving. Smoking, phone calls, and talking to passengers are evident in cruising, ride request and drop-off, respectively. Then, overall driving performance is proposed based on velocity, longitudinal acceleration (longacc) and yaw_rate. It is divided into three categories, high, medium, and low, by k-means algorithms. The average speed increases from low to high performance; however, the longacc and yaw_rate decrease. Finally, the influence of distracted driving on overall driving performance is analyzed using C4.5 algorithm. The result shows that when time is peak, the probability of high performance (HP) is higher than off-peak. The possibility of HP increases with the increase of duration; the number of, talking to passengers, listening to music or radio, eating; the duration of, viewing or sending messages, phone calls; but reduces with the increase of the number of phone calls. These findings provide theoretical support for driving performance evaluation. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Bioinformatics is the property of Bioinformatics 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.)

Based on the development of the concept of a resource-saving and environmentally friendly society, needing to develop low-carbon and sustainable urban transportation. Most of the pollutants come from the emissions of motor vehicle exhaust. Therefore, this paper analyzes the relationship between driving behavior and traffic emissions, to constrain driver behavior to reduce pollutant emissions. The GPS data are preprocessed by using Navicat for data integration, data screening, data sorting, etc., and then, the speed data are cleaned by using a combination of box-and-line plots and linear interpolation in SPSS. Second, this paper uses principal component analysis (PCA) to downsize 12 indicators such as average speed, average acceleration, and maximum speed and then adopts K-MEANS and K-MEDOIDS methods to cluster the driver’s behavioral indicators, selects the aggregation method based on the clustering indexes optimally, and analyzes the driver’s driving state by using the symbolic approximation aggregation method; finally, according to the above research results and combined with the MOVES traffic emission model to analyze the relationship between the driver’s driving mode, driving state, and traffic emissions, the decision tree can be used to predict the unknown driving mode of the driver to estimate the degree of its emissions. [ABSTRACT FROM AUTHOR]

Correcting interferometric synthetic aperture radar (InSAR) interferograms using Global Navigation Satellite System (GNSS) data can effectively improve their accuracy. However, most of the existing correction methods utilize the difference between GNSS and InSAR data for surface fitting; these methods can effectively correct overall long-wavelength errors, but they are insufficient for multiple medium-wavelength errors in localized areas. Based on this, we propose a method for correcting InSAR interferograms using GNSS data and the K-means spatial clustering algorithm, which is capable of obtaining correction information with high accuracy, thus improving the overall and localized area error correction effects and contributing to obtaining high-precision InSAR deformation time series. In an application involving the Central Valley of Southern California (CVSC), the experimental results show that the proposed correction method can effectively compensate for the deficiency of surface fitting in capturing error details and suppress the effect of low-quality interferograms. At the nine GNSS validation sites that are not included in the modeling process, the errors in the ascending track 137A and descending track 144D are mostly less than 15 mm, and the average root mean square error values are 11.8 mm and 8.0 mm, respectively. Overall, the correction method not only realizes effective interferogram error correction, but also has the advantages of high accuracy, high efficiency, ease of promotion, and can effectively address large-scale and high-precision deformation monitoring scenarios. [ABSTRACT FROM AUTHOR]

This study presents a novel method for optimizing parameters in urban flood models, aiming to address the tedious and complex issues associated with parameter optimization. First, a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model. Next, a principle for dividing urban hydrological response units was introduced, incorporating surface attribute features. The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model, and an artificial neural network (ANN) was employed to identify the sensitive parameters. Finally, a genetic algorithm (GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model. The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient (NSE) of 0.81. Compared to the ANN-GA and K-means-deep neural networks (DNN) methods, the proposed method better characterizes the runoff generation and flow processes. This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models. [ABSTRACT FROM AUTHOR]

Machine learning-based classification algorithms allow communication and computing (2C) task offloading from the end devices to the edge computing network servers. In this paper, we consider task classification based on the hybrid k-means and k ′ -nearest neighbors algorithms. Moreover, we examine the poisoning attacks on such ML algorithms, namely noise-like jamming and targeted data feature falsification, and their impact on the effectiveness of 2C task allocation. Then, we also present two anomaly detection methods using noise training and the silhouette score test to detect the poisoned samples and mitigate their impact. Our simulation results show that these attacks have a fatal effect on classification in feature areas where the decision boundary is unclear. They also demonstrate the effectiveness of our countermeasures against the considered attacks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Nanjing University of Information Science & Technology (Natural Science Edition) / Nanjing Xinxi Gongcheng Daxue Xuebao (ziran kexue ban) is the property of Editorial Office of Journal of Nanjing University of Information Science & Technology 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.)

In order to make full use of the accumulated curriculum data of Folk costume and dig out useful information from it, so as to provide useful information for curriculum teaching, the article proposes three general functions based on the requirement analysis, and pre-processes the completed grade data of ethnic costume students in 4 academic years, analyzes these data by FP-growth algorithm to understand the situation of association rules between different courses, and through K-means++ algorithm The clustering analysis of students with different levels of achievement is carried out and the results are validated by examples. In the algorithm performance analysis, the performance of FP-growth algorithm is better, the average absolute error of FP-growth algorithm is always smaller than that of Apriori algorithm; When the support degree is 20%, the running time of FP-growth algorithm is 0.4s, which is 0.4s less than that of Apriori algorithm. when the number of calculation nodes is 5, the running time of FP-growth algorithm and the accuracy of the K-means++ algorithm were higher than that of the K-means algorithm. In the Iris dataset, the accuracy of the K-means++ algorithm was 91.05%, which was 8.94% higher than that of the K-means algorithm. When mining the course grade data, the confidence level of the obtained association rules was even higher, even up to 97.15%. The standardized test score for the second group of students was 0.960. The course evaluation method used in the article was more objective and the accuracy of the data analysis was higher, providing valuable reference information for teachers' teaching. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges modelled by collecting and storing large volumes of big data, focusing on mitigating data errors. The primary goal is to propose and evaluate an enhanced K-means algorithm for big data applications. This research also aims to design an extensive energy data system to demonstrate the improved algorithm's practical utility in monitoring power equipment. The research begins with an in-depth analysis of the traditional K-means algorithm, culminating in the proposal of an improved version. Subsequently, the study outlines developing a comprehensive, extensive energy data system, encompassing architectural aspects such as data storage, mechanical layers, and data access structures. The research also involves the development of a power big data analysis platform, incorporating the improved algorithm for clustering and analyzing power equipment monitoring data. Experimental results reveal that the proposed improved K-means algorithm outperforms the traditional version, with significantly improved accuracy and reduced classification errors, achieving an error rate of less than one. The improved K-means algorithm showcased remarkable enhancements, achieving a meagre misclassification rate of just 0.08% while substantially boosting accuracy levels, consistently exceeding 95% across all datasets. Moreover, the power big data system developed in this study to meet practical requirements while enhancing storage and processing efficiency effectively. [ABSTRACT FROM AUTHOR]

The management of diabetes involves much compliance, disease awareness, and patient empowerment. Blood glucose levels in people with diabetes are abnormal, resulting in various health problems that affect their kidneys, hearts, and vision. Monitoring the patient's health has the benefit of using the Internet of Things (IoT) and diabetes patient monitoring systems. Because this system uses machine learning to classify data, its value goes beyond patient monitoring. So, this paper proposes a novel deep learning model with an effective feature selection mechanism for diabetes mellitus (DM) prediction in IoT-based healthcare environment. The proposed work starts by collecting the real-time IoT data of the patients from the Pima Indian Diabetes database. The data imbalance problem of the collected dataset is rectified by applying the synthetic minority oversampling technique. Afterward, the balanced dataset is preprocessed by applying missing value imputation and data standardization to improve the classification performance. Then, the hybrid algorithm called k-means clustering-based sailfish optimization is utilized, which performs clustering and optimization to select the essential features from the preprocessed dataset. Finally, the selected features are fed into the kaiming and switan included bidirectional long short-term memory for DM prediction. The proposed system achieves better results than the existing state-of-the-art techniques, which shows its effectiveness in predicting DM in real-time IoT datasets. [ABSTRACT FROM AUTHOR]

Because of the advent of new technologies like machine learning (ML), the Internet of Things (IoT), and big data, nearly every electronic device in the modern era of digitization is now capable of making intelligent decisions. To transform instructional management in the big data and educational informatization era, institutions are using digital technologies. Though there has been significant progress in this area, there are still numerous issues with the informatization of instructional management in universities, which have an impact on both the quality of construction and the effectiveness of the application. To improve effectiveness, efficiency, and data security, we investigated in this study how to integrate IoT devices and big data analytics methods into university instructional management systems. Universities can handle security and data integrity concerns while extracting insights from complex data streams by utilizing IoT devices with big data analytics. To achieve the stated goal, we first carefully examined the current university instructional management system to identify its shortcomings. After that, we presented our proposal for an instructional management system for universities that collects data using IoT devices, and it undergoes preprocessing to get it ready for big data analytics. Some of the data mining and big data analytics algorithms such as K-mean clustering, PCA, and Apriori algorithms were applied to get a thorough insight from the collected data. Students were grouped according to their academic activities using the K-mean clustering algorithm. By lowering the dimensionality, the principal component analysis (PCA) algorithm is utilized to determine the relationship between a student's library visit and their final grade. The result of our proposed system is carefully analyzed to display the various levels of student interaction, their study habits, and their final grade. [ABSTRACT FROM AUTHOR]