Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.32.2.021404] © 2023 SPIE. All rights reserved.

A Comprehensive Analysis of Clustering Public Utility Bus Passenger's Behavior during the COVID-19 Pandemic: Utilization of Machine Learning with Metaheuristic Algorithm

Public utility bus (PUB) systems and passenger behaviors drastically changed during the COVID-19 pandemic. This study assessed the clustered behavior of 505 PUB passengers using feature selection, K-means clustering, and particle swarm optimization (PSO). The wrapper method was seen to be the best among the six feature selection techniques through recursive feature selection with a 90% training set and a 10% testing set. It was revealed that this technique produced 26 optimal feature subsets. These features were then fed into K-means clustering and PSO to find PUB passengers' clusters. The algorithm was tested using 12 different parameter settings to find the best outcome. As a result, the optimal parameter combination produced 23 clusters. Utilizing the Pareto analysis, the study only considered the vital clusters. Specifically, five vital clusters were found to have comprehensive similarities in demographics and feature responses. The PUB stakeholders could use the cluster findings as a benchmark to improve the current system.

Enhancing Security of Medical Image Data in the Cloud Using Machine Learning Technique

To prevent medical data leakage to third parties, algorithm developers have enhanced and modified existing models and tightened the cloud security through complex processes. This research utilizes PlayFair and K-Means clustering algorithm as double-level encryption/ decryption technique with ArnoldCat maps towards securing the medical images in cloud. K-Means is used for segmenting images into pixels and auto-encoders to remove noise (de-noising);the Random Forest regressor, tree-method based ensemble model is used for classification. The study obtained CT scan-images as datasets from ‘Kaggle' and classifies the images into ‘Non-Covid' and ‘Covid' categories. The software utilized is Jupyter-Notebook, in Python. PSNR with MSE evaluation metrics is done using Python. Through testing-and-training datasets, lower MSE score (‘0') and higher PSNR score (60%) were obtained, stating that, the developed decryption/ encryption model is a good fit that enhances cloud security to preserve digital medical images.

A Research on Online Teaching Behavior of Chinese Local University Teachers Based on Cluster Analysis

COVID-19 boosted online teaching and yielded a significant amount of valuable data, yet utilizing it for education is a challenge. This study employed the K-means clustering method to analyze the online teaching behavior data of 1147 courses from a local university in East China. As a result, five types of courses with distinct teaching behaviors were identified: resource preparation (4.1%), online classroom interaction (3.6%), task evaluation (9.2%), active interaction (15.5%), and inactive interaction (67.6%). By examining the relationship between these course types and academic performance, the authors discovered no significant difference in the academic performance of students in the three course groups (i.e., resource preparation, online classroom interaction, and task evaluation) and students in the inactive interaction course group. However, there was a significant disparity in academic performance between students in active interaction courses and students in inactive interaction courses. These findings can assist teachers in planning online teaching activities more effectively and improving teaching outcomes.

VQ-ViCNet: Strengthen Unique Features Comparison Autoencoder with Embedding Space for Covid-19 Image Classification

In this paper, we propose a new novel coronavirus pneumonia image classification model based on the combination of Transformer and convolutional network(VQ-ViCNet), and present a vector quantization feature enhancement module for the inconspicuous characteristics of lung medical image data. This model extracts the local latent layer features of the image through the convolutional network, and learns the deep global features of the image data through the Transformer's multi-head self attention algorithm. After the calculation of convolution and attention, the features learned by the Transformer Encoder are enhanced by the vector quantization feature enhancement module and able to better complete the final downstream tasks. This model performs better than convolutional architectures, pure attention architectures and generative models on all 6 public datasets. © 2022 IEEE.

Efficient Online and Privacy-preserving Medical Pre-diagnosis Based on Growing Learning Vector Quantization

With more and more people turning to online medical pre-diagnosis systems, it becomes increasingly important to protect patient privacy and enhance the accuracy and efficiency of diagnosis. That is because the ever rapidly growing medical records not only contain a large amount of private information but are often highly unequally distributed (e.g., the number of cases and the rate of increase of covid-19 can be much higher than that of common diseases). However, existing methods are not capable of simultaneously boosting the intensity of privacy protection, and the accuracy and efficiency of diagnosis. In this paper, we propose an online medical pre-diagnosis scheme based on incremental learning vector quantization (called WL-OMPD) to achieve the two objectives at the same time. Specifically, within WL-OMPD, we design an efficient algorithm, Wasserstein-Learning Vector Quantization (W-LVQ), to smartly compress the original medical records into hypothetic samples. Then, we transmit these compressed data to the cloud instead of the original records to offer a more accurate pre-diagnosis. Extensive evaluations of real medical datasets show that the WL-OMPD scheme can improve the imbalance ratio of the data to a certain extent and then the intensity of privacy protection. These results also demonstrate that WL-OMPD substantially boost the accuracy of the classification model and increase diagnostic efficiency at a lower compression rate. © 2022 IEEE.

VQC-COVID-NET: Vector Quantization Contrastive Learning for Covid-19 Image Base Classification

Today, the COVID-19 epidemic has become extremely widespread. The first step in combating COVID-19 is identifying cases of infection. Real-time reverse transcriptase polymerase chain reaction is the most common method for identifying COVID (RT-PCR). This method, however, has been compromised by a time-consuming, laborious, and complex manual process. In addition to the RT-PCR test, screening computed tomography scan (CT) or X-ray images may be used to identify positive COVID-19 results, which could aid in the detection of COVID-19. Because of the continuing increase in new infections, the development of automated techniques for COVID-19 detection utilizing CT images is in high demand. This will aid in clinical diagnosis and alleviate the arduous task of image interpretation. Aggregating instances from various medical systems is highly advantageous for enlarging datasets for the development of machine learning techniques and the acquisition of robust, generalizable models. This study proposes a novel method for addressing distinct feature normalization in latent space due to cross-site domain shift in order to accurately execute COVID-19 identification using heterogeneous datasets with distribution disagreement. We propose using vector quantization to enhance the domain invariance of semantic embeddings in order to enhance classification performance on each dataset. We use two large, publicly accessible COVID-19 diagnostic CT scan datasets to develop and validate our proposed model. The experimental results demonstrate that our proposed method routinely outperforms state-of-the-art techniques on testing datasets. Public access to the implementation of our proposed method is available at https://github.com/khaclinh/VQC-COVID-NET. © 2022 IEEE.

Maintainability of Service-Oriented Architecture using Hybrid K-means Clustering Approach

The process of making changes to software after it has been delivered to the client is known as maintainability. Maintainability deals with new or changed client requirements. Service-oriented architecture (SOA) is a method for developing applications that helps services work on different environments. SOA works on patterns of distributed systems that help different applications communicate with each other using different protocols. To assess the maintainability of service-oriented architecture, different factors are required. Some of these factors are analyzability, changeability, stability, and testability. Modification is the process of upgrading the software functionality. After modification of service-oriented architecture, the module will go to the testing phase. The evaluation and verification of whether a software product or application performs as intended is known as testing. The testing phase is a combination of various stages, such as individual module testing and testing after collaborations between them. This testing stage is time-consuming in the maintenance process. The term "outlier" refers to a module in software systems that deviates significantly from the rest of the module. It represents the collection of data, variables, and methods. For instance, the program might have been coded mistakenly or an investigation might not have been run accurately. To detect the outlier module, test cases are needed. A methodology is proposed to reduce the predefined test cases. K-means clustering is the best approach to calculate the number of test cases, but the outlier is not automatically determined. In this paper, a hybrid clustering approach is applied to detect the outlier. This clustering method is used in software testing to count the number of comments in various software and in medical science to diagnose the disease of Covid patients. The experimental outcomes show that our strategy achieves better results.

Effective prediction of fake news using a learning vector quantization with hamming distance measure

It never happened before in human history the spreading of fake news;now, the development of the Worldwide Web and the adoption of social media have given a pathway for people to spread misinformation to the world. Everyone is using the Internet, creating and sharing content on social media, but not all the information is valid, and no one is verifying the originality of the content. It is sometimes complicated for researchers and intelligence to identify the essence of the content. For example, during Covid-19, misinformation spread worldwide about the outbreak, and much false information spread faster than the virus. This misinformation will create a problem for the public and mislead people into taking the proper medicine. This work will help us to improve the prediction rate. The proposed algorithm is compared with three existing algorithms, and the result is better than the other three current algorithms. The prediction rate of impact for the proposed algorithms is 93.54% © 2022 The Authors

COMPARISON ARTIFICIAL NEURAL NETWORK METHODS OF BACKPROPAGATION AND LEARNING VECTOR QUANTIZATION FOR FORECASTING STOCK PRICES

During the COVID-19 pandemic stock trading is a hot topic of discussion and encourages new investors which positive impact on the market modal. Shares of PT. XL Axiata, Tbk. (EXCL) was sluggish despite reporting a surge profit in 2021, this prompted research on how to predict the stock price of EXCL for attract investors and encourage company to be more active in carrying out business strategies. In recent years, Artificial Neural Networks (ANN) are quite used in macroeconomics forecasting, because of their ability to detect and relate linear and non-linear functions. In this study, two ANN methods were used to predict the stock price of EXCL with backpropagation (BP) and Learning Vector Quantization (LVQ). In the prediction results, model evaluation is needed to measure the forecasting model from both methods, resulting in the confusion matrix with the accuracy, sensitivity, and specificity are provided. This research is given some value to stock action suggestions at EXCL. © 2022 Little Lion Scientific.

KMK based hybrid approach for the performance estimation in case of diabetes data

In this paper k-means clustering algorithm has been used with k-points (KMK) selection. It has been applied on the PIMA Indian diabetes dataset. It has been used for distance estimation, centroid selection, effect of data size variations and for the analysis of the complete record. The cluster section has been found to be improved based on k-point selection. It has been used for the assignment of initial centroid. The results indicate that the KMK algorithm is capable in the improvement of centroid selection and distance measures in the assignments of data points. It is due to the better centroid selection mechanism by k-points selection based on the weight measures from the selected dataset. So, the obtained clusters are better in comparison to k-means.

Towards Post-pandemic Occupancy Patterns: Investigating Building Occupancy in New York City

Urban-scale energy simulation relies on the understanding of occupants' presence in buildings and consequently in cities. Therefore, occupancy profiles (i.e., the relative number of occupants in a specific hour of the day) are usually used in the energy simulation on the city level. However, available occupancy standard profiles are incapable of considering the dynamic nature of occupancy schedules and any changes that occurred due to contextual changes (such as the dramatic increase in remote working last year). Therefore, the need for a scalable method to generate dynamic occupancy profiles for buildings is crucial. Moreover, the targeted method should allow for tracking the changes that occur in occupancy profiles due to external disruption such as pandemics. In this context, this study aims at using the emerging mobile positioning data to generate context-specific data-driven occupancy profiles for commercial and institutional buildings in New York City. The generated profiles were then compared versus ASHRAE standard profiles for each building category. Then, the occupancy profiles were clustered for each building category, using K-means clustering algorithm. Finally, the effect of COVID-19 pandemic on the peak points and shape of occupancy profiles was investigated. The results showed a significant difference between the data-driven and ASHRAE standard profiles. Additionally, a considerable variation in the shape and peak hours of the generated occupancy profile clusters was detected for some building categories. These results can be used to improve the accuracy of the urban-scale simulation models. Furthermore, they can provide a more precise evaluation of the occupant's schedules and consequently the urban scale energy consumption before field implementation of the operational strategies.

Technostress Among Higher Education Students During the COVID-19 Outbreak

The experiences that higher education students have with technology and learning with the support of technological resources can generate feelings of stress and anxiety. Understanding whether or not students are ill-adapted to technology is of utmost importance to understand the extent to which changes are needed in the teaching and learning process. With this purpose, the students’ perceptions about the technology, namely its familiarity, the ease of use, the utility of technological resources, levels of satisfaction with learning from remote learning and levels of technostress during the confinement period due to the Covid-19 pandemic were evaluated. Several statistical methods were applied, among which, the Multiple Correspondence Analysis and the k-means clustering algorithm, in order to obtain a partition of students based on their perceptions and experiences in the course of remote learning. The results revealed three distinct profiles, concerning students’ perceptions about their relationship with technology.

APPLICATION OF K-MEANS CLUSTERING ALGORITHM FOR ANALYSIS OF LMS CONTENT TRANSFORMATIONS CAUSED BY THE COVID-19 PANDEMIC

In the first half of2020, due to the Covid-19 pandemic, educational institutions worldwide had to close their doors to students;learning in the classroom was not possible due to the growing virus infection. Depending on previous experience and infrastructure, institutions have more or less successfully switched to online teaching. This paper presents a method that, from the data in the report of the administrator of one of the most popular LMS systems, Moodle, can bring new knowledge about this transfer on the Moodle system. It uses the k-means algorithm, which is used to divide courses into clusters depending on the content available in each course on the LMS. To analyse this transformation, a comparison was made of the number and content of clusters from the data of the winter semester of the academic year 2019/2020, with the winter semester of the academic year 2020/2021.

Challenges and Drawbacks of the EU Medical System Generated by the COVID-19 Pandemic in the Field of Health Systems' Digitalization.

The COVID-19 pandemic and the digitalization of medical services present significant challenges for the medical sector of the European Union, with profound implications for health systems and the provision of high-performance public health services. The sustainability and resilience of health systems are based on the introduction of information and communication technology in health processes and services, eliminating the vulnerability that can have significant consequences for health, social cohesion, and economic progress. This research aims to assess the impact of digitalization on several dimensions of health, introducing specific implications of the COVID-19 pandemic. The research methodology consists of three procedures: cluster analysis performed through vector quantization, agglomerative clustering, and an analytical approach consisting of data mapping. The main results highlight the importance of effective national responses and provide recommendations, various priorities, and objectives to strengthen health systems at the European level. Finally, the results reveal the need to reduce the gaps between the EU member states and a new approach to policy, governance, investment, health spending, and the performing provision of digital services.

Orchestrating Image Retrieval and Storage over A Cloud System

Since massive numbers of images are now being communicated from, and stored in different cloud systems, faster retrieval has become extremely important. This is more relevant, especially after COVID-19 in bandwidth-constrained environments. However, to the best of our knowledge, a coherent solution to overcome this problem is yet to be investigated in the literature. In this paper, by customizing the Progressive JPEG method, we propose a new Scan Script to ensure Faster Image Retrieval. Furthermore, we also propose a new lossy PJPEG architecture to reduce the file size as a solution to overcome our Scan Script's drawback. In order to achieve an orchestration between them, we improve the scanning of Progressive JPEG's picture payloads to ensure Faster Image Retrieval using the change in bit pixels of distinct Luma and Chroma components (Y, C_b, and C_r). The orchestration improves user experience even in bandwidth-constrained cases. We evaluate our proposed orchestration in a real-world setting across two continents encompassing a private cloud. Compared to existing alternatives, our proposed orchestration can improve user waiting time by up to 54% and decrease image size by up to 27%. Our proposed work is tested in cutting-edge cloud apps, ensuring up to 69% quicker loading time. IEEE

Identification of the Right Moment for Motor Vehicle Replacement—Life-Cycle Analysis in Serbia and Montenegro

This paper deals with the issue of planning the end-of-life phase of motor vehicle life cycles in Serbia and Montenegro. This topic is trending around sustainability issues, given the very unfavorable age structure of vehicles and the increasing import of used cars, which intensifies the problem of the number of waste vehicles. On average, a motor vehicle is in active use for a period of 10 to 15 years. Individual phases of its life cycle are indicated differently, using multiple parameters. All phases are influenced by many factors, but this paper focuses on the phases of active use and the end of life of a motor vehicle. This paper investigates these two phases in terms of the influencing elements. The main aim of this study is to lay the foundations for making adequate decisions on how to handle end-of-life vehicles, from the perspective of their drivers. The study includes performing quantitative research analysis via the k-means clustering technique on a sample of 1240 drivers (private and commercial vehicles), in order to draw concrete conclusions through appropriate statistical analysis. The key findings suggest that different market, business, and environment indicators define the phases of active use and end of life, throughout the life cycle of a motor vehicle. Future research will expand the sample to surrounding countries.

Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data

In the last decade, smart spaces and automatic systems have gained significant popularity and importance. Moreover, as the COVID-19 pandemic continues, the world is seeking remote intervention applications with autonomous and intelligent capabilities. Context-aware computing (CAC) is a key paradigm that can satisfy this need. A CAC-enabled system recognizes humans’ status and situation and provides proper services without requiring manual participation or extra control by humans. However, CAC is insufficient to achieve full automaticity since it needs manual modeling and configuration of context. To achieve full automation, a method is needed to automate the modeling and reasoning of contexts in smart spaces. In this paper, we propose a method that consists of two phases: the first is to instantiate and generate a context model based on data that were previously observed in the smart space, and the second is to discern a present context and predict the next context based on dynamic changes (e.g., user behavior and interaction with the smart space). In our previous work, we defined “context” as a meaningful and descriptive state of a smart space, in which relevant activities and movements of human residents are consecutively performed. The methods proposed in this paper, which is based on stochastic analysis, utilize the same definition, and enable us to infer context from sensor datasets collected from a smart space. By utilizing three statistical techniques, including a conditional probability table (CPT), K-means clustering, and principal component analysis (PCA), we are able to automatically infer the sequence of context transitions that matches the space–state changes (the dynamic changes) in the smart space. Once the contexts are obtained, they are used as references when the present context needs to discover the next context. This will provide the piece missing in traditional CAC, which will enable the creation of fully automated smart-space applications. To this end, we developed a method to reason the current state space by applying Euclidean distance and cosine similarity. In this paper, we first reconsolidate our context models, and then we introduce the proposed modeling and reasoning methods. Through experimental validation in a real-world smart space, we show how consistently the approach can correctly reason contexts.

Detecting Learning Patterns in Tertiary Education Using K-Means Clustering

We are in the era where various processes need to be online. However, data from digital learning platforms are still underutilised in higher education, yet, they contain student learning patterns, whose awareness would contribute to educational development. Furthermore, the knowledge of student progress would inform educators whether they would mitigate teaching conditions for critically performing students. Less knowledge of performance patterns limits the development of adaptive teaching and learning mechanisms. In this paper, a model for data exploitation to dynamically study students progress is proposed. Variables to determine current students progress are defined and are used to group students into different clusters. A model for dynamic clustering is proposed and related cluster migration is analysed to isolate poorer or higher performing students. K-means clustering is performed on real data consisting of students from a South African tertiary institution. The proposed model for cluster migration analysis is applied and the corresponding learning patterns are revealed.

Applying Clustering Methods to Develop an Optimal Storage Location Planning-Based Consolidated Picking Methodology for Driving the Smart Manufacturing of Wireless Modules

Picking operations is the most time-consuming and laborious warehousing activity. Managers have been seeking smart manufacturing methods to increase picking efficiency. Because storage location planning profoundly affects the efficiency of picking operations, this study uses clustering methods to propose an optimal storage location planning-based consolidated picking methodology for driving the smart manufacturing of wireless modules. Firstly, based on the requirements of components derived by the customer orders, this research analyzes the storage space demands for these components. Next, this research uses the data of the received dates and the pick-up dates for these components to calculate the average duration of stay (DoS) values. Using the DoS values and the storage space demands, this paper executes the analysis of optimal storage location planning to decide the optimal storage location of each component. In accordance with the optimal storage location, this research can evaluate the similarity among the picking lists and then separately applies hierarchical clustering and K-means clustering to formulate the optimal consolidated picking strategy. Finally, the proposed method was verified by using the real case of company H. The result shows that the travel time and the distance for the picking operation can be diminished drastically.