Classification Technology Research Articles

Deep Learning Based Hybrid Network Architecture to Diagnose IoT Sensor Signal in Healthcare System

IoT is a fascinating technology in today's IT world, in which items may transmit data and interact through intranet or internet networks. TheInternet of Things (IoT) has shown a lot of promise in connecting various medical equipment, sensors, and healthcare specialists to provide high-quality medical services from afar. As a result, patient safety has improved, healthcare expenses have fallen, healthcare service accessibility has increased, and operational efficiency has increased in the healthcare industry. Healthcare IoT signal analysis is now widely employed in clinics as a critical diagnostic tool for diagnosing health issues. In the medical domain, automated identification and classification technologies help clinicians make more accurate and timely diagnoses. In this paper, we have proposed a Deep Learning-Based hybrid network architecture (CNN-R-LSTM (DCRL)) that combines the characteristics of a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) based long-short-term memory (LSTM) to diagnose IoT sensor signals and classify them into three categories: healthy, patient, and serious illness. Deep CNN-R-LSTM Algorithm is used for classify the IoT healthcare data support via a dedicated neural networking model. For our study, we have used the MIT-BIH dataset, the Pima Indians Diabetes dataset, the BP dataset, and the Cleveland Cardiology datasets. The experimental results revealed great classification performance in accuracy, specificity, and sensitivity, with 99.02 percent, 99.47 percent, and 99.56 percent, respectively. Our proposed DCLR model is based on healthcare IoT Centre inputs enhanced with the centenary, which may aid clinicians in effectively recognizing the health condition.

Trade and economic growth: Does the sophistication of traded goods matter?

Since the appearance of the seminal paper of Frankel and Romer (Am Econ Rev 89(3): 379–399, 1999), ‘Does trade cause growth?’, the impact of aggregate trade openness on income has been controversial. This research shows that the type of product that is traded has first-order effects, while overall trade intensity has second-order effects on per capita income because of (i) the hierarchical structure of learning-by-doing in products with different levels of sophistication of the production processes; and (ii) the fertility and education effects of trade specialization following the quantity–quality tradeoff framework of Galor and Mountford (Am Econ Rev 96(2): 299–303, 2006). Using data on trade disaggregated by the level of technological sophistication of the production process for 223 countries over the period 1962–2019, we find that (1) the effects of foreign trade on income differ widely across technology categories; (2) high-tech trade has permanent growth effects; and (3) a significant fraction of the impact of trade on income is mediated through education and fertility.

A deep learning method for intelligent decision-making in enterprise management based on the Internet of Things

With the development and progress of various sensors, the Internet of Things technology has continued to penetrate into people’s lives. Successful application cases of the Internet of Things technology have been seen in various industries. Some scholars have begun to try to apply the Internet of Things technology to the management of various industries. Deep learning technology belongs to the category of artificial intelligence technology. Its main goal is to combine a large number of training samples to train the trained object into an object that can learn sample patterns independently. At present, deep learning has a large number of applications in various intelligent operations and key intelligent recognition fields. Due to the characteristics of its learnable training set, deep learning techniques are often used in the direction of decision analysis. The research adopts a combination of questionnaire survey and modeling analysis to explore the construction of in-depth learning methods for enterprise management intelligent decision-making in the context of the Internet of Things. The research was conducted by telephone interview and online questionnaire, and was supported by nearly 100 technical backbone personnel from 25 large enterprises. The research finds that applying the Internet of Things and deep learning technology to enterprise intelligent management decisions can improve the decision-making accuracy of enterprises, and also provide enterprises with more significant decision-making analysis advantages in the industry competition. This paper first describes the concepts of the Internet of Things, deep learning and intelligent decision-making of enterprise management. Then, in order to sort out the problems existing in the current intelligent decision-making of enterprise management, the author conducted interviews and questionnaires. Through the analysis of the questionnaire data, he found the problems existing in the current intelligent decision-making of enterprise management. Finally, he designed a new strategy of intelligent decision-making of enterprise management based on the Internet of Things and deep learning technology, through the simulation and subsequent investigation and analysis of the newly designed strategy. It is found that the enterprise management intelligent decision-making method designed by combining the Internet of Things and deep learning technology is superior to the traditional enterprise decision-making method in all aspects. The design of the system has been reformed mainly from the aspects of human resource management, digital file management and real-time monitoring of production and operation, so that the popularity of the enterprise’s comprehensive information management has reached 84.2%, the efficiency of intelligent decision-making increased by 28.7% on average. The achievements of the research not only prove the role of IoT technology in promoting enterprises’ in-depth learning, but also put forward new ideas for enterprises’ scientific decision-making.

We build this city on rocks and (feminist) code: hacking corporate computational designs of cities to come

ABSTRACT Cities have long become interspaces, entangled in materialities and virtual worlds. However, as urban automation advances in cities increasingly made ‘smarter’, everyday processes are often controlled by oppressive standards hardcoded into technologies. Publicly neutralized as ‘objective’, corporately owned algorithmic architectures now function as urban gatekeepers. They determine social participation, possibilities of space appropriation on- and offline, and access to (social) infrastructures. Following five months of qualitative research on hacking and other tech-practices by German-speaking cyberfeminist collectives in 2021, my paper portrays their refusal of black-boxed, profitable, and biased technologies of classification. I argue that feminist hackspaces are important urban co-creators in digitized cities to come. They offer infrastructures to increase access to interfaces, (cyber-)spaces, and decision-making processes by sharing their tech-knowledge and tools. Their activism demonstrates how (urban) hacking is a crucial practice to break with non-democratically controlled digitalization processes: in favour of a city for all.

Max–min distance embedding for unsupervised hyperspectral image classification in the satellite Internet of Things system

In modern society, hyperspectral images have widely been used in many applications. In the earth observation system, hyperspectral images are acquired by hyperspectral sensors and transmitted to the cloud computing center for analysis, which are the part of satellite Internet of Things(IoT) to build a large scale land-cover surveillance system. Thus, during recent years, researchers throughout academia and industry have been advancing the classification of hyperspectral images. Luckily for swamped remote sensing researchers, new technology is allowing brands to extract useful knowledge using artificial intelligence to enhance supervised hyperspectral image classification technology. However, conventional methods that label and process hyperspectral images manually are labor and time-consuming. Hence, in this paper, we proposed an unsupervised hyperspectral image classification framework. The paper differs from the existing methods by three aspects: (1) We adopt multi-scale spatial features to enhance the discriminability of data. (2) We propose deep autoencoder with max–min distance embedding to extract features and reduce dimensions of data, which is more suitable for large-scale data clustering. (3) We apply k-means clustering method to get original classification, then optimize the results by the guided filter. Experimental results on two widely used hyperspectral data, Salinas and Pavia University, show the competitive performance obtained by the proposed framework compared with other approaches.

Research on emotion classification technology of movie reviews based on topic attention mechanism and dual channel long short term memory.

In recent years, with the popularity of the Internet, more and more people like to comment on movies they have watched on the film platform after watching them. These reviews hide the reviewers' feedback on films. Mining the emotional orientation information in these reviews can provide consumers with shopping references and help businesses optimize film works and improve business strategies. Therefore, the emotional classification of film reviews has high research value because few emotion dictionaries and analysis tools are available for reference and use in film reviews. The accuracy of emotion classification still needs to be improved. This study introduces the attention mechanism and dual channel long short term memory (DC-LSTM) while building the emotion dictionary in the field of Chinese film review. It classifies Chinese film reviews in terms of topic-based fine-grained emotion. First, the emotion vector is constructed using the constructed movie review emotion lexicon. The semantic vector obtained by the Word2vector tool is input to LSTM to encode the comment text. Then, the topic attention module is used to decode. Finally, the final emotion classification result is obtained through the softmax function of the entire link layer and the output layer. The thematic attention modules constructed in this study are independent of each other for attention parameter adjustment and learning. One attention module corresponds to one film theme. In this study, eight themes, including "plot," "special effects," "original work," "music," "thought," "theme," "acting skills," and "joke," were extracted, and each theme was classified into three types of emotions: "positive," "neutral," and "negative." The experimental results on the crawled Chinese film review dataset show that the proposed algorithm is superior to some existing algorithms and models in accuracy, precision, recall and F1 measure. The DCLSTM based on the thematic attention mechanism (DCLSTM-TAM) model constructed in this study introduces the emotion vector into the network and adds the theme attention mechanism. It can not only classify the emotion for different topics of a film review but also effectively deal with film reviews with fuzzy emotional tendencies. It realizes the fine-grained emotion classification of film topics and improves the accuracy of emotion classification of film reviews. The emotion classification method and model proposed in this study have good transferability, and the change of training corpus is also applicable to other short text fields.

The Role of Geographic Information Systems (GIS) in Soil Surveying and Classification

The use of GIS is a technology that is not new, but it is constantly evolving, and that reliance on it in the agricultural field is increasing dramatically. By observing the vegetation cover and physical soil characteristics such as color, texture, water holding capacity and erosion, and soil chemical characteristics, especially evaluating the fertility of the soil for several years, it has become easy to employ this technology in soil surveys and land classification. Through geographic information systems, satellite images and remote sensing data, the studied lands expanded and it became easy to predict the characteristics of these lands in the future through simulating data with mathematical models. In addition, access to rugged and remote areas became easier through satellite images. Finally, this technology is considered as a shortening of time, effort and money to produce maps, graphs, or tables with flexibility, high accuracy, and abundant information that can be used at the present time to make important decisions or in the future.

Image Classification Under Class-Imbalanced Situation

Image classification technology processes and analyzes image data to extract valuable feature information to distinguish different types of images, thereby completing the process of machine cognition and understanding of image data. As the cornerstone of image application field, image classification technology involves a wide range of application fields. The class imbalance distribution is ubiquitous in the application of image classification and is one of the main problems in image classification research. This study summarizes the literature on class-imbalanced image classification methods in recent years, and analyzes the classification methods from both the data level and the algorithm level. In data-level methods, oversampling, under sampling and mixed sampling methods are introduced, and the performance of these literature algorithms is summarized and analyzed. The algorithm-level classification method is introduced and analyzed from the aspects of classifier optimization and ensemble learning. All image classification methods are analyzed in detail in terms of advantages, disadvantages and datasets.

Use-Case-Oriented Evaluation of Wireless Communication Technologies for Advanced Underground Mining Operations

This work aims to give an overview of wireless communication technologies (WCT) for underground applications. Difficulties regarding the harsh mining environment and operational constraints for WCT implementation and use are discussed. Selected technologies are then classified regarding underground mining-specific use cases in advanced mining operations. Use-case-based application categories such as 'automation and teleoperation', 'tracking and tracing' and 'Long-Range Underground Monitoring (LUM)' are defined. The use cases determine requirements for the operational suitability and also quantify evaluation criteria for the evaluation of WCT. The result is a comparison by category of the wireless technologies, which underlines potentials of different technologies for defined use cases, but it can be concluded that the technology always has to be evaluated within the use case and operational constraints.

Does regional services development enhance manufacturing firm productivity? A manufacturing servitization perspective

Based on the micro-firm data in China, this study investigates the impact of regional services development on manufacturing productivity from the perspective of manufacturing servitization. The results indicate that: (1) The uneven development of regional services has a 13.09% inhibitory effect on manufacturing firm productivity. (2) With the level of manufacturing servitization increases, regional services development will significantly improve manufacturing firm productivity by 19.98%. (3) This improvement effect differs significantly across service sectors and manufacturing technology categories, particularly pronounced in the financial and high-tech industries. (4) The higher the share of foreign service factor inputs used by the industry to which the firm belongs, the more the negative impact of regional services development on manufacturing firm productivity. These findings, which remain robust under two-way causality and indicator reconstruction, have important implications for the next stage of sustainable development with high-quality and effective integration of manufacturing and services in China.

Цифровая инфраструктура маркетинга сельских территорий

Abstract. Digital technological means are developing, the number of Internet users is growing, so marketing activities play a key role for every organization, companies in the agricultural economic sphere are not developing either. Marketing activity has the character of an opportunity to attract new consumers and build communications with old customers. If such activities are not used, then at present it is fraught with economic losses, and the loss of all business activities as a whole. A number of scientific and technological revolutions have changed a lot, the activity and features of the agro-industrial complex. Currently, a large number of specialists use the fact of implementation in the digital sector of the agricultural industry. This problem is becoming increasingly important in the formation and dissemination of projects aimed at the development of digital technologies in the industry. The purpose of this article is a study that draws attention to the activity of representatives of the agricultural type, and a proposal based on its assumption. Objectives: to conduct a bibliographic analysis of approaches to marketing the agricultural sector; to consider the key categories of digital technologies for agricultural enterprises; to analyze the use and wider application of marketing tools in the agricultural sector. Methods. When monitoring the sensors of socio-economic analysis, graphic and analytical methods, a systematic approach. Results. A study and description of the most common area of activity has been carried out, which allows expanding communication with users of agricultural products and increasing their number. The features of the use of digital technologies in the agricultural economy are revealed. Certain main reasons arising from the use of technological tools in the agricultural industry. Scientific novelty. It was revealed that enterprises in sectoral management often do not have the opportunity to purchase goods and sell the market. Solving the problem of wider use of digital communication technologies in the industry, the possible implementation of separate and distributed software products aimed at the development of the agricultural industry.

A Corpus-based Study of Stance Adverbs in TED Scientific Talks

As an important part of interpersonal communication, stance adverbs are used to express the speakers’ opinions, attitudes and style of delivery. However, stance adverbs in spoken registers have not received enough attention. This study analyzed the stance adverbs in TED scientific talks, with a particular focus on the most common certainty stance adverb really. This research is based on a self-built corpus composed of 80 TED talks in science and technology categories from 2017 to 2022, with a total number of 161,523 words. The findings indicated that epistemic stance adverbs, particularly certainty adverbs, were the most frequently employed category. The results also revealed that speakers of TED scientific talks tended to adopt really in the medial position in the clause. Meanwhile, really was mainly used to emphasize and express factual truth. The purpose of this study is to complement the existing research on stance adverbs in the spoken registers and provide suggestions for public speaking in academic discourse.

The visual design of urban multimedia portals.

In the visual design of a portal website, color is the first intuitive factor for users. It is relatively difficult for the designer of a city portal website to choose a color system that represents a city's unique color from among the many available options. Therefore, this study extracted a decision-making model of the urban color system, which can help decision-makers and designers choose among color systems, and then effectively design a portal website that conforms to local cultural attributes. The proposed method to solve the problem involved obtaining optimal color matching by performing weight analysis of colors through 123 sample color semantics, factor analysis, and a fuzzy analytic hierarchy process. Semantic analysis was used to classify colors into four categories of fashion, technology, calm, and dazzling. The fashion color matching scheme scored relatively high. Web page color matching schemes with a white background were popular, among which a white and green color matching scheme scored relatively high. At the same time, there are differences in color preferences between genders and cultures. This study is significant because it proposes a color decision model for portal websites, which provides a reference value that can also be applied to the selection of color schemes for other types of web pages in the future.

Technology for Adults with Intellectual Disability: Secondary Analysis of a Scoping Review.

Background. Technologies are embedded in all daily life activities and are utilized by occupational therapy practitioners to facilitate participation; evidence on technology for adults with intellectual disability (ID) can support practice with the population. Purpose. Summarize the evidence on technology to support adults with ID. Method. Secondary analysis of a scoping review of articles published between 2002 and 2018. Articles in the original scoping review addressed participation, assessment, or interventions with adults with ID. Authors completed a secondary analysis of 159 articles to identify articles with information on technology. Findings. In total, 56 articles met the inclusion criteria. Technology categories were cognitive supports, mainstream technologies, supplemental communication, aids for daily living, seating and mobility, control interfaces, and preparatory technologies. Implications. This secondary analysis demonstrates the range of evidence identifying technology as a key support, the impacts of the proliferation of technologies, and the need for occupational therapy practitioners to advocate for accessible technologies.

Assessing the performance of marine plastics cleanup technologies in Europe and North America

This study fills an important gap in policymaker understanding of the effectiveness and implementation costs of marine plastic cleanup technologies. Previous studies have mainly listed inventories of different plastic litter cleanup technologies, not systematically examined their cost-effectiveness. Through a survey in Europe and North America that asked for cost and cleanup capacity data, per kilogram cost figures were calculated for several categories of cleanup technologies, ranging from fixed-in-place filters in stormwater drainage systems to mobile skimmers on water. Mobile skimmers and dredgers appear to be most cost-effective. Adding performance criteria related to operational conditions and ease of use, such as energy use and economies of scale, dredgers and stormwater filters rank higher. Litter traps in drainage systems are most preferred when furthermore considering weather sensitivity and whether technologies operate standalone, making them an interesting complementary technology to skimmers and dredgers to prevent plastic litter from entering water courses. These results show how sensitive the outcome of the ranking is to the inclusion of additional operational criteria, how different categories of technologies can effectively be combined in the plastic waste supply chain, and what the cost implications are for the future upscaling of plastic litter cleanup technologies.

Fine hyperspectral classification of rice varieties based on self-attention mechanism

The accurate identification of rice varieties using rapid and nondestructive hyperspectral technology is of practical significance for rice cultivation and agricultural production. This paper proposes a convolutional neural network classification model based on a self-attention mechanism (self-attention-1D-CNN) to improve accuracy in distinguishing between crop species in fields using canopy spectral information. After experimental materials were planted in the research area, portable equipment was used to collect the canopy hyperspectral data for rice during the booting stage. Five preprocessing methods and three extraction methods were used to process the data. A comparison of the classification accuracy of different classification models showed that the self-attention-1D-CNN proposed in this study achieved the best classification with an accuracy of 99.93%. The research demonstrated the feasibility of using hyperspectral technology for the fine classification of rice varieties, and the feasibility of using the CNN model as a potential classification method for near-ground crop monitoring and classification.

Analysis and Synthesis of Technology for Textual Information Classification

The task of developing effective text information classification systems requires the thoughtful analysis and synthesis of variable components of technology. These components strongly affect the practical efficiency and the requirements to the data. For this purpose, a typical technology was discussed, comparing the regular “learning from features” approach versus the more advanced “deep learning” approach, that studies from data. In order to implement the technology, the first approach was tested, which included the means (methods, algorithms) for analysis of the features of the source text, by applying the dimensionality transformation, and building model solutions that allow the correct classification of data by a set of features. As a result, all the steps of the technology are described, which allowed to determine the way of presenting data in terms of hidden features in data, their presentation in a standard visual form and evaluate the solution, as well as its practical efficiency, based on this set of features. In a depth study, the informational core of the document was studied, using the regression and T-stochastic grouping of features for dimensionality reduction.The separate results contain estimation of practical efficiency of the algorithms in terms of time and relative performance for each step of the proposed technology. This estimation gives a possibility to obtain the best algorithm of intelligent data processing that is useful for a given dataset and application. In order to estimate the best suited algorithm for separation in reduced dimension an experiment was carried out which allowed the selection of the best range of data classification algorithms, in particular boosting methods. As a result of the analysis of the technology, the necessary steps of this technology were discussed and the classification on real text data was conducted, which allowed to identify the most important stages of the technology for text classification.

Urban Tree Canopy and Environmental Justice: Examining the Distributional Equity of Urban Tree Canopy in Guangzhou, China.

Rapid urbanization has led to a series of environmental problems that are harmful to the physical and mental health of residents. Not only can increasing urban tree canopy (UTC) improve sustainable city development, but it can also effectively promote the quality of life for residents; however, the uneven spatial distribution of UTC can also bring about social justice problems. Currently, there are few studies related to the justice of UTC distribution in China. Based on this, the paper adopts object-oriented image classification technology to interpret and extract UTC data from satellite images, and it uses house price to explore the justice of the spatial distribution of UTC in the main urban area of Guangzhou from the perspective of environmental justice by ANOVA, Pearson correlation coefficient, and bivariate local spatial autocorrelation. The results show that: (1) There is a significant positive correlation between UTC and house price in the main urban area of Guangzhou, and there are regional differences in the distribution of UTC, with the UTC in the "very high" house price group being significantly higher than that in other groups. (2) The spatial clustering of UTC and house price in the main urban area of Guangzhou is found to be "low-low" and "high-high"; thus, it can be concluded that the spatial distribution of UTC in the main urban area of Guangzhou is uneven. This is an environmental injustice, as the areas with low UTC are spatially clustered in old residential areas, while the areas with high UTC are spatially clustered in commercial housing estates with high house prices. The study shows that urban tree planting should not only focus on quantitative improvement but also on equitable spatial layouts to promote social equity and justice thereby improving the urban ecological environment and promoting healthy urban development.

Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics.

By using a radiomics-based approach, multiple radiomics features can be extracted from regions of interest in computed tomography (CT) images, which may be applied to automatically classify kidney tumors and normal kidney tissues. The study proposes a method based on CT radiomics and aims to use extracted radiomics features to automatically classify of kidney tumors and normal kidney tissues and to establish an automatic classification model. CT data were retrieved from the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19) in The Cancer Imaging Archive (TCIA) open access database. Arterial phase-enhanced CT images from 210 cases were used to establish an automatic classification model. These CT images of patients were randomly divided into training (168 cases) and test (42 cases) sets. Furthermore, the radiomics features of gross tumor volume (GTV) and normal kidney tissues in the training set were extracted and screened, and a binary logistic regression model was established. For the test set, the radiomic features and cutoff value of P were consistent with the training set. Three radiomics features were selected to establish the binary logistic regression model. The accuracy (ACC), sensitivity (SENS), specificity (SPEC), area under the curve (AUC), and Youden index of the training and test sets based on the CT radiomics classification model were all higher than 0.85. The automatic classification model of kidney tumors and normal kidney tissues based on CT radiomics exhibited good classification ability. Kidney tumors could be distinguished from normal kidney tissues. This study may complement automated tumor delineation techniques and warrants further research.

A novel text sentiment analysis system using improved depthwise separable convolution neural networks.

Human behavior is greatly affected by emotions. Human behavior can be predicted by classifying emotions. Therefore, mining people's emotional tendencies from text is of great significance for predicting the behavior of target groups and making decisions. The good use of emotion classification technology can produce huge social and economic benefits. However, due to the rapid development of the Internet, the text information generated on the Internet increases rapidly at an unimaginable speed, which makes the previous method of manually classifying texts one-by-one more and more unable to meet the actual needs. In the subject of sentiment analysis, one of the most pressing problems is how to make better use of computer technology to extract emotional tendencies from text data in a way that is both more efficient and accurate. In the realm of text-based sentiment analysis, the currently available deep learning algorithms have two primary issues to contend with. The first is the high level of complexity involved in training the model, and the second is that the model does not take into account all of the aspects of language and does not make use of word vector information. This research employs an upgraded convolutional neural network (CNN) model as a response to these challenges. The goal of this model is to improve the downsides caused by the problems described above. First, the text separable convolution algorithm is used to perform hierarchical convolution on text features to achieve the refined extraction of word vector information and context information. Doing so avoids semantic confusion and reduces the complexity of convolutional networks. Secondly, the text separable convolution algorithm is applied to text sentiment analysis, and an improved CNN is further proposed. Compared with other models, the proposed model shows better performance in text-based sentiment analysis tasks. This study provides great value for text-based sentiment analysis tasks.