Reviewerly/exHarmony huggingface dataset:
Differential Equation Model of Carbon, Nitrogen and Zinc Components in Growing Tomatoes

Tomato is a common food on the human table.Up to now, the research on the growth and development model of tomato has been about 50 years.There are many researches on the main nutrients of tomato, such as carbon and nitrogen, but few on the trace element zinc.In this paper, taking plant nutrient C, N and Z n as variables, the differential equation model of C, N and Z n in tomato growth and development was established.According to the research of tomato as a whole and divided into root and leaf, the one-compartment and two-compartment models of tomato growth and development were established.The model was analyzed by Matlab program, and the existing experimental data was used to test the numerical simulation results, which proves that the model conforms to the facts.
Pengaruh Konsentrasi NaOH terhadap Sifat Termal Komposit Eceng Gondok dan Carbon Nanotube (CNT) dalam Matriks HDPE

This study aims to determine the effect of alkali concentration on the thermal properties of HDPE composites with water hyacinth and carbon nanotubes (CNTs). The method used in this study is the experimental method. Composites made of natural fiber filler, the alkali treated water hyacinth. The water hyacinth was also filled was treated in alkali solution at various concentration (0%, 2%, 4%, 6%, and 8%). In addition to using water hyacinth fiber, the carbon nanotube (CNT) in a matrix of high-density polyethylene (HDPE). The composites were made using Rheomix OS machine. Furthermore, thermal testing of composite specimens using machine Thermogravimetric Analysis (TGA) were conducted. The results obtained from testing the thermal properties is the thermal resistance value. Thermal resistance of composites with alkali treatment of 0%, 2%, 4%, 6%, and 8% were 258,9 0C, 259,5 0C, 260,3 0C, 264,0 0C, and 265,9 0C, respectly. Based on these results it can be concluded that the concentration of NaOH improves the thermal resistance of the composite.
Wearable Multi-Sensor Gesture Recognition for Paralysis Patients

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Multilingual-Word-Script Classification in Text Video Frames

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Integrating ICT Toward Efficient Registration System in Division of Batangas Province

Technology plays a vital role in today’s generation. In the world where the only change is constant, technology is inevitable even in disseminating information. With this technological change, computerbased mechanisms and schemes also coped up with the advances. The development of technology, devices and machineries have been developed and reversed in order to meet the demands of the new world. Various systems are designed to be accurate, efficient and beneficial to the user. Most institutes today recognize the importance of computers in meeting the pressing market demands. In the local context, the Division of Batangas Province fully supports and promotes the K-12 curriculum and offers three Strands under Senior High School. The said division has been very serious in accepting new students yet they still deal with manual enrollment system. It is undeniable that in the Division of Batangas, Senior High schools have the current situation in terms of listing and sectioning, manual listing before encoding, time consumption, a large number of enrollees and the quality of education given. In this qualitative research which maximized Discourse Analysis, the paper withheld that the problems encountered while enrolling are a long process of manual enrollment, limited place, and the school has insufficient facilitators making it hard to accommodate all the enrollees. With this, Registrar’s offices of the whole division will be empowered with greater efficacy and efficiency during the enrolment period. Moreover, accuracy, information dissemination and faster release of results would more likely be gained with the ICT based enrolment scheme. WWW or Website as a Working Weapon was developed with an online form for the enrolment system of the said school and being proposed.
Discovering The Stock Tweet Attribute. A Case Study @IDX_BEI

University needs to monitor the market development, to find out the developing interests of the students for their future, and to find out the changing need of the study program.University also needs to determine the level of public recognition toward the university and whether the universities become the choice of the senior high school student.The research conducted in the area of West Jakarta.The research conducted is a survey of senior high school students from 20 schools.The findings show that access to easily accessible locations, study programs with A accreditation as well as universities that have proper buildings and facilities are the main subject for students to choose universities.Furthermore, parents and the consulting teacher are a significant influencer to choose the university.Moreover, the student has the right decision to choose the study program.
The Network

July 21 2022 The Network Nick Cronbach Nick Cronbach Search for other works by this author on: This Site Google Scholar Author and Article Information Nick Cronbach The Network Coordinator, Senior Editor Online Issn: 1530-9282 Print Issn: 0024-094X © ISAST2022ISAST Leonardo 565. https://doi.org/10.1162/leon_e_02273 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Nick Cronbach; The Network. Leonardo 2022; doi: https://doi.org/10.1162/leon_e_02273 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsLeonardo Search Advanced Search This content is only available as a PDF. © ISAST2022ISAST Article PDF first page preview Close Modal You do not currently have access to this content.
THE SNEZHINKA GOLD ORE DEPOSIT IN THE URKINSKY ORE-PLACER CLUSTER OF THE AMUR PROVINCE

There are about 100 gold deposits in Kyrgyzstan, including large and super large ones. According to the time of their formation, several stages are distinguished, the main of which are Paleozoic. A brief overview of these stages, the geodynamic setting, and examples of the gold deposits with description of several large objects are provided.
Natural gradient evolution strategies for adaptive sampling

We evaluate two (1+1)-natural evolution strategies (NES) turned into adaptive Markov chain Monte Carlo (MCMC) samplers on a test suite of probability distributions. We compare their performance with the AM-family of samplers considered to be the state of the art in adaptive MCMC. Our experiments show that natural gradient based adaptation used in NES further improves adaptive MCMC.
SOCIAL INNOVATION APPROACHES AT HIGHER EDUCATION INSTITUTIONS

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 204-212ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0066Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
An Efficient and Robust MIoT Communication Solution using a Deep Learning Approach

Due to the volume of multimedia sensed data, a network of Multimedia Internet of Things (MIoT) devices faces various challenging constraints, most notably in terms of communication overhead, power consumption, and memory usage. A set of these MIoT devices is unable to overcome the large data-size challenge via the use of the Lossy Multimedia Compression (LMC) such as JPEG and BPG since they are limited in memory and computation. Instead, in this paper, we propose to down-scale images at MIoT devices with a factor of 2, 3 or ≥ 4, which reduces the memory consumption, computation, and communicated data size and consequently the latency and energy consumption. To recuperate high-quality images, we apply a Deep Learning (DL) denoising/super-resolution model at the server-side. On the other hand, as MIoT devices use a wireless connection, there is a higher risk of transmission packets loss compared to a wired connection. Almost, packets loss are managed through costly data re-transmissions or data redundancy. However, these solutions with intrinsically voluminous data such as the multimedia one are costly, especially for limited MIoT devices. To overcome this challenge, the denoising/super-resolution model did also undergo a training model to retrieve high-quality images from down-scaled erroneous ones. The obtained results show how effective this proposed solution is, especially when it comes to the enhancement of visual quality of down-scaled and erroneous images with minimum communication, latency, and consequently resource overhead.
Hybrid Recommendation Based on Matrix Factorization and Deep Learning

Deep learning (DL) is playing an increasingly important role in the field of recommender systems (RSs). In this paper, we enhance the performance of a DL-based RS by incorporating matrix factorization (MF), which gained a great deal of popularity as a result of the Netflix Prize competition. Thus, DL is responsible for learning the nonlinear relationship between users and items, whereas MF is used to describe the linear relationship between users and items. We use the typical DL architecture of the multilayer perceptron, and use layer normalization and the residual to improve its performance. Our experimental results showed that the proposed method can make recommendations accurately.
Management platform for data translators supported by semantic annotations

Service-Oriented Architectures (SOA) play an important role in systems/devices interoperability in the Industrial Internet of Things (IIoT), allowing the management of the interaction between consumers and producers of a given service. Despite this, when the systems cannot understand each other, it is necessary to create translators that allow their communication. The TAG-Tool is an example of an automatic generation tool for semantic data translators using annotated metadata with concepts from a reference ontology. A management approach is proposed for the use of tools for automatic generation of translators, such as the TAG-Tool, which makes this management more efficient, namely by managing the systems that can be related, thus allowing control over the translators who can be created. This way, it is possible to store created translators, avoid creating them whenever they are requested, as well as use downtime to generate non-existent and obsolete translators. This management is a relevant addition in automating the generation of translators and integration with IoT management systems. A tool was developed to validate the proposed approach.
Design and implementation of question recommendation system based on deep knowledge tracing

Online education has developed rapidly since 2020, and the completion of after-school exercises is a part of online education, which plays an important role in improving students’ knowledge. However, the existing question recommendation systems mainly have two problems: (1) The question recommendation is completely based on the parametric theoretical model. The parametric theoretical model parameterizes the questions and the students’ ability to answer the questions, so it cannot provide a personalized question recommendation strategy. (2) The question recommendation strategy depends on the teacher’s formulation, and the efficiency is not high. In order to solve the above two problems, this paper is based on deep knowledge tracing and uses a strategy for recommending questions for students’ weak knowledge points. This method first uses the deep knowledge tracing model to model students’ personal knowledge level, and then finds out students’ weak knowledge points. Recommend questions for students’ weak knowledge points. Under the real experimental data set, this method can recommend personalized questions for students without the participation of experts.
The Recent Advancements of Radio Frequency Machine Learning (RFML) Approaches in Enhancing Wireless Security Using Multi Regression Analysis Approach (MRAA)

Whereas the deep learning (DL) methods were already widely used in state-of-the-art Computer Vision (CV) as well as Natural Language Processing (NLP) implementations, it has only been in recent times that these innovations have begun to grow up a bit adequately in applications involving wireless communications, a field known as Radio Frequency Machine Learning (RFML). Particularly noteworthy is new studies that has demonstrated that deep learning (DL) is an enabler innovation for Cognitive Radio (CR) applications, as well as a valuable tool for enhancing the performance of skillfully specified techniques for frequency recognition applications including such signal identification, prediction, or categorization. Since there is an availability of comparable raw Radio Frequency (RF) information available to enable learning and assessment, almost no previous information about the target spectrum environment is necessary. This is a primary motivation for the use of RFML. Apart from the basic necessity for adequate information, there are many other important aspects to taken into perspective before using RFML systems in genuine wireless communication applications. These concerns include trustworthiness, security, and technical specifications. The sharing and disseminated nature of wireless medium makes wireless systems susceptible to a variety of threats such as interference and surveillance. Machine learning (ML) offers an autonomous technique of learning from and adapting to wireless communication features that are difficult to obtain using custom characteristics & algorithms, which may be used to assist both attacking and defensive tactics. Based on this research ways which support the application of machine learning in enhancing the security.
A hybrid K-means model based on logistic regression and decision tree algorithm for labor classification recommendation

Abstract-Logistic regression and decision tree algorithms have their own drawbacks. The former is more sensitive to multicollinearity data and has difficulty in dealing with data imbalance; the latter may increase errors more rapidly when has too many categories. The question of how to overcome the shortcomings of these two models has become a hot subject in academia and industry, garnering the attention of many researchers. We provide a hybrid K-means model based on logistic regression and decision trees in this study. To begin, this research employs a publicly accessible student data collection, with the first 70% serving as the training set and the last 30% serving as the test set. Furthermore, to assess the data's correctness, this article employs logistic regression, decision trees, and the proposed model. Second, the model's prediction outcomes were evaluated using three metrics: ME, RMSE, and MAE. Finally, the results of the experiments reveal that our hybrid model is more efficient. By assessing the student's fundamental information and connecting the student with the most appropriate labor education approach for that student, it is possible to make the best categorization suggestion.
Education Internationalization Strategy Management System Based on Fuzzy Neural Network

research-article Education Internationalization Strategy Management System Based on Fuzzy Neural Network Author: Rong Huang Jiangxi Vocational College of Mechanical & Electrical Technology, China Jiangxi Vocational College of Mechanical & Electrical Technology, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 708–713https://doi.org/10.1145/3544109.3544339Published:18 July 2022Publication History 0citation13DownloadsMetricsTotal Citations0Total Downloads13Last 12 Months13Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Deep Fusion Cnn Based Hybridized Strategy for Image Retrieval in Web: A Novel Data Fusion Technique

There is always a need for an efficient and effective Content Based Image Retrieval (CBIR) classification due to the constant development in the number of large-scale repository.During the last few years, there has been a tremendous increase in activity in using multimedia data including both scientific and commercial domains.As a result, it's necessary to organize, store, analyze, and present available facts that meet user needs.Visual components are used in CBIR to find a picture from a large image document based on the user's interest and instantaneously query sequence attributes.The term 'content' may relate to the image's low-level properties such as color, form, or material.The need for CBIR arises because most image retrieval algorithms rely solely on textual information, resulting in a lot of garbage in the outcomes.Furthermore, searching for photos in a large database using keywords might be costly, inefficient, and fail to convey the user's purpose to describe the picture.To address this, the proposed research suggests "JustClick": a unique data fusion approach based on the Deep Fusion Convolution Neural Network (DFCNN) method for enhanced extraction of features.With the notion of intent research, this approach hybridizes linguistic and visual commonalities to capture the user's purpose.Only one click on a query picture is required for the images returned by text-based searches to be re-ranked dependent on their linguistic and visual similarity to the image database.The suggested system's performance is proved by making comparisons to text-based and content-based systems.The suggested JustClick system provides an effective automatic retrieval of comparable photos with better extracting of the features, yielding encouraging results with retrieving effectiveness of 97.7% on average.
Research on English Semantic Intelligent Analysis Method Based on Modern Intelligent Recognition Technology

From the semantic point of view, word semantic calculation can be defined in the whole text or between single word meanings, so word semantics have relevance and similarity, that is, it reflects the commonness of two words in the same context and the aggregation characteristics between two words. English semantic intelligent recognition is the basic technology in English fast input, simultaneous translation and other application fields, which can greatly improve the efficiency of English reading and communication. However, in practical application, English character recognition is easily interfered by noise, which seriously affects the efficiency of English semantic recognition. Based on modern intelligent recognition technology, an English semantic analysis method is designed. Modern intelligent recognition technology can extract the information of all words in the text, accurately identify the long and short patterns of text data, and accurately identify the emotional information contained in real and random English text. This paper studies the English Semantic Intelligent analysis method based on modern intelligent recognition technology, which shows that modern intelligent recognition technology has higher accuracy and calling rate in the process of sentence translation, especially in the English translation of prepositions, function words and tenses.
OTE: An Optimized Chinese Short Text Matching Algorithm Based on External Knowledge

Short text matching is a key problem in natural language processing (NLP), which can be applied in journalism, military, and other fields. In this paper, we propose an optimized Chinese short text matching algorithm based on external knowledge (OTE). OTE can effectively eliminate semantic ambiguity in Chinese text by integrating the HowNet external knowledge base. We use SoftLexicon to optimize the word lattice graph to provide more comprehensive multi-granularity information and integrate the LaserTagger model and EDA for data augmentation. Experimental results show that OTE has an average accuracy improvement of 1.5% in three datasets compared with existing models.