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Sentiment Analysis on Hydroponic Technology Application for Urban Farming Limitations

Sentiment Analysis on Hydroponic Technology Application for ...

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Emerging technologies in Electronics, computing and Communication (ICETECC), international conference on

作者： Victor Q. Parillas Jocelyn M. Beriña Evelyn M. Baesa Edralin R. Raro Thelma Palaoag School of Engineering and Computer Studies Divine Word College of Legazpi Legazpi City Philippines College of Information Technology Baao Community College Baao Camarines Sur Philippines Institute of Computer Studies Camarines Norte State College Camarines Norte Philippines Institute of Computer Studies College of Agriculture and Natural Resources Camarines Norte State College Camarines Norte Philippines College of Information Technology and Computer Science University of the Cordilleras Baguio City Philippines

The world is undergoing rapid change due to current events. The internet has become a fundamental necessity for everyone, with Web use in virtually every area. With the growth of social networking, people use these platforms to express their views on daily topics. Hydroponics can help urban agriculture operations when residents have limited land. Despite the benefits of a hydroponic technology, it is not widely used, even in urban areas. The researchers used Twitter to analyze hydroponic farmers’ product sentiment. The data was gathered and evaluated using qualitative research methods and sentiment analysis. The tweets were gathered using the Tweepy Module and the keyword hydroponics. The gathered English tweets underwent tokenization, filtering, standardization, and segmentation. Valence Aware Dictionary for Sentiment Reasoning (VADER) was used to categorize the corpus. After scraping 6429 tweets, 5181 cleaned tweets were categorized as positive, negative, or neutral. Researchers used a pie chart and violin plot for output. Five hundred fifty-three tweets are negative, 2922 are positive, and 1706 are neutral. This result showed 56% liked hydroponic technology. 33% of neutrals liked hydroponics, while 11% disliked it. The hydroponic system has several advantages over conventional farming and some disadvantages. It is recommended that modern farmers be given hydroponic technology application awareness programs to maximize their benefits. Additional studies on hydroponic applications in different agricultural settings and crops are also needed to improve the technology’s success rate.

关键词： Sentiment analysis Dictionaries Social networking (online) Filtering Blogs Urban areas Hydroponics

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Mitigation of Rumours in Social Networks via Epidemic Model-based Reinforcement Learning

Mitigation of Rumours in Social Networks via Epidemic Model-...

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international conference on data science and Advanced Analytics (DSAA)

作者： H Ruda Nie Xiuzhen Zhang Minyi Li Anil Dolgun School of Computing Technologies RMIT University Melbourne Australia Faculty of Natural Science and Technology Tay Nguyen University Buon Ma Thuot Vietnam

ISBN: (纸本)9781665473316

While detection of rumours in online social networks has been intensively studied in the literature, mitigation of the spread of rumours has only recently gained attention and remains a challenging task. Some studies developed user opinion models to find top influential users as debunkers to spread the truth to counter rumour spread. Other studies designed an intervention framework to optimize the mitigation activities for given debunkers. The issue of optimizing the selection of debunkers in a dynamic environment where users’ beliefs and behaviour change remains under investigated. This paper addresses this issue by proposing a rumour mitigation approach based on the deep reinforcement learning framework. In particular, we model the changes in users’ beliefs with an epidemic model. We further employ deep reinforcement learning to train an agent to learn a multi-stage policy for selecting the optimal debunkers to inject truthful information under a budget constraint. Our model selects debunkers to inject truthful information at multiple stages with an overall objective to maximize the number of users who will believe in the true information (a.k.a number of recovered nodes), such that the spread of rumours is minimized. Our experiments on synthetic and real-world social networks show that our proposed method for rumour mitigation can effectively minimize the spread of rumours.

关键词： Deep learning Epidemics Analytical models Social networking (online) Decision making Reinforcement learning data science

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Diabetes disease prediction using decision tree for feature selection 1

Diabetes disease prediction using decision tree for feature ...

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1st international conference on advances in Computational science and Engineering, ICACSE 2020

作者： Sadhasivam, Jayakumar Muthukumaran, V. Thimmia Raja, J. Joseph, Rose Bindu Munirathanam, Meram Balajee, J.M. Vellore Institute of Technology Vellore India Department of Mathematics School of Applied Science REVA University Bengaluru - 64 India School of Computing Kalasalingam Academy of Research and Education Tamilnadu 626128 India Department of Mathematics Christ Academy Institute for Advance Studies Department of Mathematics Rajiv Gandhi University of Knowledge Technologies School of Information Technology and Engineering Vellore Institute of Technology Vellore India

In this paper more than one approaches are evaluated to optimise machine learning models for diabetes disease diagnosis. The main goal is to sort the medical data computation and choose the most suitable parameters to construct a faster and more perfect model using feature selection. Reducing the number of features to construct a model could direct to more useful machine learning algorithms which helps the doctors to focus on what are the most significant assessment to take into story. Feature selection is one of the process in machine learning which choose a subset of topical features namely variables for construction of models. In this research paper we use three feature selection techniques like Recursive Feature Elimination (RFE), Genetic Algorithm (GA) and Burota Package. After using feature selection at the end we use Decision Tree to predict the diagnosis Diabetes using a dataset named Pima Indian Diabetes dataset and verify the performance of result model. © Published under licence by IOP Publishing Ltd.

关键词： Feature Selection

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Empirical Investigation on Smart Wireless Autonomous Robot for Landmine Detection with Wireless Camera

Empirical Investigation on Smart Wireless Autonomous Robot f...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Madhu Kumar Vanteru Harpreet Singh Bedi Mohammed Azam M. Kalyan Chakravarthi R. Raffik Bhasker Pant Department of ECE Balaji Institute of Technology and Science Narsampet Warangal Telangana India School of Electronics and Electrical Engineering Lovely Professional University Jalandhar Punjab India Electronic and Communication Engineering ISL College of Engineering International Airport Road Bandlaguda Chandrayangutta Hyderabad Telangana India School of Electronics Engineering VIT - AP University Amaravati Andhra Pradesh India Department of Mechatronics Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Landmines are hazardous explosive devices that endanger lives and disrupt economies, finding and removing them requires specialized knowledge. To effectively find mines and pinpoint their whereabouts, a variety of technologies are available. However, most innovation should be utilized to benefit people and civilization in order to significantly increase its good impact at the detriment of its drawbacks. This paper’s goal is to emphasize the value of incorporating robotics, communication, and analysis of data into the field of landmine detection. It covers the methods that can help the autonomous robot find mines utilizing wireless camera and sensors. The suggested technology has the ability to fully operate the robot and conFigure the robot operator’s rights and restrictions. It can also save and preserve the printed statements and navigational outcomes in a separate database. Additionally, this method considerably lowers the hazard to human detectors’ safety when they stay in a minefield.

关键词： Wireless communication Wireless sensor networks Technological innovation Landmine detection Robot vision systems Cameras Sensors

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Visual-Lexical Alignment Constraint for Continuous Sign Language Recognition

Visual-Lexical Alignment Constraint for Continuous Sign Lang...

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IEEE international conference on Cognitive Informatics

作者： Liangjun Guo Rize Jin Joon-Young Paik Yenewondim Biadgie Weiwei Yang School of Computer Science & Technology Tiangong University Tianjin China School of Software Tiangong University Tianjin China Department of Software Ajou University Suwon South Korea General Data Technology Co. Ltd. Tianjin China

The aim of Continuous Sign Language Recognition (CSLR) is to recognize unsegmented signs from image streams and convert them to gloss sequences. data collection and annotation of sign language is costly, most recent CSLR works handle this problem in a weakly supervised manner and adopt network architectures consisting of a feature extractor and an alignment module. In their design, the gloss-level semantic feature is used to align the gesture-level representational feature, which may suppress the activation of non-key frames. To further enhance the generalization of the visual extractor, this paper proposes a Visual-Lexical Alignment Constraint (VLAC) with an improved self-distillation based alignment supervision. Specifically, the proposed model adopts a visual-lexical module to supervise the visual and the lexical modules separately. In addition, we modify the traditional lexical extractor from Bi-LSTM to lite-transformer encoder, which is beneficial in terms of parallelization and computational efficiency. Experimental results on the RWTH-PHOENIX-Weather-2014 dataset show that the proposed model exceeds the current state-of-the-art models.

关键词： Visualization Image recognition Computational modeling Semantics Gesture recognition Assistive technologies Streaming media

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Using the Linked data for Building of the Production Capacity Planning System of the Aircraft Factory 4th

Using the Linked Data for Building of the Production Capacit...

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4th international Scientific conference on Intelligent Information technologies for Industry (IITI)

作者： Yarushkina, Nadezhda Romanov, Anton Filippov, Aleksey Ulyanovsk State Tech Univ St Severny Venets 32 Ulyanovsk 432027 Russia

ISBN: (纸本)9783030500979

The basic principles of data consolidation of the production capacities planning system of the large industrial enterprise are formulated in this article. The article describes an example of data consolidation process of two relational databases (RDBs). The proposed approach involves using of ontological engineering methods for extracting metadata (ontologies) from RDB schemas. The research contains an analysis of approaches to the consolidation of RDBs at different levels. The merging of extracted metadata is used to organize the data consolidation process of several RDBs. The difference between the traditional and the proposed data consolidation algorithms is shown, their advantages and disadvantages are considered. The formalization of the integrating data model as system of extracted metadata of RDB schemas is described. Steps for integrating data model building in the process of ontology merging is presented. An example of the integrating data model building as settings for data consolidation process confirms the possibility of practical use of the proposed approach in the data consolidation process.

关键词： Relational databases data model schema Metadata Ontology Ontology merging data consolidation Integrating data model Production capacity planning system

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Multimodal Sentiment Analysis using Kernel Based Support Vector Machine

Multimodal Sentiment Analysis using Kernel Based Support Vec...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： V Sunil Kumar Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Ashish Khanna Deepak Gupta Madhumala R B Department Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceará Brazil CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

There has been a steady expansion in the number of social websites, electronic media outlets, and blogging services, all of which contribute a large number of user messages such as comments, reviews, and opinions from customers. The quantity of these signals, which are used to evaluate the quality of a wide variety of goods and services, is rather substantial. As a result, it is challenging to monitor and identify the feelings of customers. As a result, a method known as Sentiment Analysis (SA), also known as Opinion Mining (OM), was developed. This method gathers data from the internet by using Natural Language Processing (NLP) strategies and then analyses people's thoughts. It does this by extracting subjective information from the words, assessing opinions, and quantifying positive and negative evaluations. The end result is a conclusion about the specific entity, which may be an organisation, the public, or a product. The NLP first gathers the views, and then those opinions are sorted into positive, neutral, and negative categories. Both the marketing promotion and the customer's ability to choose the most appropriate product for their needs may be improved by doing an analysis. The purpose of this study is to evaluate the use of different machine learning and swarm intelligence optimization methods to perform sentiment analysis on data derived from movie reviews. This study helps many businesses increase their profits while simultaneously lowering the number of problems caused by a certain product. Within the scope of this investigation, three distinct models of sentiment analysis were investigated. An effective method for doing sentiment analysis known as a Hybrid Kernel based Support Vector Machine (HKSVM) has been presented. The preprocessing that is described here is meant to cut down on the data inconsistencies and get rid of the data that isn't needed. After that, the Expectation Maximization (EM) technique is used in order to identify an outlier. After t

关键词： Support vector machines Sentiment analysis Analytical models Sensitivity Veins Tagging Probabilistic logic

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Certain Electroenchaphalography Signal Analysis for Artifact Removal Using Multiwavelet Transform

Certain Electroenchaphalography Signal Analysis for Artifact...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Basavesha D Madhumala R B Prashanth Venkatareddy Sapna Kadakadiyavar Piyush Kumar Pareek Surabhi K R Department of Computer Science & Engineering Shridevi Institute of Engineering & Technology Bengaluru Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore Department of Electrical & Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru Department of Electronics and communication Sambhram Institute of Technology Bangalore Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

This work presents an efficient strategy for eliminating mixed artifacts that are present in electroencephalogram recordings. All three categories of recordings are suitable candidates for use of the approach. The presence of the noise source makes it more challenging to do an analysis of the EEG and get clinical information. This is because the EEG is being measured in a noisy environment. EEG signals are biological signals that take place in the temporal domain and are multidimensional, non-stationary, and irreproducible. EEG signals are also known as electroencephalogram signals. Electroencephalogram signals are sometimes often referred to as EEG signals. This suggests that the statistical qualities of these people do not remain the same over time as shown by the fact that this is the case. It is anticipated that it will offer information on the activities that are taking place at the level of the ensemble of excitatory pyramidal neurons, with a temporal resolution scale of milliseconds. This prediction is based on the fact that the scale measures time in milliseconds. It is highly difficult to derive any useful information from scalp EEG since it includes a substantial amount of background noise and artifacts, and the particular area from which it originates cannot be determined. As a result of these factors, it is quite difficult to analyze scalp EEG. This is because the background noise and artifacts might make it difficult to determine the source of the signal. As a result of this, it is of the utmost importance to develop specific methods for reducing the occurrence of artifacts in EEG recordings, such as the one in question. These methodologies need to be created as quickly as time permits. In this piece of research, we suggest making use of the Multiwavelet transform to clean up the EEG data and get rid of any artifacts that could be present. Following the use of the GHM algorithm, which cleans up the noisy signal produced by the Multiwavelet transform, th

关键词： Time series analysis Scalp Transforms Electroencephalography Time measurement Recording Noise measurement

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ECPASurv: Exploring Career Path with Attention Mechanism and Survival Analysis

ECPASurv: Exploring Career Path with Attention Mechanism and...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Thien D. Truong Nguyen Q. H. Ton Chau N. Truong Son P. Nguyen Orient Software Development Corp Da Nang Viet Nam Danang University of Science and Technology Da Nang Viet Nam Neurond Technology JSC Da Nang Viet Nam

ISBN: (纸本)9781665461672

Employees' career paths can be predicted by their historical experience in order to understand employee intention, accurate predictions permit organizations to make the right decisions for the retention or recruitment planning of employees. Survival analysis is widely used in clinical research but receives little attention in other domains despite its superiority in handling censored data, which frequently appear in real-world datasets. To take advantage of the survival analysis, we propose a new model, named as ECPASurv. The proposed model is built to handle two problems, with the first one being the prediction of the risk score of an employee and the second one related to the forecast of the next job title that an employee may want to apply for. Unlike previous work, ECPASurv is the combination of transformer encoder and survival analysis that can effectively utilize the hidden information of employee experience's transition. The use of the transformer encoder gives better results for both survival function prediction and next job title prediction. For the case studied and proved the efficiency of the proposed model, we created some experiments with more than 10.000 profiles; each has one or multiple job experiences and a subset of skills.

关键词： Analytical models Engineering profession Organizations Predictive models Transformers Communications technology Planning

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Towards Smart Farming: Simulation Framework to Exploit Network Connectivity for End-to-End data Transmission

Towards Smart Farming: Simulation Framework to Exploit Netwo...

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international conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA)

作者： Dongyang Huo Sri Devi Ravana Asad Waqar Malik Anis Ur Rahman Ismail Ahmedy Department of Information Systems Faculty of Computer Science & Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computing National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Computer Systems & Technology Faculty of Computer Science & Information Technology University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9781665493352

Traditional agriculture is highly dependent on human participation and experience. Therefore, it is not possible to guarantee crop yield and quality while expanding the production scale. In recent years, based on the continuous development and innovation of IoT and ICT technologies, the concept of smart farming has started to receive the attention of researchers and farmers. However, many existing smart agriculture applications and research are conducted under controlled environments such as greenhouse farming, vertical farming, and hydroponic farming, etc. Few studies have shifted attention to large-scale arable agriculture, thus neglecting the scalability of the system. This work presents a simulation stack (digital mapping) in which the results of running the simulation model in a large-scale class of real-world scenarios are used to enhance the further performance of the proposed farming system in large-scale scenarios.

关键词： Smart agriculture Technological innovation System performance Scalability Production data models Software

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