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Scraping Google Scholar data Using Cloud computing Techniques

Scraping Google Scholar Data Using Cloud Computing Technique...

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Contemporary Information Technology and Mathematics (ICCITM), international conference on

作者： Nagham A. Sultan Dhuha B. Abdullah Department of Computer Science University of Mosul Mosul Iraq

The current revolution in Internet technologies has led to a tremendous amount of data on the world wide web (www). Dealing with such big data is not an easy task in terms of collection and analysis. Moreover, the unstructured nature of most of this data makes it more difficult to collect and analyze. Therefore, scraping and crawling techniques are considered the most common types of data collection from the web. In this paper, a scraper is developed based on two methods to scrape data from one of the most complicated platforms on the web, which is Google scholar. These two methods are Beautiful Soup and SERP API. The proposed scraper is divided into three parts: the web scraper finds the needed links on the internet, the data is extracted from the source links, and lastly the data is stored in a CSV file. For implementing the scraper, Python language is used. The results showed that the designed scraper is efficient in retrieving data from Google scholar. Also, it was found that Python is a suitable and efficient language for scraping necessary data from a particular website of interest due to the abundance of library resources.

关键词： Cloud computing Big data World Wide Web Mathematics Libraries data mining Web sites

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Smart Cattle: Cattle Live Weight Estimation Based on a Deep Learning Approach 1

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9th international conference on Computational science and Technology, ICCST 2022

作者： Jaini, Nur Lydia Jane Binti Mohd Alfred, Rayner Gobilik, Januarius Obit, Joe Henry Fui, Florence Sia Sainin, Samry Mohd Shamrie Loudin, Raymond Victor Iswandono, Zamhar Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Faculty of Sustainable Agriculture Universiti Malaysia Sabah Sabah Malaysia Infinity Enterprise Sabah Malaysia Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9789811984068

ISBN: (纸本)9789811984051

Smart Cattle is a suite of technologies for boosting the quality and quantity of production in dairy farms. Measuring cattle’s live weight is essential as the captured data could be used to monitor cattle’s health and nutrition management. However, weighting cattle and consistently monitoring its weights of is time consuming and require intensive labour. Profitability of beef production system depends highly on the annual live weight gain, stocking rate, feed quality, feed quantity, and the balance between these factors. The lighter cattle causes farmers to lose profit. In feedlot, where the feed can be systematically rationed, this problem can be mitigated with feeding correction. The limitation of this approach is to monitor the live-weight and growth rate of the cattle, as the farmers need to weigh the animals at consistent interval such as once in a month during the production period. This task is laborious, time consuming and costly. Hence, the aim of this work is to develop a live weight estimation system that will be able to perform a physical weight estimation of a cattle using a deep learning approach. The objectives of this work are to design a deep learning model, specifically a Convolutional Neural Network (CNN) for cattle weighting estimation system based on features extracted from the images, to implement an optimized deep learning model for cattle physical weight estimation based on high resolution images and finally to evaluate the performance of the proposed cattle weight estimation system using a deep learning model. This approach can be used to replace traditional methods of weighing using electronic scale and direct observation and measurement can be made based on the cattle images. In this work, the impact of dropout rates on the performance of convolutional neural networks will also be studied for image classification. Based on the results obtained, the proposed CNN model with dropout regularization produced higher performance accuracy of 54.0%

关键词： Convolutional neural networks

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Smart watering system based on framework of low-bandwidth distributed applications (LBDA) in cloud computing 12th

Smart watering system based on framework of low-bandwidth di...

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12th international conference on Intelligent Networking and Collaborative Systems, INCoS 2020

作者： Sirimorok, Nurdiansyah As, Mansur Yoshida, Kaori Köppen, Mario Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Creative Informatics Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9783030577957

The estimation of soil moisture content is required for agriculture, mainly to build the irrigation scheduling model. In this study, we present a smart watering system to deal with various factors derived from the stochastic information in the agricultural operational, i.e., air temperature, air humidity, soil moisture, soil temperature, and light intensity. The methodology consists of exploiting the Internet of Things (IoT) using Low-Bandwidth Distributed Applications (LBDA) in cloud computing to integrate the real data sets collected by the several sensor technologies. We conducted experiments for the watering system used two types of soil and different plant. Here, the Long Short Term Memory Networks (LSTMs) approach in deep learning techniques used to build smart decisions concerning watering requirements and deal with heterogeneous information coming from agricultural environments. Results show that our models can effectively improve the prediction accuracy for the watering system over various soil and plants. © Springer Nature Switzerland AG 2021.

关键词： Cloud computing

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Fake News Detection and Classify the Category

Fake News Detection and Classify the Category

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Trends in Quantum computing and Emerging Business technologies (TQCEBT), international conference on

作者： Pritha Mitra Lija Jacob Department Of Data Science Lavasa Campus Christ University Pune Pune India

ISBN: (纸本)9781665453622

Everyone depends on numerous sources of E-news in today’s world when the internet is ubiquitous. Online content abounds, especially social media feeds, many of which are unreliable and may not always be factual. For people to utilise social media platforms like Facebook, Twitter, and others, fake news is a topic that may be studied through Natural Language Processing techniques. Using ideas from natural language processing and machine learning applied to social media, our goal in this work is to conduct categorization of different news items that are available online. Our intention is to empower the user to utilise NLP (Natural Language Processing) methods to identify “fake news,” which refers to misinformed material that may be categorised as genuine or false using software like Python. The model focuses on identifying false news sources based on several articles from a website, categorising the news as false or true, and determining its veracity using unreliable sources like scikit-learn and NLP for textual analysis of the website distributing the news. When a source is identified as a publisher of false news, which can be predicted with high vectorization and also suggested using the Python scikit-learn module to do tokenization and feature development, biased viewpoints may be identified and categorised in any subsequent articles from that source.

关键词： Support vector machines Quantum computing Social networking (online) Computational modeling Prototypes User interfaces Tokenization

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Building NLP Tools to Process Sinhala Text data Written using English Letters

Building NLP Tools to Process Sinhala Text Data Written usin...

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international conference on advances in ICT for Emerging Regions

作者： W.S.A. Kurera R.K. Rajapaksha H.A.P. Rupasinghe K.L.D.U.B. Liyanage Sanvitha Kasthuriarachchi Samantha Rajapakshe Faculty of Computing SLIIT Malabe Sri Lanka

Sri Lanka is a country where the Sinhala and English languages take a prominent place, but most people are not so fluent in the two languages when using digital platforms like social media. Although most of the Sri Lankans use Singlish there is no proper application to translate the relevant language into the main two languages, Sinhala or *** purpose of this research is to bring more attention of the researchers towards the sub varieties of Sinhala language, especially Singlish by bridging the gap between NLP tools available for processing textual data of main languages and their varieties. And each component focuses on translating Singlish text data into English, translating Singlish into Sinhala, sentiment analysis for Singlish text data and mapping emojis for Singlish *** are language translation tools to translate many languages like Russian, Italian, Japanese into the English Language but there are no proper tools to translate the mainly used sublanguage in Sri Lanka; that is Singlish. Therefore, the proposed solution for the above problem is developing a Singlish to English Language Translation Model and removing slang words to allow NLP technologies created for English to be utilized to handle Singlish textual *** NLP model shall have the capacity of translating Singlish words that are used on social media into English and if there are any slang words found during the translation, those words will be identified and removed. Translating the Singlish textual data into English would help the users to use other tools built to plagiarize, tools that are used to summarize and tools that are used to paraphrase English text.

关键词： Sentiment analysis Social networking (online) Buildings data models

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Energy Efficient Clustering to Improve Lifetime of Wireless Sensor Networks

Energy Efficient Clustering to Improve Lifetime of Wireless ...

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international conference on Engineering and Emerging technologies (ICEET)

作者： Syed Mohammad Abbas Zaidi Er. Prashant Ahluwalia Department of Computer Science Chandigarh University Chandigarh India

ISBN: (纸本)9781665491075

There are many nodes in a distributed environment that make up a wireless sensor network, and each node is able to perform reconfiguration tasks to fulfill user requests. Small-scale and remote locations are where sensor networks are implemented. So, the primary WSN problem becomes power consumption. The data sent by numerous sensor nodes is received by the sink node. The decision-making process is carried out after the similarity between the data from distinct sensor nodes is identified and eliminated. Additionally, the sink distributes acquired data locally and sends that data to distantly conducted networks. The currently conducted research makes use of VGDRA, an energy-efficient procedure that can lengthen WSN. The two-level hierarchies of the VGDRA protocol are designed to reduce energy usage in wireless sensor networks. The CHs are chosen using the VGDRA procedure based on their location and energy. The goal of this research is to improve the VGDRA routing algorithm in order to increase the network’s lifespan.

关键词： Wireless sensor networks Protocols Power demand Military computing Logic gates Routing Energy efficiency

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Security Technique To Prevent Port Knocking And Illegal Access in SDN

Security Technique To Prevent Port Knocking And Illegal Acce...

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Electrical Engineering and Sustainable technologies (ICEEST), international conference on

作者： Uzair Zia Muhammad Asfand Yar Tahir Naeem Monis Amin Muhammad Zeeshan Muhammad Wajeeh Us Sima Institute of Business & Management University of Engineering & Technology Lahore Pakistan Al-Khawarizmi Instituite of Computer Science University of Engineering and Technology Lahore Pakistan Faculty Of Natural Sciences Computing Science And Mathematics University Of Stirling Scotland Uk School Of Computer Science And Technology Chongqing University Chongqing China

The progress of networking technology has led to a new paradigm of network control based on data plane programmability. This allows for flexible packet processing and improved network management, which increases network security. Literature lacks network security methods for authentication and blocking illegal access. In this study, we aim to prevent assaults in a two-level defense. The first level is a dynamic firewall mechanism that stops packets from untrusted sources. The second level is dynamic port banging. Both security levels were evaluated in a simulated P4 environment. The controller receives packets from unknown hosts. The controller uses ACL rules to allow or reject packets. Each new host generates a random sequence for port knocking. Hosts may only connect in the order given. This solution works better than earlier P4-based firewall systems owing to two security layers. Moreover, it blocks unwanted network access to mitigate certain security threats.

关键词： Electrical engineering Firewalls (computing) Authentication Network security Random sequences

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Recent Advancements of Internet of Medical Things (IoMT): Challenges and Future Opportunities with Emerging technologies

Recent Advancements of Internet of Medical Things (IoMT): Ch...

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Automation, computing and Renewable Systems (ICACRS), international conference on

作者： Divy Tushar Shah Akash Patel Aishwariya Budhrani Khushi Patel Department of Information Technology Devang Patel Institute of Advance Technology and Research (DEPSTAR)Faculty of Technology and Engineering (FTE) Charotar University of Science and Technology (CHARUSAT) Changa Gujarat India Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research (DEPSTAR)Faculty of Technology and Engineering (FTE) Charotar University of Science and Technology (CHARUSAT) Changa Gujarat India Department of Computer Engineering Devang Patel Institute of Advance Technology and Research (DEPSTAR)Faculty of Technology and Engineering (FTE) Charotar University of Science and Technology (CHARUSAT) Changa Gujarat India

ISBN: (纸本)9781665460859

Internet of Medical Things (IoMT) is one of the thriving areas of current era which focuses on the digitization of healthcare services by connecting hospitals, medical resources, healthcare professionals and patients over Internet. Internet of Things (IoT) plays important role in IoMT to connect medical devices with each other for the communication of data between doctors, medical staffs, hospitals and patient for offering efficient and reliable medical services. Currently, IoMT offers various services like patient data management, diagnosis of diseases, remote healthcare monitoring, tele-surgery, etc. In this paper, the IoMT is covered with respect to its applications, use cases and case studies. The analysis shows that the IoMT is one of the most growing fields of Information Technology (IT) which uses the various sensors, equipment, and devices to sense the health-related data of humans and share the data with hospitals, doctors and healthcare professionals for remote diagnosis and treatments. However, IoMT is being adopted widely to offer tele-surgeries, real-time health monitoring and diagnosis of various diseases by integration of various technologies, still many challenges such as Interoperability, privacy and security, Energy Consumption, network latency, etc. need focus of researchers for better and accurate outcome. Here, the major challenges of IoMT are covered which will be helpful to the researcher and medical professional for improvement in healthcare domain.

关键词： Renewable energy sources Hospitals Internet of Medical Things Sensors Security Reliability Monitoring

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A Fuzzy-Based Multiobjective Cat Swarm Optimization Algorithm: A Case Study on Single-Cell data 9th

A Fuzzy-Based Multiobjective Cat Swarm Optimization Algorith...

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9th international conference on Frontiers in Intelligent computing: Theory and Applications, FICTA 2021

作者： Achom, Amika Das, Ranjita Gond, Pratibha Pakray, Partha National Institute of Technology Aizawl Mizoram India

ISBN: (纸本)9789811666230

The microRNAs and single-cell expression data embed relevant and unrelevant information about the genes. Finding the hidden gene expression pattern helps us to understand the functionality of a gene at molecular level. Particularly, gene expression pattern varies with the effect of external stimuli. Therefore, in this situation, soft clustering technique helps to detect multiple patterns of a gene at one time. In this work, a form of soft clustering approach is presented in assimilation with the fuzzy and cat swarm optimization (CSO) algorithm to find an optimal cluster center for clustering. Here, the task of clustering is formulated as the optimization of multiple cluster validity indexes. The proposed clustering algorithm (multiobjective fuzzy-based CSO) is then compared with some of state-of-the-art clustering technique on different gene expression dataset. A case study is conducted to identify the transcriptome or marker gene in each cell types of human urinary bladder single-cell RNA sequencing data. Experimental analysis of multiobjective fuzzy-based CSO clustering algorithm shows its effectiveness over other state-of-the-art clustering algorithm such as AL, K-means, GA-FCM, DE-FCM, PSO-FCM, and GWO-FCM. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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Open data Platform Architecture and Its Advantages for an Open E-Government 3rd

Open Data Platform Architecture and Its Advantages for an Op...

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3rd international conference on Computer science, Engineering and Education Applications, ICCSEEA 2020

作者： Alhawawsha, Mohammad Panchenko, Taras Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (纸本)9783030555054

E-Government is a set of pervasive technologies and automated processes now. The open data plays a crucial role in the successful implementation of this concept. The Open data Platform (ODP) architecture is described here as the framework for the open data access systems implementation, including specific requirements. The proposed architecture and its components were discussed in this paper in detail for its availability, productivity, and reliability. The open data subsystem based on the architecture presented here was developed for the Jordan Government and was successfully implemented and tested. Thus, this architecture showed its viability. The focus of the paper is the detailed analysis of the proposed ODP architecture and its characteristics. The ODP is a significant system for the mature e-Government. We propose here the architecture for it with usage-proven characteristics. This fact adds the value to the e-Government framework stability, and significant characteristics and improves the overall quality of the system. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Open data

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