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Computer-aided system for extending the performance of diabetes analysis and prediction 7

Computer-aided system for extending the performance of diabe...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Murad, Saydul Akbar Azmi, Zafril Rizal M Hakami, Zaid Hafiz Prottasha, Nusrat Jahan Kowsher, Md Universiti Malaysia Pahang Faculty of Computing Malaysia Jazan University Faculty of Computer Science and Information Technology Jazan Saudi Arabia Daffodils International University Department of Computer Science and Engineering Bangladesh Noakhali Science and Technology University Department of Applied Mathematics Bangladesh

ISBN: (纸本)9781665414074

Every year, diabetes causes health difficulties for hundreds of millions of individuals throughout the world. Patients' medical records may be utilized to quantify symptoms, physical characteristics, and clinical laboratory test data, which may then be utilized to undertake biostatistics analysis to uncover patterns or characteristics that are now undetected. In this work, we have used six machine learning algorithms to give the prediction of diabetes patients and the reason for diabetes are illustrated in percentage using pie charts. The machine learning algorithms used to predict the risks of Type 2 diabetes. User can self-assess their diabetes risk once the model has been trained. Based on the experimental results in AdaBoost Classifier's, the accuracy achieved is almost 98 percent. © 2021 IEEE.

关键词： Forecasting

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Word Embedding based News Classification by using CNN 7

Word Embedding based News Classification by using CNN

引用

7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Ahmed, Faisal Akther, Nazma Hasan, Mohammad Chowdhury, Kibtia Mukta, Md. Saddam Hossain Premier University Department of Cse Chattogram Bangladesh Department of Cse Dhaka Bangladesh

ISBN: (纸本)9781665414074

In this era of information technology, the number of online news portal is increasing day by day. These online news portals make a good profit by advertising different consumer products to their reader. However, due to the lack of intelligence, traditional news portals cannot identify what types of news are preferred by the users. As a consequence, these news portals most of the time show irrelevant advertisements to the readers and incur a great economic loss to the advertisers. If these news portals can identify what type of news a user is reading, then they can provide contextual advertisements (showing advertisements of news-related products) and gain more profit. Therefore, in this paper, we proposed a method integrating word embedding with Convolutional Neural Network (CNN) for the classification of English news into four different categories: Sports, Business, National and International. The performance of the proposed method is evaluated on our prepared dataset in terms of macro- f1 and micro-f1 scores. The experimental result shows that our proposed method achieved macro-f1 and micro-f1 scores of 0.90 and 0.89, respectively which are significantly higher than that of all the baseline methods. © 2021 IEEE.

关键词： Embeddings

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Smell Sensory System in m-Commerce- A Case Study 7

Smell Sensory System in m-Commerce- A Case Study

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Begum, K. Jarina Ibrahim, Juwairiya Anwar Refaee, Eshrag Jazan University Department of Information Technology and Security Jazan Saudi Arabia Faculty of Medicine Asia Metropolitan University Johor Bahru Malaysia

ISBN: (纸本)9781665414074

This paper analyzing two purposes. Purpose-i, the way by which the customers could buy perfume online;the basic level of smell could be identified then made combination and bringing to the multiple levels of smells. It discusses how to record the smell from the basic level to the multiple levels in smells, it could be done by deploying the autocorrelation method and recorded as binary bits then it could be stored as a data set in the memory, so that users could select/customize their desired perfume through online from any corner of the world;it could be given as a decimal number/ flavor names/ pointing some pictures of a flower at the user interface mode or sense the scent and choose the desired perfume by using our atomizer, o/p device directly. Then purpose-ii, a patient could be identified about his molecule range by his body odor/excrete matter's odor online, which could help the physician to diagnose the patient's health issue. This terminology could be referred as i/p device OIA, Olfaction Identifier Analyzer. So that anybody could test their molecule range online. This paper is analyzing the smell sensory nervous system and also leads to a new output device (atomizer), and an input (OIA) device (to be implemented) to spray and absorb the smell through the mobile device respectively. Thus the commercial work could be successfully done either it's buying a perfume or finding the molecule range of health condition. © 2021 IEEE.

关键词： User interfaces

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Light Deep Learning Model Architecture for Chest X-ray based Covid-19 Detection 7

Light Deep Learning Model Architecture for Chest X-ray based...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Sumari, Putra Syed, Saqib Jamal Sheng, Liang Han Universiti Sains Malaysia School of Computer Science Penang Malaysia

ISBN: (纸本)9781665414074

Covid-19 is a serious public health problem worldwide. To date, it has spanned worldwide with 12.8 million infected and 566, 909 confirm death. Covid-19 screening is indeed an important task and has to be done quickly as possible to many people so that early treatment can be done. The current world RT-PCR standard screening for Covid-19 detection no longer can cope to the demand of large world population. There is a need of other quick diagnosis procedure such as to use chest x-ray images and light computing algorithm to accelerate the covid-19 screening. This paper proposes Covid-19 detection based on chest X-ray image with a light computer processing. The proposed work introduces the combination of Gray Level Co-occurrence Matrix (GLCM) and Convolutional Neural Network (CNN) methods to detect Covid-19 symptom. The light version is coming from GLCM simplicity and convolutional neural network (CNN) single layer. It is fast and suitable method for places where computing resources are minimal. With light processing component, the proposed work gives the highest classification performance with 97.06% accuracy compared to other similar works. © 2021 IEEE.

关键词： Convolutional neural networks

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Inventory Visibility Scenario to Reduce Safety Stock in Supply Chain Network Using Blockchain Hyperledger Composer 7

Inventory Visibility Scenario to Reduce Safety Stock in Supp...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Mukhtar, Arwa Romli, Awanis Noor, Noorhuzaimi Mohd Abdullateef, Mansoor Al-Bashiri, Hael Universiti Malaysia Pahang Faculty of Computing Pekan Malaysia Freelance Resesarcher Madinah Saudi Arabia

ISBN: (纸本)9781665414074

Limited inventory visibility in supply chain networks increases stock levels and leads to negative consequences of stock repetition, bullwhip-effect, inventory backlog, and out of stock situations. It is essential to increase the inventory visibility and reduce the safety stock level to exchanged inventory data between supply chain stakeholders to achieve inventory accuracy and efficiency accordingly. This paper presents a technical implementation of inventory visibility scenario to reduce safety stock level in Blockchain-based supply chain network. The proposed inventory visibility scenario provides access to accurate and up to date inventory levels and reduces the safety stock level accordingly. The scenario implemented using Blockchain Hyperledger Composer and tested in Blockchain Hyperledger Composer Bluemix. In the performance evaluation, the inventory visibility scenario compared to Cloud-based and traditional supply chain models in terms of reducing the safety stock level. Scenario results show improved inventory visibility and significant reduction in inventory safety stock compared to other models. This reduction prevents negative consequences of stock repetition, bullwhip-effect, inventory backlog, and out of stock situations which helps in efficient inventory management and hence improves supply chain performance. © 2021 IEEE.

关键词： Blockchain

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Optical Character Recognition using Backpropagation Neural Network for Handwritten Digit Characters 7

Optical Character Recognition using Backpropagation Neural N...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Yap, Mei Ing Moorthy, Kohbalan Daud, Kauthar Mohd Ernawan, Ferda Universiti Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Universiti Kebangsaan Malaysia Center for Artificial Intelligence Technology Fakulti Teknologi Dan Sains Maklumat Bangi43600 Malaysia

ISBN: (纸本)9781665414074

Recognizing handwritten characters, the accuracy of the optical character recognition is usually not relatively high due to every person having their unique way of writing characters. Therefore, we focus on finding a high recognition accuracy of optical character recognition by using a backpropagation neural network. The input layer of the backpropagation neural network is the pixel number of the one-character image, which is 784 input nodes that will be the input layer of the neural network. Then the output layer of the neural network will be the 10-digit characters which are 0 to 9. The dataset that used for this research has a total of 280, 000 data. The output of the neural network will a computerized digit representing the recognized digit characters. The performance measurement is the recognition accuracy where the recognized data and the expected output data are compared and calculated. Additionally, the dataset was applied with salt and pepper noise to represent the corrupted data and use a median filter to repair the image. The recognition accuracy for the corrupted image and the corrected image are obtained and discussed. © 2021 IEEE.

关键词： Salt and pepper noise

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Factors influencing SaaS adoption by MSMEs 7

Factors influencing SaaS adoption by MSMEs

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Ayuningtyas, Ayuningtyas Azmi, Zafril Rizal M Abdullah Arshah, Ruzaini Ahmad, Muhammad Aliif Khubrani, Mousa Faculty of Technology and Informatics Universitas Dinamika Surabaya East Java Indonesia Faculty of Computing Universiti Malaysia Pahang Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Malaysia Faculty of Computer Science Information Technology Jazan University Saudi Arabia

ISBN: (纸本)9781665414074

Cloud computing has been around for quite long, yet some organizations have never used it, especially MSMEs in Indonesia. Several factors hinder its implementation by MSMEs, including unequal infrastructure for using the internet in Indonesia. This paper assesses the literature on cloud computing, SaaS, e-Commerce, and e-wallet to determine the initial factors that affect technology implementation. The results showed that the frameworks widely used to obtain and assess variables include the DOI, TAM, and TOE. Furthermore, compatibility and the pressure from competitors to adopt technology are the highly considered variables. Among the factors, perceived usefulness and ease of use is also deemed by MSMEs in implementing e-commerce and e-wallet. © 2021 IEEE.

关键词： Software as a service (SaaS)

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Reducing Docker Daemon Attack Surface Using Rootless Mode 7

Reducing Docker Daemon Attack Surface Using Rootless Mode

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Rahmansyah, Reyhan Suryani, Vera Arif Yulianto, Fazmah Hidayah Ab Rahman, Nurul Telkom University Faculty of Informatics Bandung Indonesia Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer Dan Teknologi Maklumat Johor Malaysia

ISBN: (纸本)9781665414074

Containerization technology becomes one of alternatives in virtualization. Docker requires docker daemon to build, distribute and run the container and this makes the docker vulnerable to an attack surface called Docker daemon Attack Surface - an attack against docker daemon taking over the access (root). Using rootless mode is one way to prevent the attack. Therefore, this research demonstrates the attack prevention by making and running the docker container in the rootless mode. The success of the attack can be proven when the user is able to access the file /etc/shadow that is supposed to be only accessible for the rooted users. Findings of this research demonstrated that the file is inaccessible when the docker is run using the rootless mode. CPU usage is measured when the attack is being simulated using the docker run through root privileges and rootless mode, to identify whether the use of rootless mode in the docker adds the load of CPU usage and to what extent its increased. Results showed that the CPU use was 39% when using the docker with the rootless mode. Meanwhile, using the docker with the right of the root access was only 0%. The increase of 39% is commensurate with the benefit that can prevent the docker daemon attack surface. © 2021 IEEE.

关键词： Containers

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Deep Learning Models for Air Pollution Forecasting in Seoul South Korea 7

Deep Learning Models for Air Pollution Forecasting in Seoul ...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Kiftiyani, Usfita Nazhifah, Sri Azizah Uin Sunan Kalijaga Yogyakarta Department of Informatics Engineering Yogyakarta Indonesia Syiah Kuala University Department of Infomatics Banda Aceh Indonesia

ISBN: (纸本)9781665414074

Air pollution has been a major cause of health problems in several countries such as South Korea which is a country with rapid industrial and population growth, it urges the government to pay more attention to this issue. Due to the harmful effects of air pollution, many researchers conduct studies to predict the air quality index as an effort to prevent more severe health issues. In this paper, we propose three deep learning models, namely: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and combined CNN- LSTM to do air pollution forecasting. We mainly focus on the performance of the models applied in the time-series forecasting task as a supervised learning problem. We use the data from Seoul Metropolitan Government collected hourly from 2017 to 2019 at some stations. The experiment was carried out on sulfur dioxide (SO2), carbon monoxide (CO), nitrogen oxide (NO2), ozone (O3), and two particulate matter (PM) concentrations. We evaluate our model using root mean squared error (RMSE) to compare the models' performance. The result shows that with normalization CNN model gives the lowest RMSE value, however without normalization the combined CNN-LSTM gives the lowest RMSE value. It proves that the model can predict the air quality index in Seoul South Korea. © 2021 IEEE.

关键词： Sulfur dioxide

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Gender Differences in Computational Thinking Skills among Malaysian's Primary School Students using Visual Programming 7

Gender Differences in Computational Thinking Skills among Ma...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Abdullah, Osmanullrazi Kamaludin, Adzhar Rahman, Nur Shamsiah Abdul University Malaysia Pahang Faculty of Computing Malaysia

ISBN: (纸本)9781665414074

Computational Thinking (CT) is a thought process which utilize computer science concepts to solve problem in the real life. One of the methods to develop CT among the Children is by using visual programming to create computational artifacts such as animation and games. This study collected 50 animation and 47 games projects created by the primary school students in standard 6 (12-year-old) from the eight-week lesson using visual programming Scratch. The purpose of the study is to investigate if there is a significant difference between male and female students on CT skills of flow control, logic, data representation, parallelism, synchronization, user interactivity and abstraction. Source code projects were analyzed for CT skills score. Result from Mann-Whitney U test shows the different was not statistically significant between male and female students on CT skills mentioned previously. © 2021 IEEE.

关键词： Animation

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