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International Conference on ICT For Smart Society (ICISS)

作者： Yakob Utama Chandra Harjanto Prabowo Ford Lumban Gaol Betty Purwandari Information Systems Department BINUS Graduate Program Doctor of Computer Science School of Information Systems Bina Nusantara University Jakarta Indonesia Management Department Doctor of Research Management BINUS Business School Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

The proliferation of data has become an inevitable consequence of the digital age as individuals across all domains increasingly rely on data for various purposes. Both companies and governments have a significant need for data. However, the sheer volume and complexity of this data, along with the potential risks associated with mishandling it, necessitate the establishment of a framework known as data governance. The primary objective of this research paper is to examine the factors that contribute to the effective implementation and utilization of Data governance. This study employs qualitative methods and adopts a systematic literature review approach to address the research inquiry: “What are the important factors that determine the utilization of data governance technology?” Through the systematic literature review, a total of 41 key factors were identified from 20 research literatures that have been found to significantly impact the effective implementation of data governance. There are thirteen primary technological factors that hold significance in the realm of data governance. These factors encompass technology, application and use, storage of data, sharing, archiving, and preservation, big data algorithmic systems, data flow, deletion, diversity of data, mechanisms, size of data, control, and data integrity.

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The Effectiveness of CNN-Based Models in Macroscopic Motion Direction Detection

The Effectiveness of CNN-Based Models in Macroscopic Motion ...

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Robotics and computer Vision (ICRCV), International Conference on

作者： Zhiyu Qiu Tianqi Chen Yuxiao Hua Yuki Todo Division of Electrical Engineering and Computer Science Graduate School of Natural Science & Technology Kanazawa University Kanazawa Japan Faculty of Electrical Information and Communication Engineering Kanazawa University Kanazawa Japan

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical pattern recognition capabilities, CNNs have demonstrated superior performance over traditional machine learning methods. One particularly critical application of CNNs is in motion direction detection, which is essential for enhancing security in surveillance systems, ensuring safe navigation in autonomous vehicles, and providing valuable insights in sports analytics. In this study, our primary goal was to investigate how different types of input data impact the accuracy of motion direction detection. We applied five distinct preprocessing methods to test the effect of input data on accuracy. Furthermore, we conducted experiments in various object and background scenarios to evaluate adaptability of simple 4-layer CNN, EfficientNetBO and ResNet50 in detecting motion direction. Finally, we tested the noise resistance of these three models. The results indicate that CNNs are capable of extracting the macroscopic motion direction of objects in images. Our study highlights the potential of CNN-based models for efficient and effective motion direction detection.

关键词： Resistance Training computer vision Adaptation models Accuracy Noise Dynamics Data models Convolutional neural networks Residual neural networks

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A Large Scale Dataset for Fire Detection and Segmentation in Indoor Spaces

A Large Scale Dataset for Fire Detection and Segmentation in...

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2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Maric, Petar Arlovic, Matej Balen, Josip Vdovjak, Kresimir Damjanovic, Davor Faculty of Electrical Engineering Computer Science and Information Technology Osijek J. J. Strossmayer University of Osijek Osijek Croatia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Fire represents a dangerous event, especially in inhabited areas where it can cause extensive economical damage, as well as take human lives, and therefore early fire detection is of utmost importance and requires careful attention. Utilizing images from security cameras and computer vision algorithms, it is possible to detect and raise the alarm in the event of a fire. The existence of similar-colored lights to the flame's color is the greatest obstacle to indoor fire detection when it comes to computer vision. The lights may trigger false positive detections, resulting in false alarms and potential fire suppression by automated systems. By developing a new fire dataset for the training of deep neural networks, we attempted to circumvent the stated issue. Our dataset includes images of different colored lights, images with reflections of light that resemble the color of fire, and images of fire in a variety of environments, including warehouses, factories, shopping malls, residential buildings, offices, etc. Although there are numerous scientific papers and datasets for fire detection, there are not many datasets containing images of indoor fires. In this paper, we show a process of collecting and annotating images representing indoor fire in a manner suitable for deep neural network training. Furthermore, we present the developed Fire Sense image annotation tool and the process of image annotation. The dataset currently consists of more than 11000 annotated images of various types of fires in different environments. © 2022 IEEE.

关键词： Fires

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A Machine Learning based Facial Expression and Emotion Recognition for Human computer Interaction through Fuzzy Logic System 6

A Machine Learning based Facial Expression and Emotion Recog...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Vinutha, K. Niranjan, Manoj Kumar Makhijani, Jagdish Natarajan, B. Nirmala, V. Vijaya Lakshmi, T.R. Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Applications Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350398496

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. This recognition of facial expression is highly possible with the help of machine learning technology. This research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. This algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. The fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the Vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content. © 2023 IEEE.

关键词： Emotion Recognition

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Automatic Identification and Reconstruction 3D Image of the Rhinosinusitis Mucus

Automatic Identification and Reconstruction 3D Image of the ...

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International Conference of information and Communication technology (ICoICT)

作者： Ondra Eka Putra Sumijan Muhammad Tajuddin Department of Informatics Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia Doctoral Program in Information Technology Universitas Putra Indonesia YPTK Padang Padang Indonesia Department of Computer Science Bumigora University Mataram Indonesia

ISBN: (数字)9798350363432

ISBN: (纸本)9798350363449

Rhinosinusitis Mucus is mucus produced by mucus glands found in the nose in the paranasal sinus area which indicates inflammation. Segmentation of rhinosinusitis mucus is very difficult to do because of the many characteristics of different objects in the image of the paranasal Sinus CT-Scan in separating the mucus object with its background before the extraction process. The purpose of this study was to accurately identify and measure the mucus area of rhinosinusitis from CT-scan images of rhinosinusitis patients. The test data used in this study is secondary data from CT-Scan of paranasal sinus sagittal view in radiology RSUP M. Djamil Padang, Padang City, West Sumatra, Indonesia, using the Philips Ingenuity CT CT-Scan model type MRC880 numbered Tube series 163889. The segmentation process is carried out to obtain the optimal multi threshold value from each slice of the test image using the Improved Adaptive Multi Threshold (IAMT) segmentation method which produces a binary image to separate the mucus object with its background before the extraction process. Furthermore, the results of IAMT are applied to the identification and extraction model automatically to obtain the area of the rhinosinusitis mucus object. The extraction results were used for 2D to 3D image reconstruction in calculating the volume of rhinosinusitis mucus objects. The test results showed that the smallest area of rhinosinusitis mucus was 0.516 cm 2 and the largest was 1.807 cm 2 , and the average volume was 0.95 cm 3 with Accuracy is 96,5%. The identified areas and volumes indicate inflammation in the area of the paranasal sinuses and require treatment. Thus, this study can help doctors in making the right decision to take further medical action.

关键词： Image segmentation Visualization Adaptation models Three-dimensional displays Computed tomography Urban areas Nose Radiology Object recognition Image reconstruction

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Improved YOLOv4 Infrared Image Pedestrian Detection Algorithm 6th

Improved YOLOv4 Infrared Image Pedestrian Detection Algorith...

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6th EAI International Conference on Machine Learning and Intelligent Communications, MLICOM 2021

作者： Tao, Jin Shi, Jianting Chen, Yinan Wang, Jiancai Graduate College Heilongjiang University of Science and Technology Harbin150022 China School of Computer and Information Engineering Heilongjiang University of Science and Technology Harbin150022 China Academic Affairs Office Heilongjiang University of Science and Technology Harbin150022 China

ISBN: (纸本)9783031044083

Because pedestrians are always in the active state, each target is at a different distance from the camera, resulting in a certain difference in the size of similar targets in the figure. Therefore, an infrared pedestrian detection algorithm is proposed in the paper based on Yolov4 algorithm. Aiming at the problems of low recognition rate and high background influence in infrared image downlink human small target detection, the network structure of YOLOv4 is optimized. Compared with YOLOv4 and YOLOv3, the mean Average Precision is improved by 0.53% and 1.05%, which improves the detection accuracy in a certain extent. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Infrared imaging

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A practical conflicting role-based cloud security risk evaluation method

Recent Advances in Computer Science and Communications

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Recent Advances in computer science and Communications 2021年第3期14卷 874-886页

作者： Han, Jin Zhan, Jing Xia, Xiaoqing Fan, Xue School of Computer Science Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Faculty of Information Technology Beijing University of Technology Beijing100124 China

Background: Currently, Cloud Service Provider (CSP) or third party usually proposes principles and methods for cloud security risk evaluation, while cloud users have no choice but to accept them. However, since cloud users and cloud service providers have conflicts of interests, cloud users may not trust the results of security evaluation performed by the CSP. Different cloud users may have different security risk preferences, which makes it difficult for the third party to con-sider all users' needs during evaluation. In addition, current security evaluation indexes for the cloud are too impractical to test (e.g., indexes like interoperability, transparency, portability are not easy to be evaluated). Methods: To solve the above problems, this paper proposes a practical cloud security risk evaluation method of decision-making based on conflicting roles by using the Analytic Hierarchy Process (AHP) with Aggregation of Individual Priorities (AIP). Results: Not only can our method bring forward a new index system based on risk source for cloud security and corresponding practical testing methods, but also can obtain the evaluation result with the risk preferences of conflicting roles, namely CSP and cloud users, which can lay a foundation for improving mutual trusts between the CSP and cloud users. The experiments show that the method can effectively assess the security risk of cloud platforms and in the case where the number of clouds increased by 100% and 200%, the evaluation time using our methodology increased by only 12% and 30%. Conclusion: Our method can achieve consistent decisions based on conflicting roles, high scalability and practicability for cloud security risk evaluation. © 2021 Bentham science Publishers.

关键词： Analytic hierarchy process

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Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

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computer Systems science & Engineering 2023年第9期46卷 2947-2961页

作者： IbrahimM.Alwayle Badriyya B.Al-onazi Mohamed K.Nour Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Amal S.Mehanna Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Online reviews regarding purchasing services or products offered are the main source of users’*** gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or *** practice is called review *** the last few years,various techniques have been recommended to solve the problem of spam *** spam detection study focuses on English reviews,with a lesser interest in other *** review detection in Arabic online sources is an innovative topic despite the vast amount of data ***,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion *** presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and ***,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible ***,unigram and bigram feature extractors are *** SGRU model is employed in this study to identify and classify Arabic spam *** the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU *** experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC *** extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

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Mobile English language learning: a systematic review of group size, duration, and assessment methods

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computer Assisted Language Learning 2023年第3期36卷 430-456页

作者： Monther M. Elaish Liyana Shuib Gwo-Jen Hwang Norjihan Abdul Ghani Elaheh Yadegaridehkordi Siti Zaidah Zainuddin a Faculty of Information Technology Department of Computer Science University of Benghazi Benghazi Libya b Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia c Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan d Information and Communications Technology Research Center Amirkabir University of Technology Tehran Iran e Faculty of Languages and Linguistics Department of English Language University of Malaya Kuala Lumpur Malaysia

technology-mediated learning has become a valuable tool, especially in the English language classroom. Nowadays, mobile learning is considered a significant educational tool for promoting education among learners. Several recent studies have attempted to examine different aspects of mobile learning. The size of the experimental group, and the duration and suitability of the assessment methods are important aspects in designing experimental studies in the context of mobile English language learning. Yet, very few studies have paid attention to these aspects. Therefore, this study aimed to investigate the utilisation of state-of-the-art mobile technology in English language learning, especially in terms of the size of the experimental groups, and the duration and suitability of the assessment methods. Following a systematic review protocol, 151 articles published between 2010 and 2017 were selected for analysis. Furthermore, this study considered, separately, three factors addressed in the reviewed studies: language acquisition problems, types of participants, and targeted English skills. The findings showed that thegroup sizeandstudy durationsignificantly differed among the studies based on the above factors. Questionnaires and tests were found to be the most common methods ofassessmentfor each factor, whether used separately or in combination. The results of this review can assist in the formulation of experimental designs to mitigate the challenges and facilitate the effective use of mobile learning in English language learning.

关键词： Assessment and evaluation experimental design mobile English language learning quantitative and qualitative methods

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Convolutional Neural Network Analysis on Handwriting Patterns and Its Relationship to Personality: A Systematical Review

Convolutional Neural Network Analysis on Handwriting Pattern...

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International Conference on Informatics, Multimedia, Cyber and information System (ICIMCIS)

作者： Zaid Romegar Mair Widya Cholil Evi Yulianti Dona Marcelina Theresiawati Ika Nurlaili Isnainiyah Informatics Study Program Computer and Natural Science Faculty Indo Global Mandiri University Indonesia Informatics Study Program Computer Science Faculty Universitas Pembangunan Nasional Jakarta Indonesia Information System Study Program Computer and Natural Science Faculty Indo Global Mandiri University Indonesia

It can be argued that the Convolutional Neural Network (CNN) used in this research is an efficient algorithm for classifying images based on the end prediction of the path taken. In every plot that is made, there is a similar process for achieving a prediction of the final result. The implementation for each of these procedures follows the steps that are summarized into a flow of analysis stages that can help to develop the application of an algorithm. The initial stage is to take the handwriting from the user which is then pre-processed the image, to eliminate existing noise, and sharpen the contrast, so that the image can be seen clearly. Images will be processed and analyzed using the Convolutional Neural Network model, training will be carried out, with an average training of a dataset of 100 epochs or about 7 to 10 minutes, and labeling on the trained dataset. The accuracy of the training reached 98.89%, as a proportion of the different characteristics of the handwriting sample.

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