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Effect of Camera Simulator Application for Basic Photography Subjects at Vocational School: One-Group Pre-test and Post-test Design 9

Effect of Camera Simulator Application for Basic Photography...

引用

9th International Conference on Education and Technology, ICET 2023

作者： Qonita, Qori Suwastika, Novian Anggis Masrom, Maslin 10 Bandung Bandung Indonesia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350358292

This study examines the impact of using the camera simulator application in basic photography subjects for students cognitive skills majoring in broadcasting and film in vocational high schools in Indonesia. Camera simulators have the advantage of providing opportunities for students to practice more freely and independently. In this study, the CamSim Pro application was employed as the camera simulator and was installed on desktop systems. Eighty eligible students from Public Vocational High School (PVHS) 10 Bandung participated in using the camera simulator as an intervention technology in two topics in basic photography subjects: aperture priority mode and shutter priority. This study adopts a quasi-experimental research method with a one-group pre-test and post-test design, examining the impact of camera simulator intervention. This research uses five stages, (1) designing the pre-test and its evaluation, (2) selecting the group/population to be tested, (3) preparing and providing a camera simulator intervention, (4) designing the post-test and evaluating it, and (5) conducting data analysis referring to the results of the pre-test and the post-test. The data normality test uses the Kolgomorov-Smirnov method and the Shapiro-Wilk method. Data analysis to measure the impact of technological interventions uses the Wilcoxon Signed Ranks method and the significance of the technological interventions using the Z statistic and asymptotic significance. Analysis of the collected data unveiled a compelling narrative. The Z statistic exhibited a notable value of -4.897, paired with an asymptotic significance value of © 2023 IEEE.

关键词： Students

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Artificial Intelligence in Human-Computer Interaction: To Serve and Protect

Artificial Intelligence in Human-Computer Interaction: To Se...

引用

International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS)

作者： Aleksandar D. Jevremovic Faculty of Informatics and Computing Singidunum University Belgrade Serbia

The rapid development and progress of artificial intelligence algorithms in the last decade has opened up many new possibilities and fields for its application. The field of human-computer interaction is not only not an exception, but it can also be considered a veteran. Among the general population, there is a certain level of apprehension when it comes to introducing artificial intelligence into new areas and aspects of human life. In this paper, we will attempt to shed new light on this issue and the current trend of villainizing AI, as well as present current trends in enhancing user security based on it. We will summarize the current conditions, trends and experiences in this area.

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OPTIMIZING VIRTUAL SMART IRRIGATION SYSTEMS THROUGH DIGITAL TWIN APPLICATIONS: A NEUTROSOPHIC MCDM APPROACH 51

OPTIMIZING VIRTUAL SMART IRRIGATION SYSTEMS THROUGH DIGITAL ...

引用

51st International Conference on Computers and Industrial Engineering, CIE 2024

作者： Mohamed, Mona Salem, Alaa Sallam, Karam M. Radwan, Ibrahim Abdel-Basset, Mohamed Higher Technological Institute 10th of Ramadan City44629 Egypt Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Collage of Computing and Informatics University of Sharjah Sharjah United Arab Emirates University of Canberra Canberra Australia

Recently, the intervention of cutting-edge contemporary technologies in agriculture and their applications has become imperative. To address several brought on by traditional agricultural practices that threaten agriculture and verdant landscapes which therefore imperil sustainability. One of these practices is wasting water for irrigation which is in contradiction to the general welfare. The state is under pressure to cultivate more crops, which necessitates more irrigation, because of the population's constant growth and the resulting need for food and later economic survival. Hence, this study embraces the methods that prevent impediments to conventional irrigation practices to rationalization of irrigation. As well, as embracing technologies for Converting conventional irrigation into intelligent irrigation. The simulation for the behavior of the irrigation system isn't less important than using the Internet of Things (IoT) for aggregating and processing data to determine daily irrigation. Thus, deploying a digital twin (DT) as a virtual mirror for constructing an irrigation system is crucial. Also, utilizing the appropriate application of DT is an obstacle. In turn, this study contributes to constructing innovative decision-making methodology. To evaluate the DT applications and recommend the optimal application of DT to construct virtual smart irrigation (VSI). CRiteria Importance Through Intercriteria Correlation (CRITIC) and Additive Ratio ASsessment (ARAS) of Multi-Criteria Decision Making (MCDM) techniques collaborate with Type 2 Neutrosophic Numbers (T2NNs) for supporting decision makers (DMs) in perplexing situations as well as eliminating prejudice. © 2024 Computers and Industrial Engineering. All rights reserved.

关键词： Irrigation

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User Stories in Requirements Elicitation: A Systematic Literature Review

User Stories in Requirements Elicitation: A Systematic Liter...

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International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Yanche Ari Kustiawan Tek Yong Lim Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

A user story is commonly applied in requirement elicitation, particularly in agile software development. User story is typically composed in semi-formal natural language, and often follow a predefined template. The user story is used to elicit requirements from the users' perspective, emphasizing who requires the system, what they expect from it, and why it is important. This study aims to acquire a comprehensive understanding of user stories in requirement elicitation. To achieve this aim, this systematic review merged an electronic search of four databases related to computer science. 40 papers were chosen and examined. The majority of selected papers were published through conference channels which comprising 75% of total publications. This study identified 24 problems in user stories related to requirements elicitation, with ambiguity or vagueness being the most frequently occurring problem reported 18 times, followed by incompleteness reported 11 times. Finally, the model approach was the most popular approach reported in the research paper, accounting for 30% of the total approaches reported.

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An Ensemble-Based Framework to Estimate Software Project Effort

An Ensemble-Based Framework to Estimate Software Project Eff...

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International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Mohammad Haris Fang-Fang Chua Amy Hui-Lan Lim Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

Effort estimation is essential for successful software project planning, budgeting, and risk identification. However, the techniques used to estimate effort are often inaccurate, outdated, and only consider technical factors while neglecting project management or stakeholder engagement. Expert estimation remains an important technique for leveraging human expertise in software estimation, but solely relying on this technique causes biased and subjective predictions. Machine learning (ML) techniques have shifted the direction of software project effort estimation towards computational intelligence. Nonetheless, there is a lack of deployment due to ambiguous outcomes and ineffective model-building approaches. This study presents an ensemble-based framework that can estimate software project effort more accurately with the incorporation of domain knowledge and experiences. To achieve this, six homogeneous classifier ensembles will be constructed using six distinct classifiers on the proposed USP05-FT dataset. The collected expert estimations will be integrated into the proposed dataset as an additional feature in the form of numerical values such as expert-provided software project effort estimations (in person hours) that provide additional insight and knowledge. Subsequently, the predictions of all six homogeneous classifier ensembles will be combined through majority voting to obtain a more accurate and reliable prediction with increased robustness against errors and uncertainties. The performance of the proposed framework will be evaluated using Recall, F-measure, Precision, and Accuracy. It is expected that the proposed ensemble-based framework for software project effort estimation will lead to more efficient and effective software project management, an improvement in resource allocation, empowering informed decision-making, and timely project delivery.

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Detection of Online Hate Comments Based on Feature Embedding and Deep Learning 4

Detection of Online Hate Comments Based on Feature Embedding...

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4th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Hamzah, Salehah Mohd, Masnizah Zakaria, Lailatul Qadri College of Computing Informatics and Mathematics Universiti Teknologi Mara Melaka77300 Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi43600 Malaysia

ISBN: (纸本)9798350318432

Nowadays, anti-social behavior is common on social media. One of these behaviors is d disseminating hate-based postings. Authors of hate speech have targeted specific groups of people based on their identities, such as race, religion, country, ethnicity, and gender. They are more commonly referred to as Anti-black, Anti-Muslim, Anti-Asian, xenophobic, misogynistic, and homophobic. The hate-biased content in social media is noisy, uses emojis and contains grammatical errors, making the word meaning using statistical feature extraction and machine learning difficult. Due to the success of pre-trained models in computer vision, this paper presented the utilization of Global Vector (GLoVe), feature embedding model to capture the global context, specifically for race and religion class from social media data and modeling using two deep learning approaches named Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM). The results demonstrate that CNN classifier outperforms BiLSTM in terms of F1 score using employing GloVe 100 and Glove 300 dimensions. © 2023 IEEE.

关键词： Embeddings

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Traffic Density Identification From UAV Images Using Deep Learning: A Preliminary Study

Traffic Density Identification From UAV Images Using Deep Le...

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International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA)

作者： Muhammad-Haziq Mohd-Tajudin Shahbe Mat-Desa Junaidi Abdullah Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

This study presents a preliminary investigation into the application of deep learning techniques for the identification of traffic density from unmanned aerial vehicle (UAV) images. The primary objective is to categorize the traffic flows into three classes: low, moderate, and high. The study proposes a VGG16-based framework with the aim of achieving high classification rates. The experimental results, obtained from a curated dataset with a 70:30 data-splitting ratio, demonstrate a good accuracy at 95.67%. The initial findings regarding traffic density identification are notably satisfactory, especially considering the challenges posed by the UA V -based images utilized in the experiments.

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Extreme Gradient Boosting (XGBoost) Regressor and Shapley Additive Explanation for Crop Yield Prediction in Agriculture 12

Extreme Gradient Boosting (XGBoost) Regressor and Shapley Ad...

引用

12th International Conference on Computer and Knowledge Engineering, ICCKE 2022

作者： Mariadass, Dennis A-L Moung, Ervin Gubin Sufian, Maisarah Mohd Farzamnia, Ali Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

The primary purpose of precision agriculture is to maximize crop yields while utilizing a limited amount of land resources. Apart from industrialization, which fuelled Malaysia's significant economy and development, the country's agriculture industry performs a major role in guaranteeing food security and safety, as well as long-term development and wealth creation. To increase the nation's food security, policymakers must rely on accurate crop yield predictions in order to easily obtain trade - related evaluations. Machine Learning can help anticipate yields more accurately. This paper proposes to use the XGBoost model for annual crop yield prediction in Malaysia. Experiments on the generated yield dataset show promising results with 0.98 R-Squared value and outperformed the current models. The implementation of the suggested model is extensively evaluated using the Shapley Additive Explanation (SHAP) to discover the essential features such as average temperature, average rainfall, and pesticide in the crop yield prediction. The estimates provided by machine learning algorithms will aid farmers in deciding what to grow because of this research. © 2022 IEEE.

关键词： Additives

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Social Media User Engagement for Promoting Public Health: Pattern Analysis Using Apriori Approach 5

Social Media User Engagement for Promoting Public Health: Pa...

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5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

作者： Alebaba, Daimler Benz Sura, Suaini Tuah, Nooralisa Mohd Lee, Seungwon Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Bundang Convergence Technology Campus of Korea Polytechnic University Gyeonggi-do Korea Republic of

ISBN: (纸本)9798350304152

User engagement has become an essential aspect of research in technology, marketing, and social media. Measuring and analyzing user engagement has gained significant importance due to its ability to provide valuable insights to assess engagement responses from online users engaged in published content. Previous studies have devised various methods to effectively analyze data, with statistical approaches commonly used as techniques. However, statistical approaches often struggle to effectively handle categorical variables, for which interpretation becomes limited and less informative, also failing to capture the structure and dependencies inherent in categorical data. Therefore, Apriori approach that defines association rules to generate patterns based on user frequent itemset is adapted to identify UGC patterns for user engagement in public health social media. This study aims to generate association rules based on UGC data and identify UGC patterns for user engagement. The frequency of UGC data such as like, link click, share, and comment was analyzed using Apriori approach with support and confidence thresholds. The result shows that 16 association rules were generated based on UGC data. Five with 100%, three with 90%, one with 60%, three with 50%, one with 40%, and three with 30%. It can be concluded that the Apriori approach can be used in identifying the pattern of user engagement on public health social media other than buying and selling of market basket transactions. © 2023 IEEE.

关键词： Association rules

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Efficient Contrast Subspace Mining Method for Categorical Data 22

Efficient Contrast Subspace Mining Method for Categorical Da...

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Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence

作者： Florence Sia Fui Sze Rayner Alfred Faculty of Computing and Informatics Universiti Malaysia Sabah Malaysia

ISBN: (纸本)9781450397773

Mining contrast subspace has recently received attention to identify contrast subspace where a query object is most likely similar to a target class but least likely similar to other class. It has many important applications in various domain such as healthcare, security, finance, and business. Tree-based contrast subspace mining method (TB-CSMiner) has been introduced that is capable to identify contrast subspace of query object in categorical data set. However, the effectiveness of the method has not been evaluated thoroughly. Besides, the efficiency of the method in finding contrast subspace has not yet been examined. Real world data sets more often than not containing large amount of data. It is important to have a method that can identify contrast subspace of query object not only effectively but also efficiently on large data set. This paper uses various classification methods to further evaluate the effectiveness of the TB-CSMiner and assesses the execution speed of the method in mining contrast subspace. TB-CSMiner is experimentally shown to be significantly faster and as effective in identifying contrast subspace of query object on real world categorical data sets.

关键词： Categorical data Mining contrast subspace Contrast subspace Classification Feature Selection Data mining

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