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检索条件"机构=Faculty of Computing and Informatics"

共 3670 条记录，以下是331-340 订阅

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Advancing Weed Detection in Agricultural Landscapes Using Computer Vision 5

Advancing Weed Detection in Agricultural Landscapes Using Co...

引用

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

作者： Tan, Bei Ren Moung, Ervin Afnan Uda, Muhd. Nur Kin Teo, Kenneth Tze Angeline, Lorita School of Computer Science Taylor's University Subang Jaya Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

This paper explores the need for weed detection in agricultural landscapes, where weed infestations pose significant crop productivity challenges. To address this matter, computer vision techniques are utilised to create a weed detection algorithm that accurately detects weed patches inside agricultural field images. To ensure reliable model evaluation, the dataset is split into training and testing subsets before being fed into the YOLOv3 model. The YOLOv3 model performs well in terms of precision, with an average precision of 66.4% for crop regions and a notable precision of 92.5% for detecting weeds. These results highlight the potential of artificial intelligence-driven weed detection techniques to optimize crop management practices and foster sustainable agriculture in the modern era. This research advances precision agriculture by effectively addressing weed infestations, paving the way for more efficient and targeted weed control strategies, which ultimately lead to increased crop yield and improved agricultural sustainability. © 2023 IEEE.

关键词： Computer vision

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Exploring the Hybrid Neural Network and Attention Mechanism for Classification of Social Bias 15

Exploring the Hybrid Neural Network and Attention Mechanism ...

引用

15th International Conference on Knowledge and Systems Engineering, KSE 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)9798350329742

Neural Network models are considered universal learning due to their powerful ability to solve text classification with different data types. Hybrid neural networks in text classification using bidirectional Long Short-term Memory (BiLSTM), Convolutional Neural Network (CNN) and bidirectional Gated Recurrent Unit (BiGRU) have been widely used. In this study, we conducted a preliminary study on text classification of social bias data by combining the CNN, Bidirectional LSTM, Bidirectional GRU, and attention mechanism. The output from bidirectional architectures are given a distinct focus through attention mechanisms employed in both layers. Social bias is opinionated data which is often demonstrated in stereotypical and prejudiced behavior, presenting unfair decision including hatred and emotion about people's identity such as race, religion, ethnicity and gender. Social bias is considered under low resource scenario whereby the data size is limited and subjective. The result shows the proposed hybrid model gives promising results and good potential in learning a small size of data compared to single models. © 2023 IEEE.

关键词： Long short-term memory

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Adaptive Bipartite Consensus of Linear Multi-agent Systems Under Jointly Connected Topologies

Adaptive Bipartite Consensus of Linear Multi-agent Systems ...

引用

International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Yuan, Yi Sainin, Mohd Shamrie Tang, Lili Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia College of Computer Science Hunan University of Technology Zhuzhou412008 China

ISBN: (纸本)9789811966125

In this paper, bipartite consensus is discussed for multi-agent systems (MASs) under jointly connected switching topologies. An adaptive bipartite consensus protocol is proposed for MASs under jointly connected topologies. Under the proposed protocol, bipartite consensus can be realized with some fully conditions. Finally, a simulation example is shown to verify the theoretical results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Modified Metaheuristic Optimization Algorithm for Hyperparameter Tuning in Mental Health Applications

Modified Metaheuristic Optimization Algorithm for Hyperparam...

引用

Mobile computing and Sustainable informatics (ICMCSI), International Conference on

作者： Stanislava Kozakijevic Luka Jovanovic Smiljana Tedic Ninoslava Jankovic Miodrag Zivkovic Nebojsa Bacanin Faculty of Media and Communication Singidunum University Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The World health organization considers a mentally healthy person to be able to manage life's stresses, work productively, and contribute to their communities. However, mental disorders, affecting 970 million people in 2019, often reduce quality of life, necessitating accurate diagnosis and therapy. Affective disorders, encompassing depressive and manic episodes, vary in severity and pattern, with conditions like bipolar disorder and recurrent mood disorders identified through ICD-10 classifications. Diagnosis, typically made by expert teams, remains challenging due to the complex and variable nature of mental disorders, leading to frequent misdiagnoses that hinder treatment progress and erode patient trust. Artificial intelligence (AI) holds promise in supporting mental health diagnosis by identifying subtle patterns in symptoms and validating expert conclusions. While AI's specialized nature poses limitations, integrating it into clinical practice can improve diagnostic accuracy and reduce errors. High quality models can assist doctors by ruling out less likely conditions and confirming diagnoses. Optimizing AI models, however, requires effective hyperparameter selection, a complex task classified as NP-hard. Metaheuristic algorithms offer a solution, enabling efficient parameter tuning and improving AI model performance. Given the critical implications of accurate mental health diagnoses, leveraging AI and optimization techniques is essential to enhance outcomes and improve lives. This work proposed a modified metahetusirc aimed at optimizing classification accuracy. When evaluated on a genuine dataset the best performing models attain an accuracy of 0.972222 suggesting feasibility in real world situations.

关键词： Accuracy Mood Mental disorders Metaheuristics Mental health Organizations Classification algorithms Artificial intelligence Tuning Mobile computing

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

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

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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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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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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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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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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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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

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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