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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Bisht, Girish Singh Jain, Arpit Bansla, Vandna Sharma, Khemraj Bhutia, Rudramani Kumar, Vikas Tula's Institute Department of Computer Science & Engineering Dehradun248001 India Koneru Lakshamaiah Education Foundation Department of Computer Science & Engineering A.P. Vadessahwaram India Shivalik Engineering College Dehradun248001 India Nanhua University Doctoral Program in Management Sciences Department of Business Administration Dalin Chiayi62248 Taiwan Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering A.P. India Dev Bhoomi Uttrakhand University School Engineering and Computing Uttrakhand Dehradun India

ISBN: (纸本)9798350350067

Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery detection techniques like Copy-Move Forgery Detection (CMFD) cannot detect these forged documents because they are unable to identify a sufficient number of effective keypoints in homogeneous areas, leading to inaccurate and inefficient results. SURF stands for Speeded-Up Robust Features and is used in this paper along with A-KAZE and Scale-Invariant Feature Transforms. According to our experiment, A-KAZE offers superior detection performance under diverse attacks, especially when it comes to large-scale attacks targeting homogeneous regions. A-KAZE is found to be more accurate than SIFT, SURF, and A-KAZE when applied to the NB-CASIA dataset, achieving detection accuracies of 89.2%, 93.9% and 98.98%.) © 2024 IEEE.

关键词： Image analysis

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Accessing Gender Bias in Speech Processing Using Machine Learning and Deep Learning with Gender Balanced Audio Deepfake Dataset

Accessing Gender Bias in Speech Processing Using Machine Lea...

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International Conference on Informatics and Computing (ICIC)

作者： Tricia Estella Amalia Zahra Wai-Keung Fung Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Department of Engineering Cardiff School of Technologies EUREKA Robotics Centre Cardiff Metropolitan University United Kingdom

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The rapid advancement of AI has led to the rise of Audio Deepfakes (AD), which pose serious ethical and security concerns by accurately mimicking human speech. This research addresses the urgent need for effective AD detection, with a focus on gender bias that can reduce the effectiveness of detection models. We examined how gender affects the performance of both Machine Learning (Support Vector Machine, Random Forest, Logistic Regression, XGBoost) and Deep Learning (Deep Neural Networks, Convolutional Neural Networks) models using the GBAD dataset. Our findings show that models trained on female audio outperform those trained on male audio, likely due to the expressive nature of female voice features and high-pitched artifacts in FAKE audio. This highlights the need for more robust, gender-sensitive detection systems. Future work should focus on developing adaptive models to reduce gender bias, improving security, and creating lightweight models for wider public use.

关键词： Deep learning Support vector machines Deepfakes Adaptation models Logistic regression Computational modeling Security Speech processing Informatics Random forests

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Deep-Learning Approach for An Analysis of Real-estate Prices and Transactions

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IEEE Access 2025年 13卷 89248-89265页

作者： Yang, Cheng-Hong Lee, Borcy Lin, Yu-Da Tainan University of Technology Department of Information Management Tainan Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung Taiwan Kaohsiung Medical University Ph.D. Program in Biomedical Engineering Kaohsiung Taiwan Kaohsiung Medical University School of Dentistry Kaohsiung Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Kaohsiung Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors—the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index—significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced b

关键词： Houses

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AV Navigation in 3D Urban Environments with Curriculum-based Deep Reinforcement Learning

AV Navigation in 3D Urban Environments with Curriculum-based...

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2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023

作者： Braathen De Carvalho, Kevin De Oliveira, Iure Rosa L. Brandao, Alexandre S. Universidade Federal de Viçosa Nucleus of Specialization in Robotics Department of Electrical Engineering Graduate Program in Computer Science MG Viçosa36570-900 Brazil Federal University of Viçosa Department of Electrical Engineering MG Viçosa Brazil

ISBN: (纸本)9798350310375

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV's decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments. © 2023 IEEE.

关键词： Reinforcement learning

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A Vision-Based Neural Networks Model for Turbine Trench-Filler Diagnosis 1

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7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023

作者： Isaza, Cesar Guerrero-Garcia, Fernando Anaya, Karina Jenab, Kouroush Ortega-Moody, Jorge Networking and Telecommunication Academic Program Polytechnic University of Queretaro Queretaro Mexico Productive System Master Academic Program Polytechnic University of Queretaro Queretaro Mexico School of Engineering and Computer Science Morehead State University MoreheadKY40351 United States

ISBN: (数字)9783031396199

ISBN: (纸本)9783031396182

Vision-based neural networks as artificial intelligence models have been critical in many manufacturing industries, including automotive, food, and aerospace. Machine vision and deep learning have provided practical, promising, and accessible innovations to address many problems during manufacturing and diagnostics. Recent studies offer beneficial results in implementing industrial artificial intelligence systems that require determining, comparing, and evaluating optimal technological solutions. The aerospace industry has to deal with the problem of diagnosing critical components that continually need the expertise of a trained human being. On the other hand, automatic diagnostics are becoming a critical technology to deal with this problem. However, these systems require a particular configuration of computer vision algorithms which, in the case of turbine trench-filling components, have yet to be added to the literature. Considering the above, we report in this paper a new methodology that uses a pre-trained deep neural network framework available online to automatically diagnose geometric nonconformities in aeronautical components that protect the turbines from vibrations and reduce noise from the engines to the aircraft deck. The method is based on the following stages: a computer vision system with a monochrome camera to acquire images, training of deep neural networks with transfer learning, and a stage to analyze nonconformities automatically. In addition, a benchmark of several pre-trained deep neural networks is presented to address the problem. According to the experimental results, the YOLO deep neural network topology significantly contributes to automatic diagnosis with high precision and exact rate. Finally, we show that integrating deep neural networks with control graphs is a promising strategy to optimize online diagnostics in the production lines of the aeronautical industry. © The Author(s), under exclusive license to Springer Nature Switzerland

关键词： Deep neural networks

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Stress detection system for social media users

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Procedia computer science 2023年 216卷 672-681页

作者： Mochamad Rizky Febriansyah Nicholas Rezki Yunanda Derwin Suhartono Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Software Engineering Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency – inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM.

关键词： Machine Learning Stress Detection Social Media Natural Language Processing

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Designing gardening applications to engage people with intellectual disability in gardening activities 23

Designing gardening applications to engage people with intel...

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Proceedings of the 35th Australian computer-Human Interaction Conference

作者： Saminda Sundeepa Balasuriya Laurianne Sitbon School of Electrical Engineering and Robotics Queensland University of Technology Australia School of Computer Science Queensland University of Technology (QUT) Australia

ISBN: (纸本)9798400717079

People with intellectual disability like to engage in outdoor activities such as gardening. Many adults with intellectual disability also like using applications such as social media and mobile games in their leisure time. This study looked to co-design applications to help engage people in the gardening activities conducted at a disability day centre. The research team worked with a partner disability organisation and worked with support workers and people with intellectual disability to co-design applications that included both parties values and requirements. One application focussed on the social aspect of gardening along with tracking the growth of plants whereas the second application focussed on gamifying learning about things found in the garden. The features of the apps that engaged the participants are discussed in this paper.

关键词： Insert comma delimited author-supplied keyword list Keyword number 2 Keyword number 3 Keyword number 4

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Revolutionizing Tuberculosis Treatment: Smart Technology Innovations for Mycobacterium tuberculosis Management

Revolutionizing Tuberculosis Treatment: Smart Technology Inn...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Pramanik, Atreyi Singh, Ajay Rani, Gouri Dubey, Shival Aluvala, Srinivas Uttaranchal University School of Applied and Life Sciences Division of Research and Innovation Dehradun India Uttaranchal University School of Applied and Life Sciences Dehradun India Uttaranchal University India University of Leeds Institute of Design Optimization and Robotics School of Mechanical Engineering United Kingdom SR University School of Computer Science & Artificial Intelligence India

ISBN: (纸本)9798350394412

Tuberculosis remains a significant global problem. The modification in smart technology (ST) invention generates novel chances to reform tuberculosis (TB) controlling. This work explores the potential of ST for filtering MTB diagnostics and therapy. ST includes telemedicine, mobile health uses (mHealth), wearable devices (WD) and data analytical that deliver many options of improving TB control. Telemedicine licenses distant consulting thus empowering medics to access patients in remote areas, offer quick detects as well as recommend treatment. mHealth apps certify that individuals have right of entry to education materials, can remind them when it's time for medication and offers self-monitoring tools which enhance adherence to the therapy by keeping the patient involved during the course of treatment. WD support real-time (RT) monitoring of heart rates, blood pressure level, physical activity and other vital signs that are important in coming up with modified remedy plans while also detecting response to cure earlier enough. Data analytics combined with AI can examine large amounts of patient data to identify patterns, forecast drug effects and optimize treatment strategies resulting in more efficient individualized care. These novelties through intellectual technologies may be used as solutions for tasks experienced during TB management, leading to improved patient outcomes and a reduction in the TB burden. © 2024 IEEE.

关键词： Telemedicine

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ARI humanoid robot imitates human gaze behaviour using reinforcement learning in real-world environments

ARI humanoid robot imitates human gaze behaviour using reinf...

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IEEE-RAS International Conference on Humanoid Robots

作者： Khashayar Ghamati Abolfazl Zaraki Farshid Amirabdollahian School of Physics Engineering and Computer Science (SPECS) and Robotics Research Group of the University of Hertfordshire Hatfield United Kingdom

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

This paper presents a novel approach to enhance the social interaction capabilities of the ARI humanoid robot using reinforcement learning. We focus on enabling ARI to imitate human attention/gaze behaviour by identifying salient points in dynamic environments, employing the Zero-Shot Transfer technique combined with domain randomisation and generalisation. Our methodology uses the Proximal Policy Optimisation algorithm, training the reinforcement learning agent in a simulated environment to maximise robustness in real-world scenarios. We demonstrated the efficacy of our approach by deploying the trained agent on the ARI humanoid and validating its performance in human-robot interaction scenarios. The results indicated that using the developed model, ARI can successfully identify and respond to salient points, exhibiting human-like attention/gaze behaviours, which is an important step towards acceptability and efficiency in humanrobot interactions. This research contributes to advancing the capabilities of social robots in dynamic and unpredictable environments, highlighting the potential of combining ZeroShot Transfer with domain randomisation and generalisation for robust real-world applications.

关键词： Training Heuristic algorithms Social robots Humanoid robots Reinforcement learning Robustness Optimization

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Behavior Replication of Cascaded Dynamic Systems Using Machine Learning

Behavior Replication of Cascaded Dynamic Systems Using Machi...

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2023 IEEE International Symposium on Robotic and Sensors Environments, ROSE 2023

作者： Abouheaf, Mohammed Gueaieb, Wail Mayyas, Mohammad Bowling Green State University Robotics Engineering Bowling GreenOH43403 United States Ottawa University School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada

ISBN: (纸本)9798350308044

This paper introduces an innovative data-driven approach for replicating behaviors in interconnected and heterogeneous dynamic systems. The core concept involves real-time control of dynamic systems to closely mimic reference-model trajectories using model-free techniques. Within this coupled framework, one component possesses complete information about reference-trajectories, although not necessarily their dynamics. In contrast, follower systems, with limited connectivity to reference-model trajectories, exclusively replicate the behavior of the primary process, which retains insight into model-reference dynamics. The adopted strategies are causal, integrating higher-order error dynamics to ensure precise tracking of reference-trajectories. Furthermore, these strategies incorporate variations in reference-model dynamics via a pseudo partial derivative, akin to sensitivity derivatives in model-reference adaptive strategies. To optimize the dynamic behavior of the follower process, the solution employs a reinforcement learning mechanism through adaptive critics. This mechanism approximates the optimal strategy and the associated value function. The actor and critic weights of the adaptive critic structure are tuned using a projection technique to ensure convergence of the adapted strategy. The validation of this solution is demonstrated on a dynamic system with delays, simulating an underwater vehicle scenario. The developed methodology is rigorously compared with another high-order model-free adaptive control approach. The presented approach showcases its capability to effectively replicate behaviors, resulting in improved tracking accuracy. © 2023 IEEE.

关键词： Trajectories

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