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Multimodal Deep Neural Networks for Digitized Document Classification

computer Systems Science & engineering 2024年第3期48卷 793-811页

作者： Aigerim Baimakhanova Ainur Zhumadillayeva Bigul Mukhametzhanova Natalya Glazyrina Rozamgul Niyazova Nurseit Zhunissov Aizhan Sambetbayeva Department of Computer Engineering Khoja Akhmet Yassawi International Kazakh-Turkish UniversityTurkistanKazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstanaKazakhstan Department of Information and Computing Systems Non-Profit Joint Stock Company Abylkas Saginov Karaganda Technical UniversityKaragandaKazakhstan Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan

As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially *** respond to the urgent need to categorize the growing number of digitized documents,the classification of digitized documents in real time has been identified as the primary goal of our study.A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human *** intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits,which were not conceivable ten years *** learning aids in comprehending input patterns so that object classes may be *** segmentation process divides the input image into separate segments for a more thorough image *** study proposes a deep learning-enabled framework for automated document classification,which can be implemented in higher *** further this goal,a dataset was developed that includes seven categories:Diplomas,Personal documents,Journal of Accounting of higher education diplomas,Service letters,Orders,Production orders,and Student ***,a deep learning model based on Conv2D layers is proposed for the document classification *** the final part of this research,the proposed model is evaluated and compared with other machine-learning *** results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%,94.79%,94.62%,94.43%,94.07%in accuracy,precision,recall,F-score,and AUC-ROC,*** achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker.

关键词： Document categorization deep learning machine learning classification digitization

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Global Decision Making Support for Complex System Development

Global Decision Making Support for Complex System Developmen...

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IEEE International Conference on Requirements engineering

作者： Lola Burgueño Damien Foures Benoit Combemale Jörg Kienzle Gunter Mussbacher ITIS Software University of Malaga Malaga Spain DDMS Airbus Group Toulouse France ESIR & IRISA University of Rennes Rennes France School of Computer Science McGill University Montreal Canada Department of Electrical and Computer Engineering McGill University Montreal Canada Department of Electrical and Computer Engineering INRIA Rennes France

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

To succeed with the development of modern and complex systems (e.g., aircrafts or production systems), organizations must have the agility to adapt faster to constantly evolving requirements in order to deliver more reliable and optimized solutions that can be adapted to the needs and environments of their stakeholders including users, customers, suppliers, and partners. However, stakeholders do not have sufficiently explicit and systematic support for global decision making, considering the vast decision space and complex inter-relationships. This decision space is characterized by increasing yet inadequately represented variability and the uncertainty of the impact of decisions on stakeholders and the solution space. This leads to an ad-hoc decision making process that is slow, error-prone, and often favors local knowledge over global, organization-wide objectives. As a result, one team's design decisions may impose too restrictive requirements on another team. In this paper, we evaluate our understanding of global decision making in the context of complex system development based on a conceptual model which explicitly represents and manages decision spaces including variability and impacts. We have conducted our evaluation by means of an exploratory case study where we interviewed domain experts with an average of 20 years of experience in complex system industries and report the key findings and remaining challenges. In the future, we aim at providing explicit and systematic tool-supported approaches for global decision making support for complex systems.

关键词： Industries Production systems Systematics Uncertainty Decision making Organizations Stakeholders

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Dynamic priority assignment for aperiodic tasks in limited preemptive scheduling

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IEIE Transactions on Smart Processing and Computing 2020年第5期9卷 365-370页

作者： Choi, Jae-Woo Lee, Seong-Won Embedded Software Engineering Kwangwoon University Korea Republic of Department of Computer and Information Engineering Kwangwoon University Korea Republic of

Internet of Things (IoT) systems and other real-time systems are required to handle both periodic and aperiodic incoming tasks. In a situation in which periodic and aperiodic tasks are mixed into task scheduling using a fixed priority system, the schedulability of periodic tasks could be reduced due to interruptions by aperiodic tasks. This study analyzes the effects of aperiodic tasks on non-preemptive scheduling and limited preemptive scheduling methods, such as fixed preemption points, preemption threshold scheduling, and deferred preemptive scheduling, which have recently been proposed by complementarily combining fully preemptive and non-preemptive scheduling. This study then proposes an algorithm for assigning an appropriate priority to an aperiodic task by analyzing the worst-case response time of all tasks to enhance schedulability of both periodic and aperiodic tasks. Copyrights © 2020 The Institute of Electronics and Information Engineers

关键词： Real time systems

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Enhanced Keypoint-Based Approach for Identifying Copy-Move Forgery in Digital Images

Enhanced Keypoint-Based Approach for Identifying Copy-Move F...

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International Conference on Contemporary Computing and Informatics (IC3I)

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

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

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 $\mathbf{8 9. 2 \%}, \mathbf{9 3. 9 \%}$ and $\mathbf{9 8. 9 8 \%}$.)

关键词： Accuracy Digital images Forensics Transforms Forgery Computational efficiency Reliability Informatics software development management

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Enhancing Autonomous Driving with Pre-trained Imitation and Reinforcement Learning

Enhancing Autonomous Driving with Pre-trained Imitation and ...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Jeong-Hwan Choi Dong-han Kim Ji-Sang Yoo Beom-Joon Kim Jun-Tae Hwang Department of Electronic Engineering DongSeoul University Seongnam Republic of Korea Department of Computer Engineering Kyung Hee University Yongin Republic of Korea Department of Artificial Intelligence Kyung Hee University Yongin Republic of Korea Department of Software Convergence Kyung Hee University Yongin Republic of Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper proposes a method that combines imitation learning and reinforcement learning to improve the learning performance of autonomous vehicles. The actor model was pre-trained using expert demonstration data from the CARLA simulator, and then integrated into PPO-based reinforcement learning. The experimental results showed that the model combining imitation learning outperformed the model using only reinforcement learning, particularly in the early stages of training. However, this study is limited by using only image data, and future research will incorporate vehicle position and speed information for enhanced results.

关键词： Training Imitation learning Reinforcement learning Data models Autonomous vehicles

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Advancements in Cybersecurity Using Deep Learning Techniques Attack Detection for Trojan Horses

Advancements in Cybersecurity Using Deep Learning Techniques...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Hosanna Princye P Ahila A A. Anna Lakshmi N. Ragavendran K. E. Purushothaman G. Uma Maheswari R. Saravanakumar Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bangalore Anekal India Department of Information Technology R.M.K. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Software Analyst Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Today's computer networks are threatened by Trojan horse assaults and other cybersecurity dangers. We propose and evaluate deep learning methods using the Kaggle-hosted Trojan Detection dataset to detect Trojan horse assaults. This project uses these approaches. Our malware detection technology uses CNN classification for accuracy. We clean and enhance the dataset to make the model more versatile. Due to extensive testing and optimization, Trojan horse detection rates reach 98.47%. We found that the deep learning architecture accurately identifies malware. We also discuss CNN model features that illuminate Trojan horse attacks. The technique offers complete cyber security and expands our knowledge of harmful software. Deep learning may boost cybersecurity defenses against emerging attacks like Trojan horses, according to our study.

关键词： Deep learning Training Accuracy Feature extraction Safety Trojan horses Convolutional neural networks computer crime Testing Python

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Microwave-Based Complementary Split-Ring Resonator for the Detection of Variations in Aqueous Media

Microwave-Based Complementary Split-Ring Resonator for the D...

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Microelectronics Technology and Devices (SBMicro)

作者： Reinaldo Velásquez Diego Tami Gilberto Medeiros-Ribeiro Cássio G. Rego Jhonattan C. Ramirez Graduate Program in Electrical Engineering UFMG Brazil Institute of Technological Sciences UNIFEI Brazil Department of Computer Science UFMG Brazil Department of Electronics Engineering UFMG Brazil

In this paper, we demonstrated a highly sensitive microwave-based resonator sensor capable of detecting variations in an aqueous medium of up to 0.94 mg/dL. In the presented device, the intrinsic resonant response (resonant amplitude, resonant frequency, and quality factor) is capacitively perturbed in reaction to the sample presence. To achieve higher sensitivity, the sample under testing generally is placed on the resonator surface, where the electric field is highly concentrated at the operating frequency. Thus, demonstrating excellent results in terms of sensitivity and reproducibility.

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Integrating virtual reality technology into architecture education: the case of architectural history courses

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Open House International 2021年第4期46卷 498-509页

作者： Ibrahim, Anwar Al-Rababah, Amneh Ibrahim Bani Baker, Qanita Department of Architecture Jordan University of Science and Technology Irbid Jordan Department of Computer Science and Software Engineering Jordan University of Science and Technology Irbid Jordan

Purpose: Virtual reality (VR) technology invaded various domains including architecture practice and education. Despite its high applications in architecture design education, VR has a high potential to be used in architectural history courses as well. This paper aims to examine the effect of using VR technology on the students’ learning abilities of history of architecture. Design/methodology/approach: The experimental approach was used. Two experiments were designed by creating virtual environments for two selected architectural examples from the Modern Architecture course. The participants who were students of Modern Architecture class had to complete two questionnaires, one for each example. The first one was based on Bloom’s taxonomy, and the other was prepared to test the participants’ analytical and critical skills. Besides, participants had to fill out satisfaction and ease-of-use survey on a five-step Likert scale. Findings: Participants in the VR condition achieved better grades in knowledge gain compared to those in the traditional conditions. Their analytical and critical thinking skills were improved in the VR conditions. Gender has a significant impact on analytical and critical thinking skills. Participants recorded a high level of satisfaction;however, male students were more satisfied than female students who reported concerns about the weight of the used tools and nausea symptoms. Research limitations/implications: This study informs architecture education and provides insights into the potentials of using advanced technology in architectural history education. Teaching the various history of architecture courses will be improved, shifted toward a more student-centered curriculum, and may acquire more excitement and conscious curiosity. Originality/value: Using VR in architectural education is rigorous in architectural design courses and students’ design projects’ presentations. This research expands architectural education research by examining ot

关键词： Architectural education Bloom’s taxonomy Built environment tectonics and technologies History of architecture VR technology

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Deep Reinforcement Learning for Autonomous Drone Navigation in Cluttered Environments

Deep Reinforcement Learning for Autonomous Drone Navigation ...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Gautam Solaimalai Kode Jaya Prakash Sampath Kumar S A Bhagyalakshmi P Siddharthan K. R Senthil Kumar Software Engineering Manager U.S. Bank Georgia United States of America Department of Mechanical Engineering VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad Telangana India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Civil Engineering Nehru Institute of Technology Coimbatore Tamil Nadu India Department of Mechanical Engineering R.M.K. Engineering College Thiruvallur Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

This exploration paper investigates the operation of deep underpinning literacy(DRL) for enabling independent drone navigation in cluttered surroundings. Navigating drones in cluttered spaces poses significant challenges due to the presence of obstacles and dynamic environmental conditions. Traditional navigation approaches frequently struggle to acclimatize to these complications. In this study, we propose a new frame using DRL ways to enable drones to autonomously navigate through cluttered surroundings while avoiding obstacles. The frame employs a deep neural network to learn a policy that guides the drone’s conduct grounded on environmental compliances. Through expansive simulations and real-world trials, we demonstrate the efficacity of the proposed approach in achieving robust and adaptive drone navigation in cluttered surroundings. The findings of this exploration have significant counteraccusations for colorful operations, including hunt and deliverance operations, surveillance, and package delivery, where independent drone navigation in cluttered spaces is pivotal.

关键词： Adaptation models Navigation Surveillance Artificial neural networks Aerospace electronics Deep reinforcement learning Product delivery

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Exploring Augmented Reality's Influence on Cognitive Load and Emotional Dynamics within UAV Training Environments

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IEEE Transactions on Learning Technologies 2025年 18卷 581-592页

作者： Rahimi, Fatema Sadeghi-Niaraki, Abolghasem Song, Houbing Wang, Huihui Choi, Soo-Mi Sejong University Department of Computer Science and Engineering Convergence Engineering for Intelligent Drone XR Research Center Seoul Korea Republic of Department of Information Systems BaltimoreMD21250 United States St. Bonaventure University Cybersecurity Program St. BonaventureNY14778 United States

This study investigates the cognitive and emotional processes involved in augmented reality (AR)-based learning. The study looks at learning outcomes, emotional responses, meditation, and attention using a comprehensive approach that includes self-assessment, EEG data gathering, and post-experiment questionnaires. Twelve participants, selected based on their English proficiency and lack of prior knowledge of the course material, engaged in AR-based learning, while a baseline reading condition was included to contextualize cognitive and emotional engagement. The study findings indicate that the AR group's participants demonstrated notably elevated attention and meditation levels, indicating heightened engagement and focus that is advantageous for efficient assimilation and retention of knowledge. Furthermore, AR learners reported feeling less tired and exhausted, which may have mitigated the negative emotional states that are frequently connected to learning activities. However, no significant differences in negative emotions were observed between the reading and AR groups. These results emphasize the value of customized AR environments for education goals and the need for more study to maximize learning outcomes and affective experiences in AR learning contexts. © 2008-2011 IEEE.

关键词： engineering education

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