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thesis Management System Website for Suggesting Title and Summary from Abstracts 4

Thesis Management System Website for Suggesting Title and Su...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Ali, Md. Noyan Jahan, Nusrat Siddik, Abu Bokor Akter, Mst. Shorifa Nasir, Nazib Abdun Reza, Md Tanzim Quader, Mozdaher Abdul Kabir, Ms. Sumaiya Rahman, Md Mizanur Green University of Bangladesh Department of Computer Science and Engineering KanchonPurbachal American City 1460 Bangladesh Green University of Bangladesh Department of Electrical and Electronic Engineering Purbachal American City 1460 Bangladesh Brac University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350343670

this paper describes the implementation of a website designed to assist university seniors in selecting a project and thesis topic, as well as providing guidelines for thesis submission and completion. through the usage of artificial intelligence and web programming, the system facilitates project administration while maintaining easy to use interface. the platform also includes a title generator that employs advanced NLP models such as T5 and Bart to generate relevant and original titles based on the abstracts of students' topics. In addition, it provides summarising tools to help users create clear summaries of their work. the study includes a thorough survey of 40 students to determine their requirements and expectations of the system. We also discuss various aspects such as HCI usability, UX design, and common problems encountered by graduating college students. In addition, they describe the common thesis and project management systems in universities around the globe. the system features an interface for project management, a search bar for topic searching, and the Bart and T5 models for title generation. the results of the evaluation of the system indicate a high degree of necessity and usability based on student feedback. the study ends with ideas for improvements that could be made in the future, such as a search bar that uses NLP to make it easier to find topics, students, or teachers. © 2024 IEEE.

关键词： Bart Student Guideline T5 thesis Management System (TMS) Title Generator User Experience (UX)

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Research on the dynamic characterization of hydraulic buffer based on gene expression programming approach 4

Research on the dynamic characterization of hydraulic buffer...

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4th international conference on Defence Technology, ICDT 2024

作者： Tan, Tian Dai, Jin-Song Zhang, Yong-Tao Meng, Chao Lin, Sheng-Ye The School of Mechanical Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China Northwest Institute of Mechanical & Electrical Engineering Shaanxi Xianyang712099 China

this paper is devoted to the dynamic characterization of the autocannon buffer through the surrogate model technique. Autocannon is usually equipped with spring hydraulic buffers, and flow field analysis based on numerical simulation is the focus of buffer characteristic research. However, commercial software is generally computationally expensive and challenging to interact with system dynamic models. To end these, this paper proposes constructing a surrogate model through the gene expression programming (GEP) approach and embedding it into the autocannon bond space model for recoil process analysis. the dataset for the GEP model is derived from the fluid pressure simulation based on computational fluid dynamics (CFD). By this, the buffer surrogate model is bonded to the firing dynamic model by a power bond formed by recoil velocity and hydraulic pressure. Moreover, an engineering example of an autocannon buffer is presented to demonstrate the validity of the proposed method;the analysis results show that the Pearson correlation coefficients between the GEP model and the training and testing sets are 0.99 and 0.98, respectively. the calculated recoil force extremum and recoil velocity are consistent with the experimental results. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Computational fluid dynamics

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Employing Interpretative Structural Modeling to Assess AI's Capability as an Architectural Design Instrument 4

Employing Interpretative Structural Modeling to Assess AI's ...

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4th IEEE international conference on Electronic Communications, Internet of things and Big Data, ICEIB 2024

作者： Hsu, Wei-Ling Dong, Zuorong Mou, Lin Lan, Keran Ye, Runzhi School of Civil Engineering Jiaying University Meizhou China School of Medical Jiaying University Meizhou China

ISBN: (纸本)9798350360721

artificial intelligence (AI)-aided architectural design has been gradually integrated into practice. Interpretative structural modeling (ISM) was used in data analysis. From assistants in living to autonomous vehicles and industrial production to medical care, AI is changing all aspects of our lives as a useful tool. Comprehensive education in science, technology, engineering, art, and mathematics (STEAM education) is a key to cultivating experts in AI architectural design. In this article, the relationship between STEAM education and AI is explored in architectural design by describing the importance and impact of AI on STEAM education from multiple perspectives. © 2024 IEEE.

关键词： Architectural design

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Unscrambling Representation and Recognizing Gaits in an Intelligent Attendance System 4

Unscrambling Representation and Recognizing Gaits in an Inte...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Prathik, K. Girija, J. Bangalore Institute of Technology Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350300888

Traditional approaches to managing attendance have been entirely transformed by the advent of automated and intelligent solutions due to the development of innovative attendance systems. this research introduces a revolutionary intelligent attendance system that uses unscrambled representation and gait identification using an Inception v3 model based on transfer learning to precisely identify people based on their gait patterns and record attendance. By employing transfer learning, the proposed method improves the pre-trained weights of the Inception v3 model using the outdoor gait dataset, enabling it to capture and analyze the distinctive features of human gait accurately. By separating and storing many components that go into a person's gait pattern, incorporating disentangled representation improves the system's accuracy and produces more accurate identification results. the intelligent attendance system has many benefits compared to conventional attendance management strategies. the proposed model streamlines the attendance procedures and reduces administrative expenses by removing the dependency on traditional techniques like ID cards, PINs, or biometrics. the proposed system is non-intrusive in operation, protecting user privacy and convenience. MATLAB is used for programming, and OpenCV is used to process and analyze the video and image data from the database. the system completes comprehensive testing on an outdoor gait dataset with a remarkable accuracy of 94%, proving its reliability and usefulness in practical situations. © 2023 IEEE.

关键词： MATLAB

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Teleoperation in the Age of Mixed Reality: VR, AR, and ROS Integration for Human-Robot Direct Interaction 4

Teleoperation in the Age of Mixed Reality: VR, AR, and ROS I...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Nandhini, P. Chellammal, P. Jaslin, J.S. Harthy Ruby Priya, S. Uma, M. Kaviyaraj, R. Sri Shanmugha College of Engineering and Technology Department of Computer Science and Engineering Sankari India J.J College of Engineering and Technology Department of Computer Science and Engineering Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350300093

Robotic systems that can be operated remotely are becoming more and more common in a variety of industries, including construction, disaster relief, space exploration, and industrial applications. Teleoperation, however, necessitates skilled operators who can handle sophisticated data processing and programming. this research introduces a unique teleoperation method that makes use of Virtual Reality (VR) and the Robot Operating System (ROS) to overcome this problem. By creating a 3D representation of the robot's workspace in Unity and connecting it to the actual robot with ROS, this technique optimizes control. programming is not necessary because the robot's vision serves as the foundation for the user interface. through ROS integration, a target point is established in Unity and sent to the robot as the end effector position. By simply setting up a new environment, reducing the requirement for knowledgeable operators, and improving situational awareness, this method may be used to any sort of robot. For even greater situational awareness, this technique may also be used with augmented reality (AR). AR can give the operator real-time information about the robot's surroundings by superimposing digital information over the physical world. this can be achieved by including AR markers in the physical surroundings that the VR system can detect. In the operator's field of view, the system can then superimpose information such as sensor data or robot status onto the real-world environment. © 2023 IEEE.

关键词： Augmented reality

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Designing an Enhanced MobileNet Integrated Architecture with Optimization for Accurate Detection of ASD using Extracted Facial Features 4

Designing an Enhanced MobileNet Integrated Architecture with...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Balasubramani, Jagadesh Surendran, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331540364

In the research, a new method is proposed to ensure the robust detection and extraction of facial features relevant for differentiating individuals with autism spectrum disorder. A multilevel cascaded Convolutional Neural Network (MCNN) architecture is designed for high-precision face localization in images, which can later provide a basis for subsequent fine-grained feature extraction from faces. Further, at the very initial stage of data preprocessing a very effective contrast adjustment (CA) technique is introduced that would efficiently enhance image quality and uniformity due to different lighting conditions. this improves, much better, the reliability of the facial features extracted and allows more accurate identification of discriminative emotional expression features for ASD. First, state-of-the-art high-precision facial landmark detection (FLD) techniques pinpoint key facial structures, thus helping extract relevant features for ASD classification. To further optimize feature selection, a Sand Cat Swarm Optimization (SCSO) method is introduced that identifies and maximizes subsets of facial features with maximal differences between ASD and non-ASD classes to boost classification accuracy. Finally, a customized hybrid Dense-MobileNet (DMN) architecture is provided that classifies ASD and non-ASD cases efficiently and accurately using the extracted facial features. this architecture synergistically puts together the advantages of dense layer to the MobileNet in order to guarantee high performance by achieving an accuracy of 99.22% in the identification of ASD-related features from the face. © 2024 IEEE.

关键词： Convolutional neural networks

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AFRLNet: Adaptive-context Feature Representation Learning for Facial Expressions recognition via Transformers 4

AFRLNet: Adaptive-context Feature Representation Learning fo...

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4th international conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Liu, Xionghua He, Lulu Liu, Tingting Liu, Hai He, Dingxin Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Wuhan Technology and Business University School of Computer Science and Automation Wuhan China

ISBN: (纸本)9798331534622

Facial expression recognition (FER) is a critical task in intelligent educational technologies, enabling adaptive learning environments to respond to learners' emotional states and support more personalized instruction. this article introduces AFRLNet, a novel FER framework that integrates convolutional neural networks (CNNs) and transformer architectures to enhance the identification and interpretation of student facial expressions. the proposed framework includes two key components: ContextAware Local Feature Learning (CALL) and Adaptive Feature Relationship Induction (AFRI). CALL leverages attention mechanisms to emphasize symmetrical and contextually meaningful facial regions relevant to students' affective responses, while AFRI employs a Transformer-based approach to capture global features and model long-range dependencies, thus addressing challenges such as facial occlusions or varied head poses commonly found in classroom or remote learning settings. Extensive evaluations on the RAF-DB and KDEF datasets demonstrate that AFRLNet achieves state-of-the-art accuracy of 89.44% and 94.76%, respectively, surpassing existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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A Proposed Architecture for Quality based Decision Making Approach for Software Re-engineering 4

A Proposed Architecture for Quality based Decision Making Ap...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Govil, Nikhil Sharma, Nidhi thakral, Deepti Garg, Shalini Dixit, Rinku Chaoudhary, Shailee Lohmor Gla University Department of Cea Mathura India Galgotia College of Engineering and Technology Department of It Greater Noida India Manav Rachna University Department of Computer Science & Technology Faridabad India Mit Art Design and Technology University MH Pune India New Delhi Institute of Management Department of Aiml - Data Science New Delhi India

ISBN: (纸本)9798350300093

Software re-engineering is the modification of a software system after it has been reverse engineered, typically to add new functionality or fix errors. Software re-engineering involves a set of activities aimed at reorganizing a legacy system into a new target system that conforms to quality constraints. this paper explores the reasons behind the failure of software re-engineering. this study has analyzed that some of the most common factors for failure of software re-engineering are misestimated costs and quality inefficiencies. As far as the quality factor is concerned, first the basic tool architecture is analyzed which is used by most of the tools for reverse engineering, reorganization and re-engineering. In this research study, a quality evaluation factor as well as parameters for quality evaluation are added to that particular architecture, so that customers and maintainers can analyze whether they achieve the desired goals for re-engineering. Ifthe target is achieved then the finished product is obtained again with quality and within budget, otherwise more attention should be given to the quality evaluation process. © 2023 IEEE.

关键词： Reverse engineering

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Underwater Image Enhancement using CLAHE Fusion in YIQ and HSI Color Model 4

Underwater Image Enhancement using CLAHE Fusion in YIQ and H...

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4th IEEE international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Kumar, Ravi Kumar, Ashish Kumar, Manikant Rai, Mithilesh Kumar Kumari, Shweta Mourya, Vishal Electronics and Communication Engineering Muzaffarpur Institute of Technology Muzaffarpur India Computer Science Engineering Muzaffarpur Institute of Technology Muzaffarpur India Leather Technology Muzaffarpur Institute of Technology Muzaffarpur India Information Technology Muzaffarpur Institute of Technology Muzaffarpur India National Institute of Electronics and Information Technology Patna India

ISBN: (纸本)9798331528614

- Underwater image enhancement has been employed in a variety of areas, such as terrain scanning, underwater microscopic detection, telecommunication cables, mine detection, and autonomous underwater vehicles. However, underwater imagery suffers from strong scattering, noise, color distortion, and absorption from artificial light sources, causing haziness, image blur, and a greenish or bluish texture. To address these issues, the proposed work introduces a new enhancement approach based on Contrast Limited Adaptive Histogram Equalization (CLAHE) used for images taken underwater. the proposed method employs CLAHE to adaptively enhance local contrast while constraining the amplification of noise and artifacts. the fusion method is proposed to enhance the complementary advantages of different color spaces in YIQ and HSI color models. the YIQ color space separates intensity from chrominance, allowing for independent processing of luminance information, which is crucial for preserving image details and contrast. the proposed algorithm improves low-contrast underwater images with intact color and a natural appearance with reliability according to the results of the qualitative analysis. the proposed method outperforms the state-of-the-art methods in terms of image performance measures like GMSD, VSI, MCSD, and PSNR. ©2024 IEEE.

关键词： Underwater imaging

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Student Attendance Tracking Management using Face Biometric Smart System 4

Student Attendance Tracking Management using Face Biometric ...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Balakrishnan, D. Mariappan, Umasree Kumar, Satrasala Vinay Kumar, Murari Dinesh Reddy, MacHupalli Ravi Sankar Reddy, Enja Mokshith Kumar Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnakoil626126 India P.S.R. Engineering College Department of Information Technology Tamil Nadu Sivakasi626140 India

ISBN: (纸本)9798350349900

the 'Face Recognition-Based Smart Attendance Management System' proposes an innovative solution for automating attendance tracking in educational and corporate settings. this system employs a series of pre-processing techniques including face alignment and illumination normalization to enhance the quality of input images. Feature extraction is achieved through the utilization of deep learning methods such as Transductive Support Vector Machine (TSVM) and Histogram of Oriented Gradients (HOG) for capturing facial landmarks and unique features. the recognition stage utilizes state-of-the-art algorithms like FaceNet and VGGFace to accurately identify individuals, ensuring efficient and accurate attendance management with real-time data updates. In addition to its robust feature extraction and recognition techniques, the system incorporates data storage and management components, making it capable of maintaining a comprehensive database of student or employee attendance records. through continuous learning and adaptation, the system ensures consistent performance across varying lighting conditions and poses. Furthermore, it can provide real-time feedback to instructors or administrators, facilitating swift responses to attendance irregularities. this smart attendance management system not only simplifies record-keeping but also enhances security and accountability, offering a reliable and efficient solution for attendance monitoring in diverse educational and professional environments. © 2024 IEEE.

关键词： Students

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