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Integrating Structural info.mation: Comparing Classification-Based and Segmentation-Based Predictors in Bioinfo.matics (case study: protein-peptide region residue-level interaction) using structural info.mation 29

Integrating Structural Information: Comparing Classification...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Shafiee, Shima Fathi, Abdolhossein Safari, Faezeh Department of Computer Engineering and Information Technology Razi University Kermanshah Iran School of Electrical Eng. and Computer Science Faculty of Eng. Architecture and Info. Tech University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798331523114

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method. © 2025 IEEE.

关键词： Bioinfo.matics

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Integrating Structural info.mation: Comparing Classification-Based and Segmentation-Based Predictors in Bioinfo.matics (Case Study: Protein-Peptide Region Residue-Level Interaction) Using Structural info.mation

Integrating Structural Information: Comparing Classification...

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International CSI computer Conference, CSICC

作者： Shima Shafiee Abdolhossein Fathi Faezeh Safari Department of Computer Engineering and Information Technology Razi University Kermanshah Iran Faculty of Eng. Architecture and Info. Tech. School of Electrical Eng. and Computer Science University of Queensland Brisbane QLD Australia

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

关键词： Proteins Support vector machines Deep learning Peptides Predictive models Sensitivity and specificity Prediction algorithms Robustness Drug discovery Testing

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Standardization Review on Non-Terrestrial Networks

Standardization Review on Non-Terrestrial Networks

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International Conference on info.mation Networking

作者： Yeunwoong Kyung Jaewook Lee Haneul Ko Seungmin Oh Jaehwan Lee Youngjun Kim Div. of Info. & Comm. Eng Kongju National University Cheonan Korea Dep. of Info. and Comm. Engineering Pukyong National University Busan Korea Dep. of Electronic Eng Kyung Hee University Yongin-si Korea Dep. of Computer Science and Eng Kongju National University Cheonan Korea School of Computer Science and Eng Kyungnam University Changwon Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

As the demand for global connectivity increases, non-terrestrial networks (NTNs), particularly satellite-based systems, have gained significant attention in communication network standardization. The 3rd Generation Partnership Project (3GPP) has been at the forefront of developing standards that incorporate satellite communications into the broader terrestrial network framework. This paper reviews the progression of NTN standardization, focusing on three key stages: (1) the use of satellites as relays or the next generation NodeBs (gNBs), (2) the integration of user plane functions (UPF) within satellites, and (3) the inclusion of control plane network functions (NFs) within satellites. These developments in standardizations reflect a shift from basic relay functions to fully integrated satellite networks.

关键词： Satellites 3GPP Standards Reviews Trajectory 3GPP Relays Low latency communication Satellite communications Standards Next generation networking

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AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques

AADS: An Automated Accident Detection and Nighttime Surveill...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Karthikeyan, M. Kshirsagar, Pravin Ramdas Tak, Tan Kuan Lalit, Keshav Upreti, Kamal Jain, Rituraj School of Computing Srm Institute of Science and Technology Dept. of Computing Technologies Chennai India J.D College of Engg & Mgmt. Department of Electronics Engg. MS Nagpur India Singapore Institute of Technology Singapore Manipal Institute of Technology Dept. of Computer Science & Engineering Karnataka Manipal India Christ University Department of Computer Science Delhi NCR Ghaziabad India Marwadi University Department of Info. Tech. Gujarat Rajkot India

ISBN: (纸本)9798331527495

computer vision-based surveillance is very important today's security systems to detect, track and regulate the security much better than standard cameras. However, like any other performance measurement systems they have potential pitfalls and technical, ethical, and legal implications must be well understood. The continuous rise in connection and interaction implies that safety of the public especially when navigating roads or operating in public domains is paramount. The conventional approaches to accident identification include observation or reporting from witnesses and always record slow and imprecise outcomes. With the improvement of AI and computer visions, especially with deep learning models such as YOLO, accident detection is changing. YOLO v10 which is incorporated in the surveillance systems, performs real time video analysis to provide object and pattern recognition of accidents including car accidents and incidents involving the pedestrians. When applied to the initial set of annotated accident images, the fine-tuning of the YOLO v10 model enhances its detection capability. The system is in watching the video frames that contain aberrations and issues and alarms are issued when the accidents happen and relayed to the monitoring stations or emergency departments for proper response. The optimized YOLOv10 here delivers a meaningful testing score of 72.3% mAP to outperform the regular YOLOv10 efficiency in incident detection. © 2025 IEEE.

关键词： Machine vision

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Research on Steganography Course with Large Language Model ChatGPT Assisted Learning 24

Research on Steganography Course with Large Language Model C...

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3rd International Conference on Artificial Intelligence and Education, ICAIE 2024

作者： Hwang, Min-Shiang Fatima, Kanza Chan, Chi-Shiang Wu, Chia-Chun Dept. of C.S. and Info. Eng. Asia University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798400712692

In this study, by integrating practice-oriented teaching methods and combining large language models to assist learning and teaching situations, it is proposed that the Steganography course is practical-oriented and integrates ChatGPT with the Steganography language model teaching and teaching established by the applicant. In addition to focusing In addition to cultivating students with Steganography concepts and practical operational abilities, we hope to cultivate students' abilities of active observation, independent research, and critical thinking so that students can meet the technology industry's professional skills and requirements for Steganography studies. This research explores the pre-test and post-test, of course, students' use of large language models to learn to have higher learning attitudes and achievements towards Steganography theory and practice. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Steganography

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FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradients

arXiv

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arXiv 2025年

作者： Leng, Jiaqi Ju, Yakun Duan, Yuanxu Zhang, Jiangnan Lv, Qingxuan Wu, Zuxuan Fan, Hao Haide College Ocean University of China China Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China School of Computing and Mathematic Sciences University of Leicester United Kingdom Department of Computer Engineering Qingdao City University China College of Computer Science and Technology Ocean University of China China

Surface-from-gradients (SfG) aims to recover a three-dimensional (3D) surface from its gradients. Traditional methods encounter significant challenges in achieving high accuracy and handling high-resolution inputs, particularly facing the complex nature of discontinuities and the inefficiencies associated with large-scale linear solvers. Although recent advances in deep learning, such as photometric stereo, have enhanced normal estimation accuracy, they do not fully address the intricacies of gradient-based surface reconstruction. To overcome these limitations, we propose a Fourier neural operator-based Numerical Integration Network (FNIN) within a two-stage optimization framework. In the first stage, our approach employs an iterative architecture for numerical integration, harnessing an advanced Fourier neural operator to approximate the solution operator in Fourier space. Additionally, a self-learning attention mechanism is incorporated to effectively detect and handle discontinuities. In the second stage, we refine the surface reconstruction by formulating a weighted least squares problem, addressing the identified discontinuities rationally. Extensive experiments demonstrate that our method achieves significant improvements in both accuracy and efficiency compared to current state-of-the-art solvers. This is particularly evident in handling high-resolution images with complex data, achieving errors of fewer than 0.1 mm on tested objects. Copyright © 2025, The Authors. All rights reserved.

关键词： Surface reconstruction

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AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques

AADS: An Automated Accident Detection and Nighttime Surveill...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： M. Karthikeyan Pravin Ramdas Kshirsagar Tan Kuan Tak Keshav Lalit Kamal Upreti Rituraj Jain Dept. of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Electronics Engg. J.D College of Engg & Mgmt. Nagpur MS India Engineering Cluster Singapore Institute of Technology Singapore Dept. of Computer Science & Engineering Manipal Institute of Technology Manipal Karnataka India Department of Computer Science Christ University Delhi NCR Ghaziabad India Department of Info. Tech. Marwadi University Rajkot Gujarat India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

关键词： YOLO Deep learning computer vision Surveillance Computational modeling Streaming media Cameras Real-time systems Time factors Accidents

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An enhancement of JPEG still image compression with adaptive linear regression and golomb-rice coding

An enhancement of JPEG still image compression with adaptive...

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2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009

作者： Chang, Chin-Chen Lai, Yeu-Pong Dept. of Info. Engineering and Computer Science Feng Chia University Taichung Taiwan Dept. of Computer Science and Info. Engineering National Chung Cheng University Giayi Taiwan

ISBN: (纸本)9780769537450

The JPEG compression method is one of the most popular compression schemes. There are many hardware and software systems based on the JPEG compression standard. This is an indication that the format is still very important before these systems become outdate. This paper presents an adaptive regression method applied to the standard JPEG compression for archiving higher compression ratios. Besides, an interesting coding scheme, Golomb-Rice coding, is introduced also for improve the JPEG standard. Since the coding scheme is not based on frequency analysis from certain of images to gain a codebook, the decoded images that are encoded with this scheme are assured in average quality. The standard JPEG compression scheme can import these two for higher compression rates or wider application fields. © 2009 IEEE.

关键词： Image enhancement

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Using reinfo.cement learning to the priority-based routing and call admission control in WDM networks

Using reinforcement learning to the priority-based routing a...

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International Multi-Conference on Computing in The Global info.mation Technology

作者： Chang, Ching-Lung Kang, Siao-Ji Department of Computer Science and Info. Engineering National Yunlin University of Science and Technology Taiwan

ISBN: (纸本)9780769541815

Using reinfo.cement learning (RL), this paper deals with the problem of call admission control (CAC) and routing in differentiating the services of Wavelength Division Multiplexing (WDM) networks to obtain maximized system revenue. The problem is formulated as a finite-state discrete-time dynamic programming problem. Here we adopt the RL method together with a decomposition approach, to solve this problem that is too complex to be solved exactly and demonstrate that it is able to earn significantly higher revenue than the alternatives. © 2010 IEEE.

关键词： Reinfo.cement learning

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Design of flexible PID-plus Bang-Bang controller with neural network predictive model

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IEICE TRANSACTIONS ON info.MATION AND SYSTEMS 1996年第4期E79D卷 357-362页

作者： Jung, SH Cho, KH Kim, TG Park, KH Lim, JT Faculty of Electrical Engineering KAIST Taejon 305-701 South Korea Fac. of Info. and Computer Science Hansung Univ Seoul 136-792 South Korea

PID-type controllers have been well-known and widely used in many industries. Their regulation property of those was more improved through the addition of Bang-Bang-action. In spite of the potentials of these PID-plus Bang-Bang controllers, their regulation property is still limited by the fixed window limit value that determines the control action, i.e., PID or Bang-Bang. Thus, this paper presents an approach for improving the regulation property by dynamically changing the window limit value according to the plant dynamics with Neural Network predictive model. The improved regulation property is illustrated through simulation studies for position control of DC servo-motor system in the sense of classical figures of merit such as overshoot and rise time.

关键词： PID-plus Bang-Bang control active window neural network predictive model

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