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2nd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2023

作者： Abdullah, Abdulkareem Al-Ashwal, Abdulwahab Debsi, Ali Kaf, Amira Abo Almadwami, Hashem School of Automation Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Mechatronic Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350371451

In recent years, visual Simultaneous Localization and Mapping (SLAM) systems, particularly the Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM2), have shown significant promise in robotic navigation in indoor environments. However, previous implementations often struggled with real-time processing and environmental dynamicity, limiting their practical deployment. This study evaluates the performance of ORB-SLAM2 using different sequences of the Tum RGBD dataset to construct 3D maps in real-time with an RGB-D camera (Kinectv2). The study findings indicate that ORB-SLAM2 facilitates effective real-time autonomous navigation using Turtlebot2, although previous related studies have demonstrated limitations in real-time processing capabilities and adaptive object recognition. To address these challenges, the deployment of a real-time object detection system using YOLO, a state-of-the-art one stage object detector, and a convolution neural network that surpasses other real-time object detectors in speed and accuracy. In this process, we first built our own dataset by labeling images, and then trained a YOLOv4 model to detect cracks. We tested the model in several ways, including real-time crack detection using an RGB-D camera (Kinectv2). The results were efficient, demonstrating the model's success in accurately identifying the object and predicting its location. © 2023 IEEE.

关键词： Crack detection

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Neural Networks-Based Predictive Models for Self-Healing in Cloud Computing Environments 2

Neural Networks-Based Predictive Models for Self-Healing in ...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sujatha, D. Michael Raj, T.F. Ramesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. St.Peters College of Engineering and Technology Department of Computer Science and Engineering Avadi Chennai India Sclas Smats Department of Computer Applications TamilNadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Easwari Engineering College Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798350306415

Due to the widespread adoption of cloud computing, protecting the security and uptime of cloud services is of crucial importance. Unforeseen malfunctions and performance bottlenecks in cloud settings can lead to considerable downtime and financial losses. Systems that can automatically detect, diagnose, and recover from errors using self-healing mechanisms are a promising new approach. This study investigates how neural network-based prediction models can be used to improve cloud-based systems' inherent capacity for self-healing. This research introduces a novel method of using neural networks to forecast cloud system failures and performance irregularities. The proposed prediction models are able to spot warning signs by assessing past data and current metrics in real time. Neural networks may accurately anticipate outcomes by recognizing complicated patterns and learning from large, varied information. Various forms of neural networks, including deep learning designs, are evaluated to determine their efficacy in diverse contexts. The study also looks into proactively reducing faults by incorporating these predictive models into self-healing systems. Responses, such as resource reallocation, workload migration, and backup procedures, are triggered automatically when problems are predicted. This preventative method drastically lessens the amount of time users spend waiting for their cloud-based systems to restart. Extensive simulations and real-world case studies yielded experimental data that prove the prediction models based on neural networks proposed for self-healing in cloud computing are effective. The models' proficiency in failure prediction enables corrective measures to be taken promptly and precisely. Implementing these predictive models in massive cloud environments is also discussed, as are their scalability, flexibility, and real-world consequences. by tapping into the potential of neural networks for predictive analysis, this study adds to the develop

关键词： Cloud Computing LSTM Neural Network Self-Healing

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Target Distance Estimation Model Based on Deformable Convolution and Attention Mechanism

Target Distance Estimation Model Based on Deformable Convolu...

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2023 IEEE International Conference on Electrical, Automation and computer engineering, ICEACE 2023

作者： Lin, Tao Shi, Yu Zhang, Yabo Jiang, Zeping Yi, Shiming Wang, Rong School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350309614

Environment perception tasks, including target detection, distance estimation, and other charges, are essential components of autonomous driving systems or automation systems. Among them, target distance value estimation is used to achieve tasks such as collision avoidance in automated systems. Although the target distance estimation task is indispensable, few related studies exist. This paper proposes a model combined with a deformable convolution kernel attention mechanism for monocular ranging tasks. Compared with previous distance estimation deep learning models, introducing the attention mechanism can better grasp the relationship between the target and the global situation. At the same time, deformable convolution can better capture the characteristics of different targets and reduce the impact of the background on the results. Experiments have proven that distance estimation and other models based on attention mechanisms perform better. Based on the basic model, we introduce a multi-task tool and conduct joint training through joint pixel-level tasks, which can better combine the global information of the target and allow the model to obtain better expressive capabilities. Through ablation experiment comparison, we can find that multi-task joint is after training, and the performance of the basic model can be significantly improved. © 2023 IEEE.

关键词： attention mechanism convolutional neural network dense prediction depth estimation image segmentation Monocular ranging multi-task model

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Human Novel View Synthesis from Monocular RGB Video 8

Human Novel View Synthesis from Monocular RGB Video

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8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Li, Jinghua Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350369564

The novel view synthesis of human bodies is an important step forward for virtual reality. In particular, it is able to predict the invisible domain of moving human bodies, achieving robust and stable reconstruction performance. We propose a novel view synthesis method for human body based on monocular RGB video, while rendering a 360° novel view, avoiding expensive sensor equipment. We integrated the observation space and the canonical space. On one hand, the model could be better optimized and controlled by decomposing geometric human posture and appearance;on the other hand, the rigid and non-rigid motion could be further decoupled to facilitate better optimization approximation. At the same time, we proposed a compact feature description, which could extract the representation to the maximum to achieve better results. We also present qualitative and quantitative results on the performance of the ZJU-MoCap dataset. © 2023 IEEE.

关键词： Volume rendering

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A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

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computers, Materials & Continua 2024年第11期81卷 2873-2894页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Naveed Ahmed Mohammed Ali Alshara School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia

Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption ***,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these ***,this leads to enhanced captioning network *** light of this,we present an image captioning framework that efficiently exploits the extracted representations of the *** framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language *** VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features ***,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative *** the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s *** the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve *** implementation code can be found here:https://***/althobhani/VFDICM(accessed on 30 July 2024).

关键词： Visual attention image captioning visual feature detector visual feature visual attention

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COCOs Nucifera Tree Disease Prediction Using Faster Region-Convolutional Neural Network with ResNet-50 3

COCOs Nucifera Tree Disease Prediction Using Faster Region-C...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kavitha, M.G. Raviprakash, M.L. Alsalami, Zaid Nagendar, Y. Ramachandra, A.C. Kalpataru Institute Of Technology Department Of Computer Science And Engineering Tiptur India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Computer Science And Engineering Sr University Warangal India Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India

ISBN: (纸本)9798350318609

Coconut diseases were predicted based on various categories such as color, shape, time frame, and changes in growth. Deep learning (DL) was applied for this purpose. However, the image dataset used was unlabelled, leading to challenges such as overfitting and complexity in computational time for prediction. The proposed method is the Faster Region-Convolutional Neural Network (R-CNN) model for detecting and classifying coconut clusters, aiming to minimize overfitting in the identification of coconut nucifera diseases. The coconut disease dataset involved performing image classification, distinguishing between healthy and unhealthy coconuts. Normalization, feature extraction, and classification techniques were utilized for this task. For feature extraction, the VGG-16 model was involved, which includes maximum pooling layers to extract features at different levels of the network enhancing the quality of feature maps and providing better region identification. Classification was performed using the R-CNN with ResNet-50. The dataset contained images of both healthy and unhealthy coconuts, and the regions were predicted to identify and classify the different diseases within the images. ResNet-50 captures high-level features and helps mitigate the vanishing gradient problem during training, making it easier to connect with the R-CNN method. The proposed Faster R-CNN with ResNet-50 achieved a performance accuracy of 98.40%. This performance was compared to existing methods such as Convolutional Neural Network (CNN), Deep-CNN (DCNN) and Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Long short-term memory

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Adaptive Backward-link Resource Allocation in Beam Hopping Communication System 3

Adaptive Backward-link Resource Allocation in Beam Hopping C...

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3rd International Conference on Electronic Information engineering and computer Communication, EIECC 2023

作者： Yi, Yi Huang, Zixuan School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350359961

Satellite communication plays a crucial role in the world due to its global coverage, flexibility, and reliability. As one of the key technologies, beam hopping technology can effectively improve the available capacity of the system and enhance the flexibility of satellite resource scheduling. However, most of the research on resource allocation technology for beam hopping satellites primarily focused on the theoretical level which presents challenges such as high algorithm complexity and low real-Time performance. Therefore, further research is necessary to explore practical applications. In this paper, based on the actual satellite communication scenario, the model of the bouncing beam satellite communication system is analyzed first. Then, based on the DVBRCS2 protocol standard, we propose a backward-link scheduling algorithm that supports channel state adaptation. Finally, two calculation schemes of resource allocation are designed to match different traffic requirements. Simulation results show that compared with the traditional algorithm, the proposed method can meet the system capacity with lower algorithm complexity. Moreover, it successfully meets various service requirements and holds significant engineering implications. © 2023 IEEE.

关键词： beam-hopping communication satelite DVB-RCS2 Resource allocation

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Glaucoma Classification using Light Vision Transformer

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EAI Endorsed Transactions on Pervasive Health and technology 2023年 9卷

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Tiwari, Anil Batra, Devanshu Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Information Technology Pranveer Singh Institute of Technology Kanpur India

INTRODUCTION: Nowadays one of the primary causes of permanent blindness is glaucoma. Due to the trade-offs, it makes in terms of portability, size, and cost, fundus imaging is the most widely used glaucoma screening technique. OBJECTIVES:To boost accuracy,focusing on less execution time, and less resources consumption, we have proposed a vision transformer-based model with data pre-processing techniques which fix classification problems. METHODS: Convolution is a "local" technique used by CNNs that is restricted to a limited area around an image. Self-attention, used by Vision Transformers, is a "global" action since it gathers data from the whole image. This makes it possible for the ViT to successfully collect far-off semantic relevance in an image. Several optimizers, including Adamax, SGD, RMSprop, Adadelta, Adafactor, Nadam, and Adagrad, were studied in this paper. We have trained and tested the Vision Transformer model on the IEEE Fundus image dataset having 1750 Healthy and Glaucoma images. Additionally, the dataset was preprocessed using image resizing, auto-rotation, and auto-adjust contrast by adaptive equalization. RESULTS: Results also show that the Nadam Optimizer increased accuracy up to 97% in adaptive equalized preprocessing dataset followed by auto rotate and image resizing operations. CONCLUSION: The experimental findings shows that transformer based classification spurred a revolution in computer vision with reduced time in training and classification. © 2023 Singh et al.

关键词： Semantics

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Learning Simultaneous Facial Canonical Correlation Representation for Face Hallucination

Learning Simultaneous Facial Canonical Correlation Represent...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yuan, Yun-Hao Li, Jin Qiang, Jipeng Zhu, Yi Shen, Xiaobo Li, Yun School of Information Engineering Yangzhou University Yangzhou China School of Computer Science Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350368741

The low resolution (LR) problem is rather challenging in face analysis. Most existing face hallucination methods assume that LR face images have only one resolution, but multiple resolutions may be available from different sources. To solve this issue, we propose a novel simultaneous facial canonical correlation representation learning method for face hallucination, which seeks latent correlation subspaces for multi-resolution views. Our method jointly solves multiple linear transformations by optimizing a correlation summation criterion of all pairs of resolutions. The neighborhood reconstruction is used to infer the HR facial canonical correlation representation of LR face inputs. Extensive experimental results show the superiority of our proposed method in terms of quantitative and qualitative evaluations. © 2025 IEEE.

关键词： canonical correlation analysis face hallucination latent representation multiple facial resolutions

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Improved Contextual Understanding and Emotion Detection in Large-Scale Text Data with Hybrid Deep Learning Models 5

Improved Contextual Understanding and Emotion Detection in L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Thilakavathy, P. Manikandan, G. Deepa, R. Jayalakshmi, V. Surendran, R. Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of science and technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the growth of large-scale text datasets from reviews, social media, and other online sources, sentiment analysis is essential for understanding public opinion. Traditional models struggle to understand complicated linguistic sentiments and interdependencies. This research could improve sentiment classification in customer feedback analysis, market sentiment predictions, and social media monitoring. Addressed issues include ambiguous language, sentence context flipping, and the need for robust models that can manage massive datasets. Contextual Detection Text Analysis Using Deep Learning (CDTA-DL) improves contextual knowledge and emotion identification in this research. RNNs and CNNs are used in the CDTA-DL approach to extract spatial and sequential information from text. CDTA-DL helps large-scale sentiment analysis detect sarcasm, implicit emotions, and many expressions. In a comprehensive simulation investigation employing open-access sentiment analysis datasets, CDTA-DL surpassed standard deep learning models in accuracy, recall, and F1 scores. The recommended strategy may increase emotion recognition and contextual understanding, making it ideal for large-scale sentiment analysis in various domains. © 2024 IEEE.

关键词： Contrastive Learning

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