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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Yuming Gao, Xiangxiang Zhu, Wei Wang, Xiaoling College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shanghai Jiaotong University Shanghai China East China Normal University Shanghai China

ISBN: (纸本)9783031466601

BERT and its variants are the most performing models for named entity recognition (NER), a fundamental information extraction task. We must apply inference speedup methods for BERT-based NER models to be deployed in the industrial setting. Early exiting allows the model to use only the shallow layers to process easy samples, thus reducing the average latency. In this work, we introduce FastNER, a novel framework for early exiting with a BERT biaffine NER model, which supports both flat NER tasks and nested NER tasks. First, we introduce a convolutional bypass module to provide suitable features for the current layer’s biaffine prediction head. This way, an intermediate layer can focus more on delivering high-quality semantic representations for the next layer. Second, we introduce a series of early exiting mechanisms for BERT biaffine model, which is the first in the literature. We conduct extensive experiments on 6 benchmark NER datasets, 3 of which are nested NER tasks. The experiments show that: (a) Our proposed convolutional bypass method can significantly improve the overall performances of the multi-exit BERT biaffine NER model. (b) our proposed early exiting mechanisms can effectively speed up the inference of BERT biaffine model. Comprehensive ablation studies are conducted and demonstrate the validity of our design for our FastNER framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantics

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Hybrid GrabCut Hidden Markov Model for Segmentation

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computers, Materials & Continua 2022年第7期72卷 851-869页

作者： Soobia Saeed Afnizanfaizal Abdullah N.Z.Jhanjhi Mehmood Naqvi Mehedi Masud Mohammed A.AlZain Department of Software Engineering UniversitiTeknologi Malaysia-UTM81310Malaysia School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of Engineering and Technology Mohak CollegeAlbertaCanada Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid(CSF)leaks in the *** aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging(MRI)images and another problem also relates to efficiency and less execution time for segmentation of medical *** tumor and CSF segmentation using trained light field database(LFD)datasets of MRI *** research proposed the new framework of the hybrid k-Nearest Neighbors(k-NN)model that is a combination of hybridization of Graph Cut and Support Vector Machine(GCSVM)and Hidden Markov Model of k-Mean Clustering Algorithm(HMMkC).There are four different methods are used in this research namely(1)SVM,(2)GrabCut segmentation,(3)HMM,and(4)k-mean clustering *** this framework,on the one hand,phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin *** research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features *** the other hand,in phase two,segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode(ICMM)with identifying the range of *** evaluation is also performing by the comparison of existing techniques in this *** conclusion,our proposed model gives better results than *** proposed model helps to common man and doctor that can identify their condition of brain *** future,this will model will use for other brain related diseases.

关键词： SVM tumor CSF k-NN MRI grabcut segmentation HMM

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Improved Time-Saving Face Detection in Video 9

Improved Time-Saving Face Detection in Video

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9th International engineering Conference on Sustainable Technology and Development, IEC 2023

作者： Qadir, Jihad Anwar Jalal, Shayan Ihsan Jameel, Samer Kais Al-Talabani, Abdulbasit University of Raparin College of Basic Education Department of Computer Science Kurdistan Region Rania Iraq Koya University Faculty of Engineering Department of Software Engineering Kurdistan Region Koya Iraq

ISBN: (纸本)9798350335064

Human face detection from video sequences is a difficult problem in computer vision. Face detection is the process of determining the location of a face or faces in each frame of a video. Face detection in real-time videos is an ongoing under-investigated research problem. This paper proposes a new method for face detection in videos that significantly reduces the time required to detect faces. The method targets a Search Area Boundary (SAB) in the next frames that are determined based on the position of the detected faces in the current frame. The model will look for the face in a box that is slightly larger than the SAB from the previous frame. This means that it will not search the entire video frame for the face, so it will take less time to find the face. After a few frames, the method will begin searching the entire frame for new faces. The results show that the proposed method can save up to 73.5% of the time, especially when the SAB is small, with an accuracy degradation of no more than 7% in the best case. © 2023 IEEE.

关键词： Face recognition

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CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

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computers, Materials & Continua 2022年第12期73卷 4657-4675页

作者： Adnan Ahmed Rafique Yazeed Yasin Ghadi Suliman AAlsuhibany Samia Allaoua Chelloug Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Latest advancements in vision technology offer an evident impact on multi-object recognition and scene *** sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist *** visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene *** this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an ***,after acquisition and preprocessing,the image is segmented by using ***,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are *** the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion ***,to select theminimal set of features,genetic algorithm-based feature selection is *** order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is *** objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation ***,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the *** experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance.

关键词： Convolutional neural network decision tree feature fusion neurofuzzy system

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Bi-Orthogonal Projection Learning for Dimensionality Reduction 3

Bi-Orthogonal Projection Learning for Dimensionality Reducti...

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3rd International Conference on Intelligent Power and Systems, ICIPS 2023

作者： Liang, Yingyi Fan, Junjun Li, Chunsheng Wen, Jiajun Guangdong University of Technology Guangzhou China Shenzhen University College of Computer Science & Software Engineering Shenzhen China Laboratory for Artificial Intelligence in Design Hong Kong

ISBN: (纸本)9798350328691

This paper proposes a novel bi-orthogonal projection learning (BOPL) for dimensionality reduction (DR) methods, which further extends the existing DR to a more flexible, robust, and sparse embedding framework. Unlike conventional methods that use only one kind of projection for learning in DR, the proposed BOPL introduces two kinds of projection, which are orthogonal and provides the true projection the freedom for more accurate data transformation in subspace learning. Inspired by the observation that the two projections share many similar data structures, the projections are expected to preserve the similarity structure of data by using two different reconstruction ways. The proposed method can also handle data corrupted by noises since a sparse item is employed to compensate for the noises during DR. Several novel unsupervised DR methods (i.e., BOPL_PCA, BOPL_NPE, and BOPL_LPP) are derived from the proposed framework. The results of experiments on the natural and synthetic data sets demonstrate that the proposed methods outperform the existing well-established DR methods. ©2023 IEEE.

关键词： Metadata

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YOLOv5 for Automatic License Plate Recognition in Smart Cities 4th

YOLOv5 for Automatic License Plate Recognition in Smart Citi...

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4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023

作者： Raza, Abir Badidi, Elarbi Badidi, Basma Al Zahmi, Sarah Department of Computer Science and Software Engineering College of Information Technology UAE University P.O. Box 15551 Al-Ain United Arab Emirates

ISBN: (纸本)9783031423161

Recognition and extraction of license plate information from still images or videos are the basis of modern traffic and security systems. Automatic License Plate Recognition (ALPR) is transforming public safety and transportation in many ways. Such license plate recognition systems enable advanced solutions for toll roads, offer significant operational cost savings through automation, and even open up new market opportunities, such as license plate readers mounted on police vehicles. In this work, we implement license plate recognition based on convolutional neural networks to achieve high accuracy. You Only Look Once (YOLO) algorithm is employed for detection, while, an Optical Character Recognition (OCR) technique is used for text recognition. We use four variants of the YOLOv5 for license plate detection and the EasyOCR for license plate recognition. The results show that YOLOv5x (extra-large) achieves a Mean Average Precision (mAP) of nearly 82% in detecting vehicles and license plates in an image. Furthermore, the recognition of the letters on license plates is achieved with a confidence score of nearly 65%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Domain-invariant Progressive Knowledge Distillation for UAV-based Object Detection

arXiv

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arXiv 2024年

作者： Yao, Liang Liu, Fan Zhang, Chuanyi Ou, Zhiquan Wu, Ting The College of Computer Science and Software Engineering Hohai University Nanjing210098 China

Knowledge distillation (KD) is an effective method for compressing models in object detection tasks. Due to limited computational capability, UAV-based object detection (UAV-OD) widely adopt the KD technique to obtain lightweight detectors. Existing methods often overlook the significant differences in feature space caused by the large gap in scale between the teacher and student models. This limitation hampers the efficiency of knowledge transfer during the distillation process. Furthermore, the complex backgrounds in UAV images make it challenging for the student model to efficiently learn the object features. In this paper, we propose a novel knowledge distillation framework for UAV-OD. Specifically, a progressive distillation approach is designed to alleviate the feature gap between teacher and student models. Then a new feature alignment method is provided to extract object-related features for enhancing student model’s knowledge reception efficiency. Finally, extensive experiments are conducted to validate the effectiveness of our proposed approach. The results demonstrate that our proposed method achieves state-of-the-art (SoTA) performance in two UAV-OD datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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A Self-play and Sentiment-Emphasized Comment Integration Framework Based on Deep Q-Learning in a Crowdsourcing Scenario : Extended Abstract 39

A Self-play and Sentiment-Emphasized Comment Integration Fra...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Rong, Huan Sheng, Victor S. Ma, Tinghuai Zhou, Yang Al-Rodhaan, Mznah Nanjing University of Information Science & Technology School of Artificial Intelligence Nanjing China Texas Tech University Department of Computer Science Lubbock United States Nanjing University of Information Science & Technology School of Computer & Software Nanjing China Auburn University Department of Computer Science & Software Engineering Auburn United States King Saud University College of Computer & Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350322279

Crowdsourcing is a sourcing model where individuals or organizations obtain goods and services from a large, relatively open and often rapidly evolving group of internet users. The most common way that crowdsourcing can facilitate machine learning is to annotate instances with labels [1]. However, the same instance may have inconsistent class labels, in the eyes of various annotators. Therefore, current efforts in crowdsourcing mainly focus on the truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, instances with the integrated labels could facilitate the training on machine learning models. The future direction of crowdsourcing is to apply more fine-grained truth inference methods to different application domains [2]. Consequently, we evolve toward another challenging problem of comment integration. That is, how can we integrate or summarize the core opinions of multiple product comments obtained from users, rather than the discrete labels. © 2023 IEEE.

关键词： Crowdsourcing

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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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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

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

ISBN: (纸本)9798350378092

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. © 2024 IEEE.

关键词： Cyber attacks

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Time Series Fusion Model for Predicting Electric power tools and equipment 6

Time Series Fusion Model for Predicting Electric power tools...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Xue, Liang Ling, Xinghong Yang, Lei Yang, Yang Computing Science and Artificial Intelligence College Suzhou City University Suzhou China College of Computer Science and Software Engineering Hohai University Nanjing China Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare Suzhou City University Suzhou China

ISBN: (纸本)9798331541699

To effectively address the issue of inadequate accuracy and timeliness in industrial data classification, this paper proposes a fusion classification model based on time series (DT-RF-MLP) for loss prediction of electrical work equipment. Firstly, data information is extracted from the dataset and features are constructed based on the data information to enhance the diversity of data features. Secondly, redundant and highly correlated features are removed by quantifying the correlation degree between the features to simplify the model inputs. On this basis, a fusion classification model is constructed and comparative experiments are conducted with a single model to validate the advantages of the fusion model in terms of prediction performance. The experimental results demonstrate that the model markedly enhances prediction accuracy while concurrently reducing the duration of the model run. © 2024 IEEE.

关键词： Prediction models

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