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A Lightweight Chinese Summarization Generation Method Based on Knowledge Distillation

A Lightweight Chinese Summarization Generation Method Based ...

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Information Science, Parallel and Distributed Systems (ISPDS), international conference on

作者： Shiyue Liu Yannan Xiao Xueyong Wang Wenming Huang Zhenrong Deng Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer and Information Security Guilin University of Electronic Technology Guilin China School of Computer and Information Security Guilin University of Electronic Technology Guilin China Guilin Xintong Technology Co. Guilin China

In recent years, large-scale pretrained natural language processing models such as BERT, GPT3 have achieved good results in processing tasks. However, in daily applications, these large-scale language models usually exist large model size or long running time, and the model portability and application are not very convenient. To settle this problem, we come up with a lightweight summarization generation method based on knowledge distillation (LW-BERT-KD, abbreviated as LBK model) to prove that complex neural network knowledge (taking BERT model as an example) can be extracted to lightweight language processing models (taking BiLSTM model as an example), thus achieving the purpose of complex knowledge extraction and model compression. In this paper, the BERT model is regarded as the teacher model, and the BiLSTM is regarded as the student model. The knowledge distillation technology is the transfer of knowledge from teacher models to student models, so that the generated model is effective, lightweight, and easy to port. On the Chinese abstract generation dataset, the training effect of this model is significantly better than the basic Transformer baseline. The experimental results show that if the training parameters are increased by 100 times, the model achieves comparable results with complex preprocessing language models.

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An Effective Clustering Using Moth Flame Optimization for Content-Based image Retrieval

An Effective Clustering Using Moth Flame Optimization for Co...

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Mobile Networks and Wireless Communications (ICMNWC), ieee international conference on

作者： Srinivas Aluvala Zainab Abed Almoussawi Muntather Almusawi Zamen Latef Naser S. Meenakshi Sundaram School of Computer Science & Artificial Intelligence SR University Warangal Telangana India College of Islamic Science Ahl Al Bayt University karbala Iraq The Islamic university Najaf Iraq National University of Science and Technology Dhi Qar Iraq Department of AI and DS Nitte Meenakshi Institute of Technology Bengaluru India

Content-based image retrieval plays an important role in many domains and the volume of the image database increasing tremendously. It is very complex to comparing the query image feature with all images in the dataset during the retrieval phase. Due to computational complexity increase which degrades the performance of recognition accuracy. The proposed model Clustering using Moth Flame Optimizer are pre-processing, feature extraction, feature fusion, clustering and classification. The corel lk dataset is utilized in this research for effective classification of CBIR method. Filter using First Average Peer Group (F APG) is used to remove the noise in the pre-processing stage. Feature extracted using Local Binary Pattern (LBP) and Hue Saturation Value (HSV) based feature extraction techniques. All these two features are fused into single feature using average and weighted technique. Next, proposed model clustering using Moth Flame Optimization algorithm, it enhance the flames and flame value is obtained in ***, the relevant image is retrieved using SAR classifier which utilized Deep Learning (DL) for effective classification. The obtained result shows the proposed clustering using MFO model achieve better accuracy of 99.87% on Corel lk dataset which insure accurate classification compared to other existing methods like Bi-Iayer method and Texture from semantic pyramid.

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A Novel Design and Development model for people counting in a Closed Environment with Machine Learning Approach

A Novel Design and Development model for people counting in ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Praveen Gupta Umashankar Ghugar Chinta Vishnu Vardhan Rakesh Nayak Chaur Singh Rajpoot Department of Computer Science and Engineering GITAM School of Technology GITAM (Deemed to be University) Visakhapatnam India Department of Computer Science and Engineering OP Jindal University Raigarh CG Parul Institute of Technology Parul University Vadodara Gujarat India

image processing can be used to solve some daily applications. computer vision and Artificial Intelligence in the computer system provide the capability to work like the human mind. In the last decade, computer programming is moving towards processing images and finding useful results and analysis. This experiment is an effort to count the number of people in a closed environment. there are many situations when it is required to count the persons in the room. An application can do this work with accuracy and in less time. this can be enhanced to another area where it can count some other objects also. In this experiment efforts have ben done for the identification of people and count them correctly. It has not been tested on large numbers yet. You only look once (YOLO) real time object detection technique is used with some more microenvironments. this can be used for real-life situations and for the safety of any objects. This paper is based on YOLO. The experiments have many benefits and applications in all kinds of industries. in the future, it may be extended to videos.

关键词： Industries computer vision image processing Object detection Machine learning Programming Real-time systems

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Philippine Coin Detection System Using Mask R-CNN Algorithm

Philippine Coin Detection System Using Mask R-CNN Algorithm

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international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Isagani V. Villamor Mary Lee D. Apelo David Angelo C. Ascan School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

This paper introduces a Philippine Coin Detection System utilizing the Mask R-CNN Algorithm for accurate classification and denomination of 1, 5, 10, and 20 peso coins. image preprocessing techniques, such as Gaussian smoothing and Canny edge detection, are applied prior to classification. With a 70 % accuracy for multiple coin detection, the evaluation reveals specific coin accuracies: 1-peso (90%), 5-peso (87.5%), 10-peso (85 %), and 20-peso (92.5 %), with an overall accuracy and precision of 77.5 % and 78.33 % respectively. The study signifies a significant advancement in automated coin recognition systems with potential implications for financial automation and related applications.

关键词： Accuracy Smoothing methods Automation image edge detection Humanoid robots Classification algorithms image preprocessing

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Research on Feature-based image Recognition based on Convolutional Neural Network

Research on Feature-based Image Recognition based on Convolu...

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computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), ieee international conference on

作者： Qi Ma Zeyu Yang Qingyao Zhang Yejia Yang Haibin Ran Yuhao Bian College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Recently, image identification has achieved promising performance and become the most essential component in real-life image-based applications through training a machine learning model. However, existing methods extract the features and the identification accuracy is almost depended on these features with extractions process, which is the fundamental process for learning models. Existing methods are almost concentrated on the utilization of these feature and ignore the process of extracting procedure. As far as we can concern, the image features contain numerous information and can assist the learning model to enhance the identification accuracy. In this work, we initially utilize a convolutional neural network to obtain the features in the feature space. Subsequently, an attention mechanism and the feature clustering are applying to optimize the extracted features. Finally, we evaluate our proposed model with most used methods in the identical environment setups. From our extensive experimental results, we can observe that our model outperforms compared methods with reasonable computation costs.

关键词： Training Adaptation models Computational modeling Scalability Transfer learning Process control Feature extraction

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EDRNet: A Lightweight Underwater Sonar Target Detection Model Based on an Efficient De-Redundancy Network

EDRNet: A Lightweight Underwater Sonar Target Detection Mode...

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ieee Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Qiuqin Fang Jianxun Tang Zhe Chen Mingsong Chen School of Information and Communication Guilin University of Electronic Technology Guilin China School of Ocean Engineering Guilin University of Electronic Technology Guilin China Cognitive Radio and Information Processing Key Laboratory Authorized by China's Ministry of Education Foundation Guilin University of Electronic Technology

Underwater object detection is one of the hot research topics in the field of acoustic signal processing. Using sonar to automatically detect seabed objects has significant importance for marine resource development and underwater military training. Traditional sonar image detection methods based on machine learning and clustering have problems such as large model parameters, redundant computation, low detection accuracy, and difficult deployment in practical engineering applications. To address these issues, this paper proposes a lightweight underwater sonar object detection model named EDRNet. First, it introduces the lightweight network EfficientFormerV2, which solves the problems of large model parameters and redundant computation. Second, it introduces the SimSPPF module, which enlarges the receptive field of the network. Finally, it adds the parameter-free attention mechanism SimAM, which enhances feature extraction and improves model computation efficiency and detection accuracy. Experiments on the SeabedObjects-KLSG dataset and the STCD dataset show that the proposed model achieves an average detection accuracy of 94.7%, which is 22.8%, 1.8%, 2.3%, and 0.4% higher than the existing mainstream models Faster R-CNN, YOLOv3, YOLOv4, and YOLOv5, respectively. The parameter size is 58.6% of YOLOv5s, and the computation amount is 51.25% of YOLOv5s. This verifies that the proposed model effectively reduces the model size while maintaining performance and has stronger practical significance for underwater sonar image detection.

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Gastrointestinal Endoscopic image Classification using a Novel Wavelet Decomposition Based Deep Learning Algorithm

Gastrointestinal Endoscopic Image Classification using a Nov...

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ieee international conference on Electro-Information Technology

作者： Ankita Sethi Shivam Damani Arshia K. Sethi Anjali Rajagopal Keerthy Gopalakrishnan Akhila Sai Sree Cherukuri Shivaram P. Arunachalam GIH Artificial Intelligence Laboratory (GAIL) Division of Gastroenterology and Hepatology Mayo Clinic Rochester MN USA Department of Medicine Mayo Clinic Rochester MN USA Department of Medicine Division of Gastroenterology and Hepatology Mayo Clinic Rochester MN USA Department of Radiology Mayo Clinic Rochester MN USA

More than 11% of Americans are affected by diseases related to the gastrointestinal (GI) tract. GI endoscopy is an established imaging modality for diagnostic and therapeutic procedures. Large volumes of images and videos generated during this procedure, makes image interpretation cumbersome and varies among physicians. Artificial intelligence (AI) assisted computer-Aided Diagnosis (CAD) system for digital GI endoscopy is gaining attention that can disrupt GI practice. Several studies have reported the application of computer vision and machine learning algorithms in GI endoscopy. Endoscopic images of varying anatomic features of the Gi tract, challenges their accurate classification. Therefore, a need exists in accurately classifying different GI endoscopic images for upstream processing in the diagnostic platform for digital GI endoscopy. The purpose of this work was to develop a deep learning model using convolutional neural network (CNN) and wavelet decomposed CNN for improved accuracy using publically available GI endoscopic images from Kvasir dataset with 8 different image groups namely Z-line, Pylorus, Cecum, Esophagitis, Polyps, Ulcerative Colitis, Dyed and Lifted Polyps & Dyed Resection Margins. Wavelet decomposition along with CNN architecture allows utilization of spectral information which is mostly lost in conventional CNNs that can enhance model performance. The models were trained with 80% images and 20% were used for testing and accuracy was compared. 10% improvement in accuracy for multi-class classification was observed with wavelet CNN model compared to conventional CNN. The results indicate the potential of image decomposition methods for enhancing digital GI endoscopic procedures.

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Food Nutrient Extraction Based on image Recognition and Entity Extraction

Food Nutrient Extraction Based on Image Recognition and Enti...

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ieee international conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Hanzhong Gao Yanjun Liu Jingjuan Li Jianwei Gao Columbian College of Arts & Sciences Washington DC USA Institute of Vegetables Shandong Key Laboratory of Greenhouse Vegetable Biology Shandong Branch of National Vegetable Improvement Center Huanghuai Region Vegetable Scientific Station of Ministry of Agriculture (Shandong) Shandong Academy of Agricultural Sciences Jinan China

Nutrition is an important aspect of public health, and in recent years, there has been increasing interest in the nutritional information of food. However, processing this information can be a challenging task due to the large amounts of data involved. Machine learning (ML) has emerged as a useful tool to address this challenge. In this paper, we present a data resource that uses the FoodData Central (FDC) nutrient database to explore the combination of food images, nutritional information, and text with ML. We begin by providing an overview of machine learning and its applications in nutrition research, including the use of ML algorithms to identify food intake patterns, predict nutrient intakes, and evaluate dietary guidelines. We then describe the features and applications of Inception-v3, Inception-v4, and MobileNetV2 in ML, highlighting how these models can be used to extract nutritional information from food images. To further explore the potential of ML in nutrition research, we developed a quick search app that integrates images, text, and nutritional information. This app uses image recognition algorithms to identify food items in pictures, and text processing techniques to extract food information from text data. Users can simply take a picture of a food item and the app will provide the details of its nutritional content. This app can be used to facilitate the study of food and nutrition information and help promote healthier eating habits. In conclusion, the development of data resources and apps that use ML algorithms can be particularly helpful in processing large amounts of nutrition data and making it more accessible to the public. By harnessing the power of ML, we can advance our understanding of the relationship between diet and health, and ultimately work towards improving public health outcomes.

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An Efficient Dataflow for Convolutional Generative Models

An Efficient Dataflow for Convolutional Generative Models

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ieee international conference on Field-Programmable Technology (FPT)

作者： Zhengzheng Ma Guojie Luo National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Center for Energy-Efficient Computing and Applications Peking University

Convolutional generative models have achieved significant success in the field of artificial intelligence in recent years. These models typically rely on numerous convolution and transposed convolution operators, both of which are highly computationally intensive but present distinct computation patterns. As a result, accelerating the convolutional generative models can be challenging, particularly when the transposed convolution is likely to become performance bottleneck. In this paper, we propose a dataflow for overcoming the challenge and effectively accelerating both computation-hungry operators of convolutional generative models in the unified architecture. Additionally, we have devised specific tiling schemes to flexibly map both convolution and transposed convolution and fully reuse hardware resources. To demonstrate the effectiveness of our approach, we implemented DCGAN and ST on the Xilinx ZCU102 platform. Our experiments show that the proposed dataflow can reduce memory cost by up to 36%, achieving comparable performance for both convolution and transposed convolution.

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Abnormal image Classification Using Vision Transformer for Smart Agriculture

Abnormal Image Classification Using Vision Transformer for S...

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ieee international conference on Engineering Technologies and Applied Sciences (ICETAS)

作者： Talha Hafeez Muhammad Farrukh Shahid Tariq J.S. Khanzada Fast School of Computing FAST-NUCES Karachi Pakistan Department of Information System Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Computer Systems Engineering Departments Mehran UET Jamshoro Pakistan

Smart agriculture is an emerging innovative sector that harnesses technological advancements like sensor monitoring, image processing, soil quality evaluation, and automated water systems, all of which contribute towards enhanced crop yields and superior quality of produce. With the progression towards automated systems in smart agriculture, the need for a robust data infrastructure that assures data integrity is critical. This demands an integrated approach with anomaly detection algorithms and a robust classification technique to ensure an error-free data pipeline. This study employs two distinct datasets to achieve abnormal image classification using a vision transformer (ViT) and a convolutional neural network (CNN). The primary dataset, obtained through direct collection from cotton fields in Karachi and Balochistan, Pakistan, comprises high-resolution agricultural images captured using specialised cameras and Unmanned Aerial Vehicles (UAVs). The secondary dataset was sourced from a publicly available database. The two classification models, ViT and CNN, were applied to both datasets. These supervised models are primarily used to classify images as normal or abnormal. The results of the two classification models were also compared, with the ViT model surpassing the CNN model in performance and achieving the highest accuracy on both datasets. The Vision Transformer model proved to be robust for supervised data, showing remarkable accuracy of 96.00% on the public dataset, 99.18% on both camera based images and 97.56% on UAV images of collected dataset as compared to the CNN model's accuracy that are 82.00% on the public dataset and 98.36% on camera and 95.12% on UAV images of collected dataset.

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