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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

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A Novel Item Recommendation Framework based on Deep Learning Algorithm 7

A Novel Item Recommendation Framework based on Deep Learning...

引用

7th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Chunrong, Yao Wuhan Qingchuan University Wuhan430000 China

ISBN: (纸本)9798350341485

With their unique adaptive characteristics, neural networks can effectively solve unstructured problems such as patterns and complex information. They have been widely used in neural expert systems, convex optimization and prediction, and other fields. The organic integration of neural networks and other conventional research methods will greatly promote the development of artificial intelligence, information analysis and other fields. In this study the deep neural network is applied to the task of item recommendation. Efficient information search is the initial step for the task of item recommendation, the Top-K sorting algorithm is constructed. Top-K sorting is based on the List-wise method, the List-wise method uses all document lists as the samples of the input space, also called the document list method, and its output is a sorted list. This can be treated as the pre-processing of the data. Then, the graph neural network is selected as the deep structure, it is a general term for learning methods and also the core idea is to continuously update the representation of the nodes on the basis of the message-passing mechanism. In the experiment, MovieLens-20M, MovieLens-1M and the BookCrossing data sets are used to verify the proposed algorithm. Recall and AUC are used as the performance indicator, and compared with the state-of-the-art algorithms, the proposed model outperformed. © 2023 IEEE.

关键词： Expert systems

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vision Transformer based ResNet Model for Pneumonia Prediction 4

Vision Transformer based ResNet Model for Pneumonia Predicti...

引用

4th International conference on Electronics and Sustainable Communication systems, ICESC 2023

作者： Sowmya, Thatikonda Sai Narasimhulu, Thumma Sunitha, Gurram Manikanta, Thupakula Venkatesh, Thirupathi Sree Vidyanikethan Engineering College Department of CSE A.P Tirupati India Mohan Babu University School of Computing Department of Ai & Ml A.P Tirupati India

ISBN: (纸本)9798350300093

This research study aims to develop a pneumonia detection system using vision transformers. Pneumonia is a very serious respiratory illness that may result in severe health issues, and early detection is essential for effective treatment. Deep learning-based computer vision algorithms have yielded encouraging results in medical image analysis in recent years, and vision transformers have emerged as a potent tool for processing visual data. The proposed system uses a vision transformer to process chest x-ray images and extract visual traits, which can be utilized for classification. The proposed model 'vit_base_resnet50_224_in21k' is trained on a vast and diverse dataset of annotated chest x-ray images to understand the patterns and characteristics of pneumonia. The system's performance is estimated using standard evaluation parameters - accuracy, loss, specificity, sensitivity, F1-score and ROC curve. The proposed model has achieved a performance accuracy of 92.96% for pneumonia detection. The results demonstrate the potential of vision transformers in chest x-ray image analysis and contribute to the development of more accurate and efficient tools for pneumonia detection. This system has the potential to assist healthcare professionals in making faster and more accurate diagnosis, which can ultimately improve outcomes and save lives. © 2023 IEEE.

关键词： image analysis

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A New Efficient Split & Merge Algorithm for Embedded systems

A New Efficient Split & Merge Algorithm for Embedded Systems

引用

IEEE International conference on image processing

作者： Nathan Maurice Julien Sopena Lionel Lacassagne LIP6 Sorbonne University Paris

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This article presents a new image segmentation algorithm based on a Split & Merge approach. By nature, the execution time of Split & Merge algorithms is data-dependent, as their halting conditions are tied to the homogeneity of each region. While previous algorithms made the Split step less sensitive to input data, the execution time of the more complex Merge step remains highly sensitive to image content. This paper tackles the sensitivity and performance problems from a system and architecture perspective. Memory reallocations due to array fusions are eliminated with the introduction of a TTA (Three Table Array) structure in the Merge step. As iterating over entries in this structure causes a loss of memory locality, we propose two new mechanisms that implement a software cache to mitigate this. An experimental study on an embedded system (Nvidia Jetson Xavier NX) has shown our Merge algorithm to be 10.6 times faster than the state-of-the-art Split & Merge algorithm for $960 \times 720$ images. Moreover, the execution time of our algorithm is also more resistant to image characteristics.

关键词： Resistance image segmentation Embedded systems Sensitivity Software algorithms Streaming media Prediction algorithms

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Face Recognition Implementation Based on image processing Techniques

Face Recognition Implementation Based on Image Processing Te...

引用

Signal processing and Intelligent Computing (SPIC), International conference on

作者： Bingcan Yang Xiai Chen School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

image processing is a technique that involves the manipulation and enhancement of digital images. It encompasses various aspects such as image acquisition, image preprocessing, image enhancement, image segmentation, feature extraction, and object detection. The purpose of image processing is to process and enhance images using computer technology, aiming to achieve higher image quality for subsequent processing and applications. In practical applications, image processing techniques employ different algorithms and methods to achieve various processing and enhancement effects. Additionally, image processing techniques can be applied in fields such as medicine, security, autonomous driving, VR/AR, etc. For instance, in the medical field, image processing techniques can be utilized for image segmentation and diagnosis of medical images, thereby improving the accuracy and analytical capabilities of medical imaging. In the security field, image processing techniques can be employed for tasks such as video surveillance and facial recognition, enhancing the efficiency and accuracy of security monitoring. In the domain of autonomous driving, image processing techniques can be used for detecting and recognizing the surrounding environment of vehicles, thereby enhancing the safety and performance of autonomous driving systems. In conclusion, image processing technology is a very important technology. By continuously improving the accuracy and reliability of the algorithm, it is expected to be applied in a wider range of applications.

关键词： image segmentation image recognition Accuracy image processing Face recognition Signal processing algorithms Signal processing video surveillance Medical diagnostic imaging Autonomous vehicles

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Direction-of-arrival estimation with new versions of Min-Norm algorithm based on Nyström method 7

Direction-of-arrival estimation with new versions of Min-Nor...

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7th International conference on image and Signal processing and their Applications, ISPA 2022

作者： Naceur, Aounallah Ali, Khalfa Kasdi Merbah University Faculty of New Information Technologies and Communication Department of Electronic and Telecommunications Ouargla30000 Algeria Mohamed Boudiaf University Faculty of Technologies Department of Electronic M'Sila28000 Algeria

ISBN: (纸本)9781665480420

Direction-of-arrival estimation is a significant problem in many telecommunication systems which their architecture is composed with antenna arrays. In literature, several algorithms were proposed as a challenge, for different scenarios, to detect rapidly and accurately the direction of impinging signals on the antenna array. In this paper firstly, we use the Nyström method to develop a low computational Min-Norm version for solving the problem of direction-of-arrival estimation by spectral peak searching. Then, we generalize the root version of the proposed algorithm. Finally, we give some simulation results for demonstrating the efficiency and the performances of the new proposed algorithm versions. © 2022 IEEE.

关键词： Array processing

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Comparison and Application of Vehicle Target Detection Methods Based on YOLOv5s and YOLOv7 7

Comparison and Application of Vehicle Target Detection Metho...

引用

7th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Gu, Chenyu Du, Hong Zhang, Xiaozheng Li, Lidan Yang, Zhonglin Liu, Gaotian China Northern Vehicle Research Institute Beijing China

ISBN: (纸本)9798350350760

Currently, the methods for pedestrian detection have undergone significant expansion, yet vehicle target detection still retains immense potential for widespread application in the field of intelligent recognition. Nevertheless, vehicle target detection encounters challenges in terms of detection effectiveness, impacted by factors such as lighting conditions, environmental variations, and occlusions. This study centers on vehicle target detection, aiming to tackle the issues of difficult detection, easy missed detection, and algorithm selection within complex backgrounds. By utilizing the YOLOv5s and YOLOv7 algorithms, we have constructed a dataset that encompasses images featuring vehicles against complex backgrounds and in small scales. This dataset is then used to train the models, which are subsequently employed to perform target recognition for vehicles individually. Through comparative experimentation, we have proven the superiority of the YOLOv7 algorithm in detecting vehicles amidst complex backgrounds and those of small scales. ©2024 IEEE.

关键词： image processing vehicle target detection YOLOv5s YOLOv7

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Green Intelligence: A Sequential CNN Odyssey in Mustard Leaf Disease Detection

Green Intelligence: A Sequential CNN Odyssey in Mustard Leaf...

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2024 ASU International conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Sakshi Sharma, Chetan Sharma, Shamneesh Sharma, Tushar Gochhait, Saikat School of Computer Science and Engineering Sharda University Uttar Pradesh Greater Noida India UpGrad Campus UpGrad Education Private Limited Karnataka Bangalore India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Sidtm Symbiosis International Deemed University Maharashtra Pune India Nri Samara State Medical University Russia

ISBN: (纸本)9798350372229

Mustard plants are a crucial agricultural commodity for food and oil production. However, they are frequently vulnerable to various diseases that can substantially reduce crop yield. Early identification and diagnosis of these illnesses are essential for efficient management and control, ensuring sustainable production and agricultural output continuity. This research presents a new method for categorizing mustard leaf diseases using Convolutional Neural Networks (CNNs). The study utilizes sophisticated machine learning algorithms to differentiate between healthy and diseased mustard leaves, which is crucial for maintaining agricultural output and guaranteeing food security. The study involves training CNN architectures - Sequential CNN, ResNet-50, VGG, and AlexNet - on a meticulously curated dataset comprising images of healthy and diseased mustard leaves. The classifier's effectiveness is validated through comprehensive testing, demonstrating significant precision, recall, and F1-score advancements over conventional methods. This approach provides an efficient tool for disease detection in mustard crops and contributes to sustainable agricultural practices, aligning with the global goal of food security and environmental sustainability. © 2024 IEEE.

关键词： Agricultural productivity Crops Data Augmentation image processing Precision Agriculture Sustainable Agriculture

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Adaptive Cache Management for Complex Storage systems Using CNN-LSTM-Based Spatiotemporal Prediction 3

Adaptive Cache Management for Complex Storage Systems Using ...

引用

3rd International conference on image processing, Computer vision and Machine Learning, ICICML 2024

作者： Wang, Xiaoye Li, Xuan Wang, Linji Ruan, Tingyi Li, Pochun Western University London Canada Columbia University New York United States George Mason University Fairfax United States Northeastern University Boston United States

ISBN: (纸本)9798350355413

This paper proposes an intelligent cache management strategy based on CNN-LSTM to improve the performance and cache hit rate of storage systems. Through comparative experiments with traditional algorithms (such as LRU and LFU) and other deep learning models (such as RNN, GRU-RNN and LSTM), the results show that the CNN-LSTM model has significant advantages in cache demand prediction. The MSE and MAE values of this model are significantly reduced, proving its effectiveness under complex data access patterns. This study not only verifies the potential of deep learning technology in storage system optimization, but also provides direction and reference for further optimizing and improving cache management strategies. This intelligent cache management strategy performs well in complex storage environments. By combining the spatial feature extraction capabilities of convolutional neural networks and the time series modeling capabilities of long short-term memory networks, the CNN-LSTM model can more accurately predict cache needs, thereby Dynamically optimize cache allocation to improve system response speed and resource utilization. This research provides theoretical support and practical reference for cache optimization under large-scale data access modes, and is of great significance to improving the performance of future storage systems. © 2024 IEEE.

关键词： convolutional neural network deep learning Intelligent cache management storage system optimization

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Computer-Aided Audit Software Based on Fuzzy Clustering Algorithm

Computer-Aided Audit Software Based on Fuzzy Clustering Algo...

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image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Yuan Fen Wuhan Qingchuan University Wuhan City Hubei Province China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

According to the needs of auditing work, efforts are made to develop suitable computer-aided auditing software to gradually change or enhance traditional auditing techniques and methods, which can better play the role of audit supervision. The purpose of this paper is to study the design and optimisation of computer-aided audit software based on fuzzy clustering algorithms. By analysing the technical difficulties of computer-aided auditing, this paper proposes the design concept of the project software. The improved fuzzy C-mean clustering algorithm DFCM and the fast audit steps of the project based on improved fuzzy clustering are specifically introduced, and the data processing methods of the system are described in detail, and the specific application of the project software is also presented. The experimental results show that the computer-aided audit software in this paper can be applied to calculate the audit price of the project to be audited and the software is feasible.

关键词： image processing Software algorithms Clustering algorithms Quality control Data warehouses Data processing Software Protection Expert systems Optimization

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Imperfectly Perfect AI Chatbots: Limitations of Generative AI, Large Language Models and Large Multimodal Models

Imperfectly Perfect AI Chatbots: Limitations of Generative A...

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2024 International conference on Computing, Communication, Cybersecurity and AI

作者： Naik, Dishita Naik, Ishita Naik, Nitin Birmingham City Univ Birmingham W Midlands England King Edward VI Five Ways Sch Birmingham W Midlands England Aston Univ Sch Comp Sci & Digital Technol Birmingham W Midlands England

ISBN: (纸本)9783031744426;9783031744433

Generative AI has transformed the landscape of AI chatbots in a way that they are increasingly becoming human-like in their understanding and working. Generative AI is used for creating new contents, and Large Language Models (LLMs) and Large Multimodal Model (LMMs) are a type of generative AI. Most modern AI chatbots are designed using LLMs and LMMs, which provide them with the power of generative AI. LLMs deal with the text, whereas LMMs deal with more modalities including text, image, audio and video. This continuous enhancement of generative AI models is bringing AI chatbots closer to the human working. Nonetheless, these AI chatbots are not perfect, as they have several limitations, and their users should comprehend before fully relying on these imperfectly perfect AI chatbots. Depending on the type of use of AI chatbots, these limitations may or may not impact them significantly. This paper will organise the limitations of AI chatbots into six main categories: intelligence and understanding related limitations;accuracy and credibility related limitations;ethics and regulations related limitations;accountability, transparency and consistency related limitations;design, coding and training related limitations;and human, machine and vender related limitations. The aim of this paper is to emphasise, organise and analyse numerous limitations of AI chatbots into the proposed categories;accordingly, users should become more cognisant about all these limitations in deciding their suitability or unsuitability for the specific use cases.

关键词： AI Chatbots Multimodal AI models ChatGPT Gemini Limitations of AI Chatbots LLMs Large Language Models LMMs Large Multimodal Models NLP Natural Language processing Generative AI Lack of Intelligence Lack of Contextual Understanding Hallucination Transparency Accountability Legal and Regulatory Risks Uncanny Valley Vender Dependency Jailbreaking

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