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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

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Performance Enhancement in Agriculture Sector Based on Image processing

Performance Enhancement in Agriculture Sector Based on Image...

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Innovations in computer science & Software Engineering (ICONICS), international conference on

作者： Ali Hassan Faisal Rehman Muhammad Shahbaz Ashraf Ahmed Ashfaq Hanan Sharif Rana Zeeshan Salman Akram Hira Akram Department of Computer Science & Information Technology Lahore Leads University Lahore Pakistan

In the realm of agriculture and horticulture, machine vision and soft computing approaches have shown promise in overcoming the limitations of traditional methods for identifying plant illnesses utilizing various plant components. In all relevant studies such as fruit grading, leaf lesion area identification, and so on, image segmentation is the first and most important step. In order to diagnose the illness more effectively, a robust approach for numerous crops employing diverse plant components such as Fruit, Flower, and Leaf has been suggested in this study. Before segmentation, the acquired real-time pictures are pre-processed for illumination normalization and color space conversion. To enhance the segmentation outcomes, the conventional ML, image processing and deep learning approaches scheme has been made adaptable, and edge detection transformations have been implemented. To separate the sick regions from photos, the aim function of the Machine Learning approach has been adjusted, and cluster centers have also been upgraded. The many classes of plant illnesses noticed by shooting various types of images of diseases in plants, including many popular plants such as grapes, apple, and tomato. In terms of both broad human observation and computing time, the results achieved are superior.

关键词： Image segmentation Plants (biology) Pipelines Lighting Object segmentation Agriculture Real-time systems

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Trimap generation with background for natural image matting 3

Trimap generation with background for natural image matting

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3rd international conference on Optics and Machine Vision, ICOMV 2024

作者： Fu, Qian Liang, Yihui Kun, Zou Feng, Fujian Xu, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510680319

Image matting is a widely-used image processing technique that aims at accurately separating foreground from an image. However, this is a challenging and ill-posed problem that demands additional input, such as trimaps and background images, for providing prior knowledge. However, the manual annotation of trimaps require lots of labor, limiting the application of trimap-based methods. Some trimap-free methods explore alternatives with low labor requirements by utilizing captured background images, including background-based methods. However, the quality of alpha mattes predicted by trimap-free methods still fall short of trimap-based methods. To reduce the performance gap between background-based and trimap-based methodes, we present Trimap Generation from Background Image (TG-BG) method which can generate trimaps from the input image and a captured background image. It provides an economical solution to facilitate the application of trimap-based methods, allowing for low-cost and high-quality alpha matte predictions. TP-BG leverages a ViT backbone for feature extraction and employs the Image and Background Detail Fusion Stream (IBDFS) to capture multi-scale detail information. The introduction of foreground impact loss encourages the network to pay more attention to the foreground in the image. We validate the trimap prediction performance of TP-BG by comparing the alpha matte quality obtained by background-based methods and that obtained by trimap-based methods integrated with TP-BG. The experimental results demonstrate that TP-BG can generate high-quality trimap from a background image, and trimap-based methods integrated with TP-BG outperform the state-of-the-art background-based methods in terms of four alpha matte quality metrics. © 2024 SPIE.

关键词： Costs

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Data Security Mining Method for Social Media Users’ Mental Health Status Test Based on Machine Learning Algorithm 3rd

Data Security Mining Method for Social Media Users’ Mental ...

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3rd EAI international conference on IoT and Big Data Technologies for Health Care, IotCARE 2022

作者： Su, Junyu Liu, Wenjian Zhao, Hanxu Wang, Wennan Shi, Chiyu Faculty of Data Science City University of Macau China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China Academy of Management Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9783031335440

Users who frequently use social media can easily lead to changes in their mental health status. In order to accurately predict users’ mental health status, a data mining method for testing social media users’ mental health status is designed based on machine learning algorithms. Perform clustering processing on the test data, calculate the centroid of each cluster and the sum of squared errors of the data set, and obtain the clustering result of the test data;extract the characteristics of social media users’ mental health status, and calculate the abnormal score of the object's mental health status, Use this to distinguish the user's psychological state;build a data mining model based on machine learning algorithms, build a one-dimensional convolutional neural network framework, record the activation functions of the convolutional layer and the pooling layer, and obtain the output value of the fully connected layer;design social media user psychology Health state test data analysis algorithm to obtain a safe mining method for social media users’ mental health state test data. Obtain the best period of social media users’ mental health status observation through experiments. It can be seen from the experimental data that the correlation of the mental health state mining results obtained by this method is higher than 0.91, and the prediction accuracy is higher than 92%, which improves the effectiveness of mental health state prediction. The best observation period is one month to two months before the expected time. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Forecasting

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Study on Improved YOLOv7-based Algorithm for Detecting Crack Defects in Ancient Buildings 24

Study on Improved YOLOv7-based Algorithm for Detecting Crack...

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3rd international conference on computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Fang, Tianke Hui, Zhenxing Rey, William P Xie, Zhiying Yang, Aihua He, Yuanrong School of Information Technology Mapua University Manila1200 Philippines Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen Fujian361024 China School of Computing and Information Engineering Xiamen University of Technology Xiamen Fujian361024 China School of Information Media Zhangzhou Vocational College of Science and Technology Zhangzhou Fujian363200 China Hunan Key Laboratory of Remote Sensing Monitoring of Ecological Environment Dongting Lake Area Changsha Hunan410004 China

ISBN: (纸本)9798400716942

In the protection and restoration work of ancient buildings, the detection and analysis of the cracks and defects of the exterior walls of ancient buildings is a crucial task. In this paper, an improved YOLOv7 algorithm is proposed to detect the defects of ancient external walls, aiming to achieve efficient and accurate detection of the defects of ancient external walls. The method involves preprocessing the image using a rectangle clipping algorithm, graying the image using the maximum method, and denoising the image with a median filter. Next, the image is labeled using the LabelImg tool, and the data is split into training, validation, and test sets. During model training, a CBAM-Net module is inserted after several CBS modules in the backbone network, the GIOU loss function is replaced by CIOU, and feature task extraction is performed. The realized attention mechanism is added to the Yolov7 model. The results show that the mAP value of crack detection after the improved algorithm reaches 81.2%. In contrast to the initial YOLOv7 algorithm, the precision of the model has been boosted to 87.2%, exhibiting a 6.0% enhancement in accuracy and an 85.2% improvement in recall rate. The improved YOLOv7 crack detection algorithm reduces network calculation and improves crack classification and recognition ability. This approach is crucial in safeguarding ancient buildings as it enables varying levels of maintenance depending on the severity of cracks. © 2024 ACM.

关键词： Attention mechanism Deep learning Defect detection Image processing YOLOv7

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IoT Enabled Driver Compatible Cost-Effective System for Drowsiness Detection with Optimized Response Time 3rd

IoT Enabled Driver Compatible Cost-Effective System for Drow...

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3rd EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

作者： Sarkar, Argha Kundu, Mayuri Pareek, Prakash Gupta, Nishu Reis, Manuel J. Cabral S. School of Computer Science and Engineering REVA University Karnataka Bangalore560064 India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Electronic Systems Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Gjøvik2815 Norway Engineering Department UTAD/IEETA Vila Real5001-801 Portugal

ISBN: (纸本)9783031289743

The present work researches driver drowsiness, which constitutes a huge problem, and can turn into fatal incidents potentially involving the losing of lives, being it while driving in a highway (car, bus, truck, etc.), in the railway, or any other transportation mean (ship, airplane, etc.). Recent technology has been involved in the study of the fatigue behaviour. The purpose of this work is to identify the in-driver’s drowsiness, contributing to get rid of the accidents, mainly during the night, and to ensure better safety in train and on the highways. A camera is used to capture images from the driver, and face detection is executed in real-time to find out the exact position of driver’s face. Here, the blinking of the driver’s eye is under consideration. The fundamental concept lies in the closing of the driver’s eye for a specific duration of time. If the closing time exceeds that certain duration, drowsiness is detected and an alarm is sounded for awareness. Python and OpenCV (using Haarcascade library) are the fundamental programming platforms for sensing the facial attributes. In this work, a cost-effective system for the driver’s drowsiness detection is aimed. Further, the result also indicates the reduction of response and processing time, which is ideal for real time detection. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Python

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Securing Health Records Using Quantum Convolutional Neural Network 3rd

Securing Health Records Using Quantum Convolutional Neural N...

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3rd international conference on Sustainable Expert Systems, ICSES 2022

作者： Arulmozhi, B. Sheeba, J.I. Pradeep Devaneyan, S. Department of Computer Science and Engineering Pudhucherry Technologial University Puducherry605014 India Department of Mechanical Engineering Sri Venkateshwaraa College of Engineering and Technology Puducherry605012 India

ISBN: (纸本)9789811978739

In block chain technology, a distributed architecture, blocks are the data storage and processing unit. Data must be exchanged from one party to another safely and securely, and the block chain must be updated using smart contracts. But still, the problem facing was securing the healthcare data. So, this paper proposed securing health records using the QCNN method, which stands for a Quantum Convolutional Neural Network with Block Chain Technology, to safeguard medical data. Block chain technology's consensus process ensures that all data is correct and all transactions are safe. The datasets are collected from the data world repository. The datasets are pre-processed using label coding with the standard scalar method. The features are analyzed with QCNN and the patient healthcare data to increase the security and interoperability using block chain technology. The research specifically looks at Electronic Health Record (EHR) security issues to be overcome. This proposed quantum conventional neural network (QCNN) deep learning architecture provides higher accuracy and security than the other existing algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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Time-Weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection

Time-Weighted Frequency Domain Audio Representation with GMM...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jian Guan Youde Liu Qiaoxi Zhu Tieran Zheng Jiqing Han Wenwu Wang Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University China School of Computer Science and Technology Harbin Institute of Technology China Centre for Audio Acoustics and Vibration University of Technology Sydney Australia Centre for Vision Speech and Signal Processing University of Surrey UK

Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer parameters. Existing statistical frequency representations, e.g. the log-Mel spectrogram’s average or maximum over time, do not always work well for different machines. This paper presents Time-Weighted Frequency Domain Representation (TWFR) with the GMM method (TWFR-GMM) for anomalous sound detection. The TWFR is a generalized statistical frequency domain representation that can adapt to different machine types, using the global weighted ranking pooling over time-domain. This allows GMM estimator to recognize anomalies, even under domain-shift conditions, as visualized with a Mahalanobis distance-based metric. Experiments on DCASE 2022 Challenge Task2 dataset show that our method has better detection performance than recent deep learning methods. TWFR-GMM is the core of our submission that achieved the 3rd place in DCASE 2022 Challenge Task2.

关键词： Measurement Deep learning Visualization Time-frequency analysis signal processing Complexity theory Task analysis

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vThrot: Fine-Grained, Virtual I/O Resource Redistribution Scheme 3rd

vThrot: Fine-Grained, Virtual I/O Resource Redistribution Sc...

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3rd international conference on Innovations in Computing Research, ICR 2024

作者： Kim, Taehoon No, Jaechun Park, Sung-Soon Department of Computer Science and Engineering Sejong University Seoul Korea Republic of Department of Computer Engineering Anyang University and Gluesys Co. LTD Manan-go Korea Republic of

ISBN: (纸本)9783031655210

As the size of host storage increases, VMs on the same host would receive a larger virtual storage, which is then divided into multiple virtual partitions. Each partition is sufficiently large to independently execute heterogeneous applications. However, most of the current I/O resource management systems focus on allocating host resources among VMs running on the same host, rather than redistributing the I/O resources allocated to the VMs between their virtual partitions. In this paper, we present a dynamic, fine-grained virtual I/O throttling scheme, called vThrot, where I/O resources allocated to a VM can be dynamically redistributed on its virtual partitions. In vThrot, each virtual partition independently executes heterogeneous applications while retaining their unique I/O characteristics. Furthermore, to reflect the urgency of applications, it is equipped with the priority-based hint that can effectively be passed to the host kernel, facilitating faster I/O processing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Resource allocation

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Research and simulation of adaptive routing mechanism in InfiniBand network 3

Research and simulation of adaptive routing mechanism in Inf...

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3rd international conference on Advanced Manufacturing Technology and Manufacturing Systems, ICAMTMS 2024

作者： Wu, Ruolan Wan, Wei Li, Junhong School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Co. Ltd. Beijing100193 China School of Software Tsinghua University Beijing100084 China

ISBN: (纸本)9781510681804

InfiniBand interconnect network is widely used in parallel computing. Network adapter (HCA) is one of the necessary hardware components for architecture deployment. With the expansion of nodes in parallel computing system, InfiniBand network may have higher performance requirements. However, the current InfiniBand network uses deterministic routing, and adaptive routing can be introduced to meet higher requirements. Because the original InfiniBand protocol is strictly sequence-preserving, adding adaptive routes to the network improves performance but requires out-of-order rearrangement of network cards, which requires a certain cache capability. In this paper, the function extension of InfiniBand protocol is designed to realize the adaptive function and complete the modeling and simulation. Two indexes related to out-of-order are also designed in this paper, and the direct influence of adaptive routing on packet out-of-order and network adapter buffer depth is studied from many aspects. Through the simulation of the above system, the experimental result of All-to-All communication between model nodes shows that under the Greedy and RoundRobin adaptive port selection algorithms, the buffer depth required by the network card is 61 and 62 2KB of packet space, respectively. This provides an estimate of the buffer that the network card needs to handle out-of-order packets. © 2024 SPIE.

关键词： Parallel processing systems

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A Review on Artificial Intelligence Techniques for Multilingual SMS Spam Detection 3rd

A Review on Artificial Intelligence Techniques for Multiling...

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3rd international conference on computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Ramanujam, E. Shankar, K. Sharma, Arpit Computer Science and Engineering National Institute of Technology Silchar Assam Silchar788010 India Electronics and Instrumentation Engineering National Institute of Technology Silchar Assam Silchar788010 India University Bengaluru India

ISBN: (纸本)9789819966899

With social networks’ increased popularity and smartphone technology advancements, Facebook, Twitter, and short text messaging services (SMS) have gained popularity. The availability of these low cost text-based communication services has implicitly increased the intrusion of spam messages. These spam messages have started emerging as an important issue, especially to short-duration mobile users such as aged persons, children, and other less skilled users of mobile phones. Unknowingly or mistakenly clicking the hyperlinks in spam messages or subscribing to advertisements puts them under threat of debiting their money from either the bank account or the balance of the network subscriber. Different approaches have been attempted to detect spam messages in the last decade. Many mobile applications have also evolved for spam detection in English, but still, there is a lack of performance. As English has been completely covered under natural language processing, other regional languages, such as Urdu and Hindi variants, have specific issues detecting spam messages. Mobile users suffer greatly from these issues, especially in multilingual countries like India. Thus, this paper critically reviews the artificial intelligence-based spam detection system. The review lists out the existing systems that use machine and deep learning techniques with their limitations, merits, and demerits. In addition, this paper covers the scope for future enhancements in natural language processing to efficiently prevent spam messages rather than detect spam messages. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Text messaging

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