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Automatic Building Extraction Based on High Resolution Aerial Images

Automatic Building Extraction Based on High Resolution Aeria...

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Electronic Information Technology and Computer Engineering (EITCE), international conference on

作者： Yiwen Hu Fenglin Guo School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

ISBN: (数字)9781728135847

ISBN: (纸本)9781728135854

Building extraction is a hotspot of research. Buildings in remote sensing images are numerous, and buildings have different distribution patterns in different regions, which means it is difficult to extract buildings by one traditional method for all areas. Deep learning plays more and more important part of computer version and image processing, which has made some progress in target detection and identification, especially in medical graphic recognition. In this paper, we compared three common datasets, and selected the most suitable one. Mask R- CNN is applied to this dataset. We provided a better method and this method can be used in different areas when dataset is rich enough for training. The method in this paper just need high resolution aerial images to extract buildings automatically without any other steps, because identification and extraction are achieved in one network. The research showed: the dataset we selected is suitable; Mask R-CNN can be used in high resolution remotely images; Mask R-CNN is good at building extraction, and greatly reduce the loss of classification and leakage. Therefore, this paper provided a suitable method for automatic building extraction.

关键词： Buildings Feature extraction Image resolution Remote sensing data mining Object detection Image segmentation

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Tracked-Vehicle Retrieval by Natural Language Descriptions With Domain Adaptive Knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions W...

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Huy Dinh-Anh Le Quang Qui-Vinh Nguyen Vuong Ai Nguyen Thong Duy-Minh Nguyen Nhat Minh Chung Tin-Trung Thá i Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper introduces our solution for Track 2 in AI City Challenge 2022. The task is Tracked-Vehicle Retrieval by Natural Language Descriptions with a real-world dataset of various scenarios and cameras. We mainly focus on developing a robust natural language-based vehicle retrieval system to address the domain bias problem due to unseen scenarios and multi-view multi-camera vehicle tracks. Specifically, we apply CLIP [16] to effectively extract both visual and textual representations for contrastive representation learning. Furthermore, for new scenarios in the test set, we pro-pose a novel Domain Adaptive Training method that utilizes information from labeled data and transfers it to the unseen domain by generating pseudo labels. By using this simple and effective strategy, we not only bridge the domain gap between the training set and test set, but also require less computational cost and data compared to previous top performance methods. Finally, we employ a context-sensitive post-processing method to address model’s uncertainty and eliminate the wrong retrieved vehicle track. Taking one step further, we also investigate the impact of different text formats and the number of pseudo labels data for the fine-tuning process. Our proposed method has achieved 3rd place in the AI City Challenge 2022, yielding a competitive performance of 47.73% MRR accuracy on the private test set, which verified the effectiveness of the proposed solution.

关键词： Training Representation learning Visualization Adaptive systems Uncertainty Computational modeling Urban areas

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Expression of Concern for: Exploring the Potential Benefits of machine learning for Optimizing Real-Time data Analysis

Expression of Concern for: Exploring the Potential Benefits ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Ravindra Kumar Nitin N. Sakhare N. Thangarasu K.K. Senthilkumar Pawan S. Wawage A Rengarajan Department of Civil Engineering Vivekananda Global University Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This studies paper examines the capability blessings of implementing device getting to know algorithms to optimize real-time information evaluation. The subjects mentioned consist of techniques for predicting traits in actual-time information, the ability of gadget studying algorithms to discover anomalies and outliers in datasets, and how using records mining and herbal language processing (NLP) can improve the accuracy of real-time facts insights. It also evaluates the demanding situations that can arise while applying machine gaining knowledge of algorithms to actual-time statistics, along with scalability problems and overfitting of records. In end, the authors argue that system studying has notable potential to enhance the accuracy and timing of actual-time facts analysis, and that they provide steerage on choosing and enforcing appropriate gadget getting to know algorithms.

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Research on Seismic Signal Classification and recognition Based on EEMD and CNN

Research on Seismic Signal Classification and Recognition Ba...

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IEEE international conference on Electronics and Communication Engineering (ICECE)

作者： Bingjun Li Hanming Huang Tingting Wang Mengqi Wang Pengfei Wang College of Computer Science and Information Engineering Guangxi Normal University Guilin China Institute of Geophysics China Earthquake Administration Beijing China

ISBN: (数字)9781728192581

ISBN: (纸本)9781728192598

The accurate classification of seismic signals is of great significance for updating clear earthquake catalog, early-warning the arrival of strong earthquake, and further researches in the field of seismology. This paper first preprocesses the seismic signal data of two types of events: earthquakes and explosions, and then uses the ensemble empirical mode decomposition (EEMD) method to decompose these preprocessed seismic data. A set of intrinsic mode functions being distributed from high-frequency to low-frequency are obtained from each seismic waveform, and then corresponding Hilbert spectrum is obtained by applying Hilbert transform to each of these intrinsic mode functions (IMFs). The image of Hilbert spectrum is scaled to a suitable and comparable size, by repeated trial-and-error experiments, to the size of 32*32 pixels, and then the 32*32 pixels gray-scale image is fed to a convolutional neural network(CNN) to classify the types of seismic signals. In this paper, 1674 waveforms of 54 earthquake events and 1509 waveforms of 63 explosion events are processed as above procedures. By five-fold cross-validation, 80% of the data is used as the training set, remaining 20% data as testing set. The highest classification accuracy rate of testing set is 94.98% with the average accuracy rate 94.41%,this results are significant higher than those of multi-layer perceptron(MLP) and Support Vector machine(SVM), which implying stronger classification capability of the scheme unifying EEMD and CNN proposed in this paper.

关键词： Training Earthquakes Seismology Support vector machine classification pattern classification Explosions Testing

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Mixing State Estimation of Peristaltic Continuous Mixing Conveyor with Distributed Sensing System Based on Soft Intestine Motion

Mixing State Estimation of Peristaltic Continuous Mixing Con...

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IEEE international conference on Soft Robotics (RoboSoft)

作者： K. Wakamatsu K. Inoue D. Hagiwara H. Adachi D. Matsui S. Kurumaya R. Nishihama M. Okui K. Nakajima Y. Kuniyoshi T. Nakamura Faculty of Science and Engineering Chuo University Japan 1-13-27 Kasuga Bunkyo-ku Tokyo Japan Graduate School of Information Science and Technology University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Research and Developmemnt Initiative Chuo University Japan 1-13-27 Kasuga Bunkyo-ku Tokyo Japan

ISBN: (数字)9781728165707

ISBN: (纸本)9781728165714

A peristaltic continuous mixing conveyor that focuses on the mechanism of the intestine has been developed as a technology for the mixing and transport of a solid-liquid mixture and high viscosity fluids. The developed peristaltic continuous mixing conveyor succeeded in the mixing and transport of such mixtures in a previous study. A simple cyclic pattern is currently used as the movement pattern of the system, and the control method can be further improved. In this study, we aim to realize an intelligent mixing and generation of a conveyor pattern based on intestinal movement. The mixing state of content during activation was estimated through machine learning. The results from the time-series measurement data show that an internal mixing state in three units of the peristaltic continuous mixing conveyor was estimated.

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Optimizing Potato Disease Detection in Pakistan with machine learning: A Comparative Analysis

Optimizing Potato Disease Detection in Pakistan with Machine...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), international conference on

作者： Irfan Ahmed Manahal Khalil Safoora Ghulam Jabar Mahmood Khadija Batool Nibtal Khalid Pakiza Arshad Dept of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science & Technology Zhejiang University Hangzhou China Dept of Computer Science Int. Institute of Science Art & Technology Gujranwala Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

The agricultural sector of Pakistan depends heavily on the production of potatoes, however diseases like Bacterial Wilt, Late Blight, and Early Blight are posing a growing danger to this industry since they can negatively affect crop productivity and food security. Detecting these illnesses using traditional methods is time-consuming, labor-intensive, and sometimes it requires specialized knowledge which is not easy for farmers. The implementation of a machine learning (ML) framework for illness identification using the methods of K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Support Vector machine (SVM), and Logistic Regression (LR) overcomes these challenges. This study utilizes data mining and preprocessing strategies to improve the accuracy of models and adaptability on a dataset of potato plants’ photos gathered across diverse areas in Pakistan. The models are assessed using performance criteria such computational efficiency, accuracy, precision, recall and F1-score. Random Forest model having 97% accuracy outperforms the other ML models in identifying plant disease. The suggested approach provides a practical, affordable, and effective way to identify diseases in a timely manner, allowing farmers to make informed decisions to lower crop losses. The aim of the study is to highlight how ML has the potential to transform Pakistani agriculture, enhancing food security and promoting long-term economic prosperity.

关键词： Support vector machines Productivity Adaptation models Accuracy Biological system modeling Computational modeling Crops Food security Random forests Classification tree analysis

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A Signal Processing and machine learning Framework for Enhanced TDS Simulation

A Signal Processing and Machine Learning Framework for Enhan...

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Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Shuvendu Pal Shuvo Shirshendu Pal Shibazee Arifa Akter Khuki Chaitee Das Goutam Paul Mitaly Paul Mita Dept. of CE Khulna University of Engineering and Technology Khulna Bangladesh Dept. of EEE Bangladesh University of Engineering and Technology Dhaka Bangladesh Dept. of MSE Khulna University of Engineering and Technology Khulna Bangladesh Dept. of Pharmacy Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh Dept. of Economics Khulna University Khulna Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Total dissolved solids (TDS) is one of the most significant indicators of regulated wastewater effluents, and it is critical in assessing and improving the performance of any wastewater treatment facility. The literature review reveals that the pattern of TDS and other water quality parameters generally shows noisy and complicated features; hence, capturing their pattern is tough. This paper proposes a new approach to noise reduction by Ensemble Empirical Mode Decomposition (EEMD), incorporating machine learning algorithms like Artificial Neural Network (ANN) and Support Vector machine (SVM) to improve the accuracy of TDS prediction. The data on temperature, pH, turbidity, and TDS were collected from the Bangabandhu Water Treatment Plant (BWTP) in Khulna, Bangladesh. In the current work, traditional and EEMD-based approaches were developed, and the performances of these were evaluated based on MSE, RMSE, and MAE. It is presented that the EEMD-based approach reduced the error considerably over the traditional models, while a maximum reduction of 89.46% and 91.04% in MSE are achieved in ANN and SVM, respectively. It further suggested that the proposed approaches could be applied to other wastewater treatment plants. Practical deployment of this model will help the operators make real-time decisions in optimized treatment processes.

关键词： Support vector machines Adaptation models Accuracy Empirical mode decomposition Adaptive systems Noise Artificial neural networks Solids Wastewater Wastewater treatment

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Detailed review on Integrated Healthcare Prediction System Using Artificial Intelligence and machine learning

Detailed review on Integrated Healthcare Prediction System U...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Deepa Parasar Arif Ali N. Muthuvairavan Pillai Archana Shahi Badria Sulaiman Alfurhood Kumud Pant CSE Amity University Maharashtra Department of CSE Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Computer Science and Business Systems R.M.D Engineering College Kavarapettai Chitkara Business School Chitkara University India Department of Computer Sciences College of Computer and Information Sciences Princess Nourahbint Abdulrahman University Saudi Arabia Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. Computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. Sensors connected through wireless networks make it possible to track the actions of a whole population. Sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology’s function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

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A Shilling Attack Model Based on TextCNN

A Shilling Attack Model Based on TextCNN

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Dongfang Hu Bin Xu Jun Wang Linfeng Han Jiayi Liu Henan Xuji Instrument Co. Ltd Xuchang China China Electric Power Research Institute Beijing China State Grod Shanxi Marketing Service Center Taiyuan China

ISBN: (数字)9781728198279

ISBN: (纸本)9781728181585

With the development of the Internet, the amount of information on the Internet is increasing rapidly, which makes it difficult for people to select the information they really want. A recommendation system is an effective way to solve this problem. Fake users can be injected by criminals to attack the recommendation system; therefore, accurate identification of fake users is a necessary feature of the recommendation system. Existing fake user detection algorithms focus on designing recognition methods for different types of attacks and have limited detection capabilities against unknown or hybrid attacks. The use of deep learning models can automate the extraction of false user scoring features, but neural network models are not applicable to discrete user scoring data. In this paper, random walking is used to rearrange the otherwise discrete user rating data into a rating feature matrix with spatial continuity. The rating data and the text data have some similarity in the distribution mode. By effective analogy, the TextCNN model originally used in NLP domain can be improved and applied to the classification task of rating feature matrix. Combining the ideas of random walking and word vector processing, this paper proposes a TextCNN detection model for user rating data. To verify the validity of the proposed model, the model is tested on MoiveLens dataset against 7 different attack detection algorithms, and exhibits better performance when compared with 4 attack detection algorithms. Especially for the Aop attack, the proposed model has nearly 100% detection performance with F1 - value as the evaluation index.

关键词： Analytical models data models Feature extraction Recommender systems Detection algorithms machine learning algorithms Classification algorithms

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Ethics of Artificial Intelligence: Research Challenges and Potential Solutions

Ethics of Artificial Intelligence: Research Challenges and P...

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international conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Tanzeela Jameel Rukhsana Ali Iqra Toheed Department of Humanities and Social Sciences Bahria University Islamabad Pakistan Faculty of Humanities and Social Sciences University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

Artificial Intelligence (AI) is a rapidly emerging paradigm with many applications in healthcare, industries, and smart cities. However, this rise of global interest in AI has fueled a renewed interest from the public sector and global policymakers. As AI networks (e.g., chatbots, automation systems, and helping agents) are paving their way as interactive household items, a critically important research issue is understanding the ethical impact of these autonomous agents. What is the explanation of the AI decision-making process? What are the legal, societal, and moral consequences of these decisions and actions? Should these AI systems be allowed to make decisions for human beings and to what extent? These questions along with some of the underlying concerns are the main research focus of modern societies and institutions. Contrary to the popular and frightening dystopic image of AI, this article aims to present recent research developments on the ethics of AI. In particular, a concise and brief introduction to different AI techniques is provided which is followed by a detailed discussion on the ethics of AI and its influencing components. Since data is the key to improve AI algorithms, details on developing high-quality data are also given. Finally, some solutions to the ethical issues of AI are discussed. This article is expected to act as a fundamental building block and as a comprehensive survey for ethical solutions of AI systems.

关键词： Ethics machine learning Decision making Companies Object recognition Computer science

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