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GMTS: GNN-based multi-scale transformer siamese network for remote sensing building change detection

International Journal of Digital Earth 2023年第1期16卷 1685-1706页

作者： Xinyang Song Zhen Hua Jinjiang Li School of Infomation and electronic engineering Shandong Technology and Business UniversityInstitute of Network Technology(ICT)YantaiPeople's Republic of China School of Computer Science and Technology Shandong Technology and Business UniversityYantaiPeople's Republic of China

With the remarkable success of change detection(CD)in remote sensing images in the context of deep learning,many convolutional neural network(CNN)based methods have been *** the current research,to obtain a better context modeling method for remote sensing images and to capture more spatiotemporal characteristics,several attention-based methods and transformer(TR)-based methods have been *** research has also continued to innovate on TR-based methods,and many new methods have been *** of them require a huge number of calculation to achieve good ***,using the TR-based mehtod while maintaining the overhead low is a problem to be ***,we propose a GNN-based multi-scale transformer siamese network for remote sensing image change detection(GMTS)that maintains a low network overhead while effectively modeling context in the spatiotemporal *** also design a novel hybrid backbone to extract *** with the current CNN backbone,our backbone network has a lower overhead and achieves better ***,we use high/low frequency(HiLo)attention to extract more detailed local features and the multi-scale pooling pyramid transformer(MPPT)module to focus on more global features ***,we leverage the context modeling capabilities of TR in the spatiotemporal domain to optimize the extracted *** have a relatively low number of parameters compared to that required by current TR-based methods and achieve a good effect improvement,which provides a good balance between efficiency and performance.

关键词： Remote sensing(RS) change detection(CD) depthwise over-parameterized convolutional(DO-Conv) attention mechanism transformer graph convolution

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CRipS: Coconut Ripeness Stage Detection System

CRipS: Coconut Ripeness Stage Detection System

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Venkatesh Prajwal, K.P. Patil, Preeti Priyanka, G. Poojary, Prajwal Basapur, Satish B University of Visvesvaraya College of Engineering Dept. of Computer Science and Engg. Bangalore India Dr. Ambedkar Institute of Technology Dept. of Infomation Science and Engg. Bangalore India

ISBN: (纸本)9798350300826

Manual Coconut harvesting requires tree-climbing skill, it is hazardous because of the tree's hard shell, height and high occlusions on the tree. Further, it is difficult to determine whether a coconut is ripe or not because of the outside illumination and various background. The development of a coconut ripen stages detection system with the help of deep learning techniques is of great interest to farmers. This research work proposes a deep learning-based instance segmentation technique to identify different coconut ripeness stages. To improve instance segmentation accuracy to identify different coconut ripeness stages, the SOLOv2 model is used. In this research work, collected image dataset consisting of approximately 500 RGB images of different types of coconut fruits in coconut plantations during daylight under natural lighting conditions using a handheld digital single-lens reflex camera. The proposed SOLOv2 model was trained, validated, and tested on 500 manually acquired and augmented images. On the test dataset, the mean average precision (mAP) and Average Precision (at different intersections-over-union (IoU) thresholds), Average Precision (at different intersection-over-union (IoU) thresholds) attained by the model were 0.628, 0.628 and 0.811, respectively, with an average precision and recall for identifying ripeness stages as 0.912 and 0.883, respectively. I data enables the creation of a comprehensive vision system to pinpoint the cutting spot on the coconut cluster and select the harvesting method. The experimental results enable us to develop an automated ripeness detection system to determine the ripeness stages for harvesting and pinpointing the cutting spot on the coconut tree. © 2023 IEEE.

关键词： Harvesting

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer Systems science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed Abid Sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

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Music Genre Categorization using Machine learning Algorithms

Music Genre Categorization using Machine learning Algorithms

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2021 International Conference on Computational Intelligence and Computing Applications, ICCICA 2021

作者： Prashanthi, Vempaty Kanakala, Srinivas Akila, V. Harshavardhan, A. Gokaraju Rangaraju Institute of Engineering and Technology Infomation Technology Hyderabad India Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9781665420402

Music genre prediction is a difficult job in the field in Retrieval of Musical Data. Music group categorization is essential for the music recommending systems, since genre has a high weight in such systems and their recommendations. A machine learning model is designed which automatically classifies the genre of a music clip. Here, we are going to extract acoustic music features with the help of digital signal processing and then classification of music is done with the help of machine learning methods. Librosa, is a tool we will be using for audio feature extraction, which offers a full-featured work-flow situation for low and high-level audio features. In this paper, we are going to utilize k-Nearest Neighbours method for the reason that in many research it is shown that this method gives good outcomes in such scenario. We will be using music dataset GTZAN Genre Collection (1010 clips). © 2021 IEEE.

关键词： Music

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Using Machine Learning Techniques for Cancer Classification

Using Machine Learning Techniques for Cancer Classification

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Reddy, Ch. Srinivasa Singh, Rajesh Bhavani, R. Dasgupta, Sanhita Singh, Yashpal Pratan Singh, Suryabhan Vigan's Institute of Information Technology Department of IT Duvvada India Uttaranchal University Uttaranchal Institute of Technology India Saveetha Institute of Medical and Technical Sciences Institute of Computer Science and Engineering Saveetha School of Engineering Chennai600124 India Bir Bikram Department of Computer Memorial College Tripura Agartala India Jain Deemed to Be University Department of Computer Science & Engineering Karnataka Bengaluru India Deen Dayal Upadhyaya Gorakhpur University Institute of Engineering and Technology Department of Infomation Technology Gorakhpur India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Given the extremely high death and suffering rates caused by breast cancer, women are particularly at risk. Because of this, it is imperative that algorithms be developed to find breast disease's early warning signs. Decision trees, random forests, regression analysis, and Mri are four categorised algorithms that detect cancer, and the end results were contrasted. Machine learning is essential for predicting breast cancer's early signs. Three data augmentation different classifiers will be used in this research. when categorising algorithms Data with unbalanced classifications are a serious issue that calls for careful treatment and preprocessing. We'll train the various M.L. models using a large dataset of breast cancer patients. The optimal method for this issue is ultimately determined by comparing all the strategies in terms of efficiency and precision. Our research will demonstrate the efficacy of several methods for this category of breast cancer so that we can use the proper strategy. © 2022 IEEE.

关键词： Convolutional neural networks

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CRipS: Coconut Ripeness Stage Detection System

CRipS: Coconut Ripeness Stage Detection System

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Venkatesh Prajwal K P Preeti Patil Priyanka G Prajwal Poojary Satish B Basapur Dept. of Computer Science and Engg. University of Visvesvaraya College of Engineering Bangalore India Dept. of Infomation Science and Engg. Dr. Ambedkar Institute of Technology Bangalore India

关键词：

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On the Design of Capacity-Achieving Distributions for Discrete-Time Poisson Channel with Low-Precision ADCs

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Qianqian Li, Lintao Liu, Lixiang Yang, Lei Gong, Caihong Li, Hua Hao, Shiya Dai, Xiaoming University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Technology and Engineering Center for Space Utilization Beijing100094 China Zhongyuan University of Technology School of Electronic and Infomation Engineering Zhengzhou451191 China University of Science and Technology Beijing Shunde Innovation School Foshan528399 China

This paper investigates the design of the capacity-achieving input distribution for the discrete-time Poisson channel (DTPC) under dark current effects with low-precision analog-to-digital converters (ADCs). An efficient algorithm integrating the Newton-Raphson and Blahut-Arimoto (BA) methods is proposed to jointly optimize the input distribution and amplitude levels under both peak and average power constraints. Additionally, the Karush-Kuhn-Tucker (KKT) conditions are established to provide necessary and sufficient conditions for the optimality of the obtained capacity-achieving distribution. Simulation results illustrate that the proposed algorithm attains 72% and 83% of the theoretical capacity at 5 dB for 1-bit and 2-bit quantized DTPC, respectively. Furthermore, for a DTPC with finite-precision quantization (i.e., log2K bits), the capacity can be achieved by a non-uniform discrete input distribution with support for K mass points, under the given power constraints. © 1967-2012 IEEE.

关键词： analog-to-digital converter Capacity-achieving distribution discrete-time Poisson channel non-uniform discrete input distribution

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TransRegex: Multi-modal Regular Expression Synthesis by Generate-and-Repair

TransRegex: Multi-modal Regular Expression Synthesis by Gene...

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International Conference on Software engineering (ICSE)

作者： Yeting Li Shuaimin Li Zhiwu Xu Jialun Cao Zixuan Chen Yun Hu Haiming Chen Shing-Chi Cheung State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Hong Kong University of Science and Technology Hong Kong China Science #x0026 Technology on Integrated Infomation System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665402965;9781665448314

Since regular expressions (abbrev. regexes) are difficult to understand and compose, automatically generating regexes has been an important research problem. This paper introduces TransRegex, for automatically constructing regexes from both natural language descriptions and examples. To the best of our knowledge, TransRegex is the first to treat the NLP-and-example-based regex synthesis problem as the problem of NLP-based synthesis with regex repair. For this purpose, we present novel algorithms for both NLP-based synthesis and regex repair. We evaluate TransRegex with ten relevant state-of-the-art tools on three publicly available datasets. The evaluation results demonstrate that the accuracy of our TransRegex is 17.4%, 35.8% and 38.9% higher than that of NLP-based approaches on the three datasets, respectively. Furthermore, TransRegex can achieve higher accuracy than the state-of-the-art multi-modal techniques with 10% to 30% higher accuracy on all three datasets. The evaluation results also indicate TransRegex utilizing natural language and examples in a more effective way.

关键词： Natural languages Maintenance engineering Tools Software engineering

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Requirement-oriented health metrics of integrated avionics systems 14

Requirement-oriented health metrics of integrated avionics s...

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14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019

作者： Guo, Yangming Wang, Xiaodong Zhao, Ningshe En, Yunfei Ma, Jiezhong Huang, Yun School of Computer Science NorthWestern Polytechnical University Xi'an China School of Infomation Engineering Xi'an University Xi'an China Fifth Electronics Research Institute of Industry and Information Technology Guangzhou China

ISBN: (纸本)9781538694909

With the development of avionics system, the demand of health metrics of integrated system is getting stronger and stronger. By describing the requirements and resources of an integrated system logically, a requirementoriented health metrics method of integrated system is proposed in this paper, then a reconstruction model of system resources is proposed depend upon the method. Furthermore, the decision-making method based on health metrics of integrated system is discussed. © 2019 IEEE.

关键词： Health

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