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Machine Learning Yield Forecast in the IoT System for Cultivating Accuracy 1

2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Jayakarthik, R. Kumar, Santosh Goswami, Sohan Mahaveerakannan, R. Hafeez, Shaik Abdul Tamilnadu Chennai India Department of Computer Science ERA University Uttar Pradesh Lucknow India Department of Computer Science and Technology Technique Polytechnic Institute West Bengal Panchrakhi India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

Crop production is a very complicated attribute that depends on a number of variables, including genotype, environment, and their interactions. The functional relationship between yield and these interaction elements must be fundamentally understood for accurate yield prediction to be possible, and both large datasets and potent algorithms are needed to show this relationship. In order to accurately estimate crops, we applied cutting-edge machine learning techniques. Our ability to gather data for huge agricultural areas spread throughout far-flung regions of the globe is made possible by the Internet of Things’ architectural principles. So that we may make forecasts about crops, our machine learning system can use this data. Nitrogen, phosphorus, potassium, temperature, humidity, and rainfall all have a role in determining the best crop to grow. Recommendations are made in light of these factors. There are 2200 total occurrences in the data collection, and there are 8 associated attributes for each one. For each of the 8 possible characteristic combinations, around twenty-two distinct plant species are available as potential options. The most effective model can be produced by utilizing the supervised learning approach and few of the machine learning methods available in WEKA. As prospective possibilities for the methods of machine learning that would be used in the classification process, the decision table classifier and the multilayer perceptron rules-based classifier JRip were selected. The system’s design took into account both the growing Internet of Things and the essential measurements required for farming. The average weighted value of the Receiving Operator Attributes has been found to be 1, the performance measured by the classifiers that were chosen has a value of 98.2272%, and the maximum time needed to construct the model is 8.03 s. Employing machine learning in agriculture aims to increase the production and nutritional value of the plants generated

关键词： Adversarial machine learning

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Dynamic monitoring method of oil pipeline leakage based on ultrasonic and optical fiber hybrid sensing technology 2

Dynamic monitoring method of oil pipeline leakage based on u...

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2nd International Symposium on Sensor technology and Control, ISSTC 2023

作者： Li, Yan Gao, Yihang Jilin Communications Polytechnic School of Transportation Information Changchun130015 China Heilongjiang University College of Computer Science and Technology Harbin150006 China

ISBN: (纸本)9798350314465

There is a big positioning error in oil pipeline leakage monitoring, so a dynamic monitoring method of oil pipeline leakage based on ultrasonic and optical fiber hybrid sensing technology is proposed. For a given arbitrary signal, the extreme points of all modal components are extracted;Solve the envelope of the maximum and minimum signal sets, decompose the modal components of the original virtual signal according to the average value of the original signal envelope, construct the dynamic monitoring process of oil pipeline leakage, match and classify, and finally achieve the purpose of locating the leakage point according to the oil pipeline pressure data, and realize the dynamic monitoring of oil pipeline leakage. The experimental results show that this method can quickly and accurately monitor the occurrence of leakage events, achieve the shortest alarm time and the smallest leakage positioning error, and have high positioning accuracy. © 2023 IEEE.

关键词： Optical fibers

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Sparse Recovery Space Time Adaptive Processing Based on Log-Determinant 15

Sparse Recovery Space Time Adaptive Processing Based on Log-...

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15th International Conference on Signal Processing Systems, ICSPS 2023

作者： Chang, Yuqing Du, Xiaolin Jin, Biao Tian, Tuanwei Li, Jianbo School of Computer and Control Engineering Yantai University Yantai China Ocean College Jiangsu University of Science and Technology Zhenjiang China School of Physics and Electronics Henan University Kaifeng China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510675056

Airborne radars usually face non-uniform clutter environments, and it is difficult to obtain enough independent and identical distributed (i.i.d.) training samples, which degrades the clutter suppression performance of space time adaptive processing (STAP). To address the problem, a log-determinant sparse rocovery based STAP (LSR-STAP) method is proposed in this paper. Through the sparse recovery theory of low-rank matrices and the prior knowledge of clutter covariance matrices, the corresponding problem is modeled using the log-determinant (LogDet) approximation of the rank function, and the solution of the resulting nonconvex optimization problem is derived in the framework of the symmetric alternating direction method of multipliers (S-ADMM). The simulation results show the superiority of the proposed method over similar algorithms. © 2024 SPIE.

关键词： Space time adaptive processing

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Dual-Path Downsampling Algorithm Based on HWD-MP

Dual-Path Downsampling Algorithm Based on HWD-MP

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Zilong Wang Jia Bai Qihang Zhang Changbin Shao School of Computer Jiangsu University of Science and Technology Zhenjiang China School of Computer Science Xi'an Polytechnic University Xian China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

In object detection tasks, the quality of downsampling significantly impacts model performance. Conventional downsampling methods, such as max pooling which retains the maximum value within a window, often lead to the loss of critical feature information. This study proposes a plug-and-play dual-path downsampling module, designing the Haar Wavelet-based Downsampling and Max Pooling (HWD-MP) module based on the Haar Wavelet-based Downsampling (HWD) operation. One branch employs max pooling for downsampling, while the another branch utilizes the Haar wavelet transform to decrease the spatial resolution of feature maps while maintaining all the information., thereby addressing the issue of information loss caused by max pooling downsampling and achieving diversity in downsampled features. Experiments on the Yolov7-tiny model demonstrate that this approach can greatly enhance the detection effectiveness, increasing the mAP50 by 2.5% and 1.5% on the RSOD and NWPU VHR-10 remote sensing datasets, respectively.

关键词： Wavelet transforms YOLO Cloud computing Accuracy Computational modeling Big Data applications Data models Spatial resolution Remote sensing Software engineering

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Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

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computers, Materials & Continua 2022年第12期73卷 5299-5314页

作者： K.Pradeep Mohan Kumar Jenifer Mahilraj D.Swathi R.Rajavarman Subhi R.M.Zeebaree Rizgar RZebari Zryan Najat Rashid Ahmed Alkhayyat Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Information Technology School of Engineering and TechnologyKebridehar UniversityKebridehar250Ethiopia Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Computer Network Department Technical College of InformaticsSulaimani Polytechnic UniversitySulaimaniIraq College of Technical Engineering The Islamic UniversityNajafIraq

Recently,smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available *** the advantages of smart cities,security remains a huge challenge to be ***,Intrusion Detection System(IDS)is the most proficient tool to accomplish security in this ***,blockchain exhibits significance in promoting smart city designing,due to its effective characteristics like immutability,transparency,and *** order to address the security problems in smart cities,the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning(PPSF-BODL)*** proposed PPSFBODL model includes the collection of primary data using sensing ***,z-score normalization is also utilized to transform the actual data into useful ***,Chameleon Swarm Optimization(CSO)with Attention Based Bidirectional Long Short TermMemory(ABiLSTM)model is employed for detection and classification of *** is employed for optimal hyperparameter tuning of ABiLSTM *** the same time,Blockchain(BC)is utilized for secure transmission of the data to cloud *** cloud server is a decentralized,distributed,and open digital ledger that is employed to store the transactions in different methods.A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSFBODL model over recent approaches with a maximum accuracy of 97.46%.

关键词： Blockchain smart city security intrusion detection system chameleon swarm optimization deep learning parameter tuning

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Cooperative Maneuvering Decision-Making of Multi-UAVs Based on MADRL-VD 7th

Cooperative Maneuvering Decision-Making of Multi-UAVs Based ...

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Li, Haolin Guo, Zhengyu Lin, Longbin Han, Diru Luo, Delin School of Aerospace Engineering Xiamen University Xiamen361102 China National Key Laboratory of Air-Based Information Perception and Fusion Henan Luoyang471000 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819733354

In order to make UAVs more effective in accomplishing complex combat missions, multi-UAV cooperative combat will become the most effective means. In this paper, we extract the basic elements of UAV combat and under certain assumptions, different flight states as well as action sets are constructed based on the principle of kinematics, and an air combat confrontation simulation platform is established. Under the changing confrontation environment, the Qatten algorithm is designed using the value decomposition method, and the UAVs are trained to take appropriate decision-making actions based on their own local observation situation, so as to enhance the capability of the UAVs’ autonomous cooperative confrontation. Our experiment takes the UAV 2 to 1 as the research scenario, and the results show that in the above scenario our algorithm can present a good fit. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss

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Mathematical Biosciences and Engineering 2024年第4期21卷 5521-5535页

作者： Ran, Zhongnan Jiang, Mingfeng Li, Yang Wang, Zhefeng Wu, Yongquan Ke, Wei Xia, Ling School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Department of Cardiology Beijing Anzhen Hospital Capital Medical University No. 2 Anzhen Road Chaoyang District Beijing100029 China School of Applied Sciences Macao Polytechnic Institute China Department of Biomedical Engineering Zhejiang University Hangzhou310027 China

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively. © 2024 the Author(s).

关键词： Diseases

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Hollow Gradient-Structured Iron-Anchored Carbon Nanospheres for Enhanced Electromagnetic Wave Absorption

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Nano-Micro Letters 2023年第1期15卷 144-160页

作者： Cao Wu Jing Wang Xiaohang Zhang Lixing Kang Xun Cao Yongyi Zhang Yutao Niu Yingying Yu Huili Fu Zongjie Shen Kunjie Wu Zhenzhong Yong Jingyun Zou Bin Wang Zhou Chen Zhengpeng Yang Qingwen Li Key Laboratory of Multifunctional Nanomaterials and Smart Systems Advanced Materials DivisionSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123JiangsuPeople’s Republic of China School of Science Nanchang Institute of TechnologyNanchang 330099JiangxiPeople’s Republic of China Division of Nanomaterials and Jiangxi Key Lab of Carbonene Materials Jiangxi Institute of NanotechnologyNanchang 330200JiangxiPeople’s Republic of China School of Nano-Tech and Nano-Bionics University of Science and Technology of ChinaHefei 230026AnhuiPeople’s Republic of China School of Materials Science and Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798Singapore Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and TechnologySuzhou University of Science and TechnologySuzhou 215009People’s Republic of China School of Mechanical and Power Engineering Nanjing Tech UniversityNanjing 211800People’s Republic of China Henan Key Laboratory of Materials On Deep-Earth Engineering School of Materials Science and EngineeringHenan Polytechnic UniversityJiaozuo 454003People’s Republic of China College of Safety Science and Engineering Xi’an University of Science and TechnologyXi’an 710054People’s Republic of China

In the present paper,a microwave absorber with nanoscale gradient structure was proposed for enhancing the electromagnetic absorption *** inorganic-organic competitive coating strategy was employed,which can effectively adjust the thermodynamic and kinetic reactions of iron ions during the solvothermal *** a result,Fe nanoparticles can be gradually decreased from the inner side to the surface across the hollow carbon *** results reveal that it offers an outstanding reflection loss value in combination with broadband wave absorption and flexible adjustment ability,which is superior to other relative graded distribution structures and satisfied with the requirements of lightweight *** addition,this work elucidates the intrinsic microwave regulation mechanism of the multiscale hybrid electromagnetic wave *** excellent impedance matching and moderate dielectric parameters are exhibited to be the dominative factors for the promotion of microwave absorption performance of the optimized *** strategy to prepare gradient-distributed microwave absorbing materials initiates a new way for designing and fabricating wave absorber with excellent impedance matching property in practical applications.

关键词： Gradient structures Carbon nanospheres Electromagnetic wave absorption Impedance matching

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Latent space knowledge representation enhancement for low resource machine translation

Latent space knowledge representation enhancement for low re...

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2023 International Conference on Intelligent Systems, Communications, and computer Networks, ISCCN 2023

作者： Du, Sihua Liu, Xiaoming Yang, Guan School of Computer Science Zhongyuan University of Technology Zhengzhou450007 China Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou450007 China

ISBN: (纸本)9781510666238

Low-resource machine translation usually uses data augmentation or constrained hidden space representations to improve translation quality, which ignores the knowledge representation divergence between different languages in latent space. We propose a latent space knowledge representation enhancement to improve the translation effect by reducing the divergence of knowledge representation between the source language and target language. Firstly, this method splits knowledge and feature representation from text representation in each language. Then, it promotes the knowledge representation of mutual learning between the two languages to reduce divergence. Next, the optimized knowledge representation and feature representation are re-combined to obtain enhanced text representation. Finally, the enhanced text representation is translated and reconstructed to reduce the differences between knowledge representations further. Through extensive experiments on public low-resource datasets 'English-German' and 'English-Turkish,' The method can achieve better performance on the test set. The results show that the method can effectively improve the ability of low-resource machine translation by reducing the divergence in knowledge representation between languages. © 2023 SPIE.

关键词： Knowledge representation

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Flocculated unclassified tailings settling efficiency improvement by particle collision optimization in the feedwell

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International Journal of Minerals,Metallurgy and Materials 2022年第12期29卷 2126-2135页

作者： Huazhe Jiao Weilin Chen Aixiang Wu Yang Yu Zhuen Ruan Rick Honaker Xinming Chen Jianxin Yu School of Civil Engineering Henan Polytechnic UniversityJiaozuo 454003China School of Civil and Resource Engineering University of Science and Technology BeijingBeijing 100083China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000China China Geological Environmental Monitoring Institute Beijing 100081China State Collaborative Innovation Center of Coal Work Safety and Clean-efficiency Utilization Jiaozuo 454150China Mining Engineering Faculty University of KentuckyLexington 40506America

Efficient thickening of tailings is a prerequisite for the metal mine tailings backfill and surface disposal *** effective collision of ultrafine tailings particles in suspension with flocculant molecules is essential for flocs aggregates formation and *** feeding speed and flocculant adding method will lead to the failure of effective dispersion of flocculant and high particle content in thickener *** this work,the effect of turbulence intensity and flocculant adding method on floc size,strength,and movement characteristics are *** to solve the turbidity increased,a pilot-scale continuous thickening test was carried *** a single particle and multiple flocs of full tailings as the research object,the particle iterative settlement model of flocs was *** influence of turbulence intensity on collision effect is studied by tracking and simulating particle *** results show that in the process of single particle settlement,chaos appears in the iterative process owing to particle adhesion which caused by micro *** the turbulence intensity is 25.99%,the maximum particle size of tailings floc is 6.21 mm and the maximum sedimentation rate is 5.284 cm·s^(−1).The tailings floc presents a multi-scale structure of particle-force chain system when hindered settling,and the interweaving of strong and weak force chains constitutes the topological structure of *** results are applied to a thicker in plant,the flocculant addition mode and feed rate are optimized,and the flocs settling speed and overflow clarity are improved.

关键词： unclassified tailings flocculation settling rate thickener feedwell turbulence intensity flocs micro-structure

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