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Indonesian Agricultural-crops Classification Using Transfer Learning Model

Procedia computer science 2023年 227卷 128-136页

作者： Mahmud Isnan Alam Ahmad Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNet- B0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues.

关键词： Agriculture Classification Crop Deep Learning Transfer learning

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Processing of ECG Signal Using Optimized Feature Selection for the Prediction of Irregular Heart Beat

Processing of ECG Signal Using Optimized Feature Selection f...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Rema, K. Maheswaran, C.P. Noorul Islam Centre for Higher Education Department of Computer Applications Tamilnadu Nagercoil India Sri Krishna College of Technology Department of Artificial Intelligence and Data Science Coimbatore641042 India

ISBN: (纸本)9798350333244

The categorization of Electro Cardio Gram (ECG) trials into significant arrhythmia programs to identify abnormal heartbeats is presented in this study as an effective hybridized method. The most frequently used and recognized detection technology to display heart fitness is the physiological detection utilizing ECG data. Arrhythmia beat categorization also plays a significant part in ECG analysis that is intended to clarify cardiac well-being position when investigating heartbeat. Artificial Neural Networks (ANN) is deployed for the segmentation of input ECG images. The proposed work uses Fourier transform to remove the intrinsic sound of ECG signals during the preprocessing step in order to properly categorize ECG samples into main arrhythmia classifications. Chicken Swarm Optimization (CSO) is employed for the effective feature selection which is followed by CNN based classification. This demonstrates how the suggested ECG classification model is successful in correctly classifying ECG signals for abnormality detection along with IoT based © 2023 IEEE.

关键词： Electrocardiograms

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Temporal Global Re-detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking 12th

Temporal Global Re-detection Based on Interaction-Fusion At...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Ma, Jingyuan Hou, Zhiqiang Han, Ruoxue Ma, Sugang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province Xi’an710121 China

ISBN: (纸本)9783031463075

In long-term visual tracking, target occlusion and out-of-view are common problems that lead to target drift, adding re-detection to short-term tracking algorithms is a general solution. To better handle the problem of target disappearing and reappearing in long-term visual tracking, this paper proposes a temporal global re-detection method based on interaction-fusion attention. Firstly, ResNet50 is used as the feature extraction network to obtain the depth features of the template and the search region. Then, a new interaction-fusion attention is added to extract the connection of different dimensionality of features. Finally, Temporal ROI Align is introduced to select candidate boxes, increasing the use of historical information by re-detection method, and improving the accuracy of target localization. STMTrack algorithm is selected as the short-term tracking algorithm, which works with the proposed re-detection method to construct a long-term tracking algorithm, and experiments are conducted on the UAV123, LaSOT, UAV20L, and VOT2018-LT datasets, and the effectiveness of this re-detection method can be seen from the experimental results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical detection

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Double spatial modulation with transmit antenna group for communication signal transmission 3

Double spatial modulation with transmit antenna group for co...

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3rd International Conference on Electrical, Communication and computer Engineering, ICECCE 2021

作者： Yu, Ping Zhuang, Xiaomei Liu, Hong School of Data Science and Computer Peizheng College of Guangdong China

In this paper, to make use of the idle transmit antennas for the improvement of multiplexing gain and the spectral efficiency, a new design of double spatial modulation (DSM) with transmit antenna group (DSM-TG) is proposed. More specifically, all of transmit antennas are grouped into G groups, each group implements a DSM system independently. Then, by using the vector combiner, G groups of DSM symbol vectors are constructed into a transmitted spatial vector. Furthermore, the average bit error ratio is analyzed. Finally, to make a fair comparison, simulation results are presented for the transmission of same data rate, and show the proposed DSM-TG achieves the better bit error rate (BER) performance gain as compared with other spatial modulation schemes. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Spectrum efficiency

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Can xLLMs Understand the Structure of Dialog? Exploring Multilingual Response Generation in Complex Scenarios

arXiv

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arXiv 2025年

作者： Hu, Zhongtian Cui, Yiwen Li, Ronghan Zhao, Meng Wang, Lifang School of Computer Science and Engineering Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China School of Artificial Intelligence and Big Data Henan University of Technology China

Multilingual research has garnered increasing attention, especially in the domain of dialogue systems. The rapid advancements in large language models (LLMs) have fueled the demand for high-performing multilingual models. However, two major challenges persist: the scarcity of high-quality multilingual datasets and the limited complexity of existing datasets in capturing realistic dialogue scenarios. To address these gaps, we introduce XMP, a high-quality parallel Multilingual(X) dataset sourced from Multi-party Podcast dialogues1. Each sample in the dataset features at least three participants discussing a wide range of topics, including society, culture, politics, and entertainment. Through extensive experiments, we uncover significant limitations in previously recognized multilingual capabilities of LLMs when applied to such complex dialogue scenarios. For instance, the widely accepted multilingual complementary ability of LLMs is notably impacted. By conducting further experiments, we explore the mechanisms of LLMs in multilingual environments from multiple perspectives, shedding new light on their performance in real-world, diverse conversational contexts. © 2025, CC BY.

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Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients

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Intelligent Automation & Soft Computing 2023年第7期37卷 455-470页

作者： Salah Alzghoul Othman Smadi Ali Al Bataineh Mamon Hatmal Ahmad Alamm Biomedical Engineering Department The Hashemite UniversityZarqaJordan Department of Electrical and Computer Engineering Norwich UniversityNorthfieldVermont05663USA Department of Biochemistry and Molecular Biology The Hashemite UniversityZarqaJordan National Centre for Big Data Science and Artificial Intelligence AmmanJordan

Currently,the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis(AS)is made by healthcare professionals based on the patient’s clinical biometric records.A delay in surgical aortic valve replacement(SAVR)can potentially affect patients’quality of *** using ML algorithms,this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following *** study represents a novel approach that has the potential to improve decision-making and,ultimately,improve patient *** analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or were indicated for *** divide the data into two groups:those who died within the first year after SAVR and those who survived for more than one year or were still alive at the last *** then use six different ML algorithms,Support Vector Machine(SVM),Classification and Regression Tree(C and R tree),Generalized Linear(GL),Chi-Square Automatic Interaction Detector(CHAID),Artificial Neural Net-work(ANN),and Linear Regression(LR),to generate predictions for the best timing for *** results showed that the SVM algorithm is the best model for predicting the optimal timing for SAVR and for predicting the post-surgery survival *** optimizing the timing of SAVR surgery using the SVM algorithm,we observed a significant improvement in the survival period after *** study demonstrates that ML algorithms generate reliable models for predicting the optimal timing of SAVR in asymptomatic patients with moderate-to-severe AS.

关键词： Aortic stenosis aortic valve replacement machine learning survival period enhancement artificial intelligence in cardiology

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Addressing the Challenge of Missing Medical data in Healthcare Analytics: A Focus on Machine Learning Predictions for ICU Length of Stay

Addressing the Challenge of Missing Medical Data in Healthca...

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2024 Intermountain Engineering, Technology and Computing, IETC 2024

作者： Karankot, Mahmad Isaq Marceau, Max Glenn, Ethan M. Fowers, Rylan P. Hedges, David M. Sheehey, Bonnie Whitaker, Bradley M. Electrical and Computer Engineering Montana State University BozemanMT United States SelectHealth Analytics/Data Science Department MurrayUT United States History and Philosophy Montana State University BozemanMT United States

ISBN: (纸本)9798350373073

This research investigates the impact of missing data on the performance of machine learning algorithms, with a particular focus on the MIMIC-IV dataset. This project aims to investigate the extent to which missing data negatively impacts the training of machine learning algorithms, and whether demographic groups with a higher proportion of missing data (i.e.,ethnicity) have lower predictive accuracy. Using advanced machine learning and data analysis techniques, our results highlight important considerations related to missing data in medical datasets and provide useful insights for improving predictive modeling and decision support systems in clinical practice offers. © 2024 IEEE.

关键词： Singular value decomposition

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A large-scale study of performance and equity of commercial remote identity verification technologies across demographics 18

A large-scale study of performance and equity of commercial ...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Fatima, Kaniz Schuckers, Michael Cruz-Ortiz, Gerardo Hou, Daqing Purnapatra, Sandip Andrews, Tiffany Neupane, Ambuj Marshall, Brandeis Schuckers, Stephanie Clarkson University Electrical and Computer Engineering PotsdamNY United States Univ. of North Carolina Charlotte School of Data Science CharlotteNC United States United States DataedX Group Llc United States

ISBN: (纸本)9798350364132

As more types of transactions move online, there is an increasing need to verify someone's identity remotely. Remote identity verification (RIdV) technologies have emerged to fill this need. RIdV solutions typically use a smart device to validate an identity document like a driver's license by comparing a face selfie to the face photo on the document. Recent research has been focused on ensuring that biometric systems work fairly across demographic groups. This study assesses five commercial RIdV solutions for equity across age, gender, race/ethnicity, and skin tone across 3,991 test subjects. This paper employs statistical methods to discern whether the RIdV result across demographic groups is statistically distinguishable. Two of the RIdV solutions were equitable across all demographics, while two RIdV solutions had at least one demographic that was in-equitable. For example, the results for one technology had a false negative rate of 10.5% +/- 4.5% and its performance for each demographic category was within the error bounds, and, hence, were equitable. The other technologies saw either poor overall performance or inequitable performance. For one of these, participants of the race Black/African American (B/AA) as well as those with darker skin tones (Monk scale 7/8/9/10) experienced higher false rejections. Finally, one technology demonstrated more favorable but inequitable performance for the Asian American and Pacific Islander (AAPI) demographic. This study confirms that it is necessary to evaluate products across demographic groups to fully understand the performance of remote identity verification technologies. © 2024 IEEE.

关键词： Biometrics

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PDDD-PreTrain:A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

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Plant Phenomics 2023年第2期5卷 313-332页

作者： Xinyu Dong Qi Wang Qianding Huang Qinglong Ge Kejun Zhao Xingcai Wu Xue Wu Liang Lei Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China The School of Physics and Optoelectronic Engineering Guangdong University of TechnologyGuangzhou 510006China National Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural BioengineeringMinistry of EducationGuizhou UniversityGuiyang 550025China

Plant diseases threaten global food security by reducing crop yield;thus,diagnosing plant diseases is critical to agricultural *** intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming,costly,inefficient,and subjective *** a mainstream AI method,deep learning has substantially improved plant disease detection and diagnosis for precision *** the meantime,most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased ***,the commonly used pre-trained models are from the computer vision dataset,not the botany dataset,which barely provides the pre-trained models sufficient domain knowledge about plant ***,this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic *** address this issue,we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease *** addition,we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification,plant disease detection,plant disease segmentation,and other *** extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time,thereby supporting the better diagnosis of plant *** addition,our pre-trained models will be open-sourced at https://***/and Zenodo platform https://***/10.5281/zenodo.7856293.

关键词： diagnosis Plant thereby

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A Mixed-Integer Programming Formulation for a Simplified Model of the Double Row Layout Problem

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Wuhan University Journal of Natural sciences 2023年第5期28卷 433-440页

作者： GUAN Jian LIN Geng FENG Huibin RUAN Zhiqiang Modern Educational Technology Center Minjiang UniversityFuzhou 350121FujianChina College of Mathematics and Data Science Minjiang UniversityFuzhou 350121FujianChina College of Computer and Control Engineering Minjiang UniversityFuzhou 350121FujianChina

The double row layout problem(DRLP)is to assign facilities on two rows in parallel so that the total cost of material handling among facilities is *** it is vital to save cost and enhance productivity,the DRLP plays an important role in many application ***,it is very hard to handle the DRLP because of its complex *** this paper,we consider a new simplified model for the DRLP(SM-DRLP)and provide a mixed integer programming(MIP)formulation for *** continuous decision variables of the DRLP are divided into two parts:start points of double rows and adjustable clearances between adjacent *** former one is considered in the new simplified model for the DRLP with the purpose of maintaining solution quality,while the latter one is not taken into account with the purpose of reducing computational *** evaluate its performance,our SM-DRLP is compared with the model of a general DRLP and the model of another simplified *** experimental results show the efficiency of our proposed model.

关键词： facility layout mixed integer programming double row layout problem production optimization manufacturing design

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