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Detection system of dead and sick chickens in large scale farms based on artificial intelligence

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第5期18卷 6117-6135页

作者： Bao, Yiqin Lu, Hongbing Zhao, Qiang Yang, Zhongxue Xu, Wenbin Nanjing XiaoZhuang Univ Coll Informat Engn Nanjing 211171 Peoples R China Nanjing Univ Coll Software Nanjing 210093 Peoples R China Schulich Sch Business Dept Informat Syst Toronto ON 416647 Canada Nanjing Huazhu Ind Intelligent Equipment Co Ltd Nanjing 211175 Peoples R China

With the continuous enrichment of scientific and technological means, the production of most chicken farms has been able to achieve automation, but for the dead and sick chickens in the farm, there is no automatic monitoring step, only through continuous manual inspection and discovery. In the face of this problem, there are many solutions to identify dead and sick chickens through sound and image, but they can not achieve the ideal effect. In this paper, a sensor detection method based on artificial intelligence is proposed. This method 1) The maximum displacement of chicken activity is measured by fastening a foot ring on each chicken, and the three-dimensional total variance is designed and calculated to represent the chicken activity intensity. 2) The detection terminal collects the sensing data of foot ring through ZigBee network. 3) The state of chicken (dead chicken and sick chicken) can be identified by machine learning algorithm. This method of artificial intelligence combined with sensor network not only has high recognition rate, but also can reduce the operation cost. The practical results show that the accuracy of the system to identify dead and sick chickens is 95.6%, and the cost of the system running for 4 years can be reduced by 25% compared with manual operation.

关键词： artificial intelligence three dimensional total variance machine learning classification algorithm sensor networks

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A Pruning Optimized Fast Learn plus plus NSE algorithm

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IEEE ACCESS 2021年 9卷 150733-150743页

作者： Chen, Yong Zhu, Yuquan Chen, Haifeng Shen, Yan Xu, Zhao Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China China Unicom Internet Things Co Ltd Nanjing 210014 Jiangsu Peoples R China Jiangsu Univ Dept Informat Management & Informat Syst Zhenjiang 212013 Jiangsu Peoples R China Nanjing Longyuan Microelect Technol Co Ltd Nanjing 211106 Jiangsu Peoples R China

Due to the large number of typical applications, it is very important and urgent to study the fast classification learning of accumulated big data in nonstationary environments. The newly proposed algorithm, named Learn++.NSE, is one of the important research results in this research field. And a pruning version, named Learn++.NSE-Error-based, was given for accumulated big data to improve the learning efficiency. However, the studies have found that the Learn++.NSE-Error-based algorithm often encounters a situation that the newly generated base classifier is pruned in the next integration, which reduces the accuracy of the ensemble classifier. The newly generated base classifier is very important in the next ensemble learning and should be retained. Therefore, the two latest base classifiers are reserved without being pruned, and a new pruning algorithm named NewLearn++.NSE-Error-based was proposed. The experimental results on the generated dataset and the real-world dataset show that NewLearn++.NSE-Error-based can further improve the accuracy of the ensemble classifier under the premise of obtaining the same time complexity as Learn++.NSE algorithm. It is suitable for fast classification learning of long-term accumulated big data.

关键词： classification algorithms Big Data Error analysis Detection algorithms Data models Batch production systems Probability distribution Ensemble learning nonstationary environment classification algorithm big data mining

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A classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

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WATER 2022年第4期14卷 542页

作者： Kim, Jongchan Jonoski, Andreja Solomatine, Dimitri P. IHE Delft Inst Water Educ Hydroinformat Chair Grp NL-2611 AX Delft Netherlands K Water Human Resources Dev Inst Daejeon 34350 South Korea Delft Univ Technol Water Resources Sect NL-2628 CD Delft Netherlands Water Problems Inst RAS Gubkina 3 Moscow 117971 Russia

Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectively. The dataset was classified into groups consisting of two, three, or four classes based on cyanobacterial cell density after a week, which was used as the target variable. We developed 96 machine learning models for Chilgok weir using four classification algorithms: k-Nearest Neighbor, Decision Tree, Logistic Regression, and Support Vector Machine. In the modeling methodology, we first selected input features by applying ANOVA (Analysis of Variance) and solving a multi-collinearity problem as a process of feature selection, which is a method of removing irrelevant features to a target variable. Next, we adopted an oversampling method to resolve the problem of having an imbalanced dataset. Consequently, the best performance was achieved for models using datasets divided into two classes, with an accuracy of 80% or more. Comparatively, we confirmed low accuracy of approximately 60% for models using datasets divided into three classes. Moreover, while we produced models with overall high accuracy when using logCyano (logarithm of cyanobacterial cell density) as a feature, several models in combination with air temperature and NO3-N (nitrate nitrogen) using two classes also demonstrated more than 80% accuracy. It can be concluded that it is possible to develop very accurate classification-based machine learning models with two features related to cyanobacterial blooms. This proved that we could make efficient and effective models with a low number of inputs.

关键词： cyanobacterial blooms machine learning classification algorithm feature selection imbalanced dataset oversampling

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An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction

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MEDICAL ENGINEERING & PHYSICS 2024年 130卷 104213页

作者： Zhu, Lei Wang, Wentao Huang, Aiai Ying, Nanjiao Xu, Ping Zhang, Jianhai Hangzhou Dianzi Univ Sch Automat Hangzhou 310000 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci Hangzhou 310000 Peoples R China Key Lab Brain Machine Collaborat Intelligence Zhej Hangzhou 310018 Peoples R China

Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for detecting epilepsy, EEG is currently considered one of the most effective analytical tools due to the abundant information it provides about brain activity. The aim of this study is to explore potential time-frequency and channel features from multi-channel epileptic EEG signals and to develop a patientspecific seizure prediction network. In this paper, an epilepsy EEG signal classification algorithm called Channel Recurrent Criss-cross Attention Network (CRCANet) is proposed. Firstly, the spectrograms processed by the short-time fourier transform is input into a Convolutional Neural Network (CNN). Then, the spectrogram feature map obtained in the previous step is input into the channel attention module to establish correlations between channels. Subsequently, the feature diagram containing channel attention characteristics is input into the recurrent criss-cross attention module to enhance the information content of each pixel. Finally, two fully connected layers are used for classification. We validated the method on 13 patients in the public CHB-MIT scalp EEG dataset, achieving an average accuracy of 93.8 %, sensitivity of 94.3 %, and specificity of 93.5 %. The experimental results indicate that CRCANet can effectively capture the time-frequency and channel characteristics of EEG signals while improving training efficiency.

关键词： EEG Epilepsy prediction classification algorithm Spectrogram Short-time fourier transform Attention

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A Hybrid Machine Learning Framework for Predicting Students' Performance in Virtual Learning Environment

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2021年第24期16卷 255-272页

作者： Evangelista, Edmund Zayed Univ Abu Dhabi Campus Abu Dhabi U Arab Emirates

Virtual Learning Environments (VLE), such as Moodie and Blackboard, store vast data to help identify students' performance and engagement. As a result, researchers have been focusing their efforts on assisting educational institutions in providing machine learning models to predict at-risk students and to improve their performance. However, it requires an efficient approach to construct a model that can ultimately provide accurate predictions. Consequently, this study proposed a hybrid machine learning framework to predict students' performance using eight classification algorithms and three ensemble methods (Bagging, Boosting, Voting) to determine the best-performing predictive model. In addition, this study used filter-based and wrapper-based feature selection techniques to select the best features of the dataset related to students' performance. The obtained results reveal that the ensemble methods recorded higher predictive accuracy when compared to single classifiers. Furthermore, the accuracy of the models improved due to the feature selection techniques utilized in this study.

关键词： machine learning Weka predictive model ensemble student performance prediction classification algorithm virtual learning

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Thyroid Disease Prediction Using XGBoost algorithms

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Journal of Mobile Multimedia 2022年第3期18卷 917-934页

作者： Sankar, S. Potti, Anupama Naga Chandrika, G. Ramasubbareddy, Somula Department of CSE Sona College of Technology Salem India Department of CSE Sree Dattha Institute of Engineering and Science Hyderabad India Department of IT VNRVJIET Hyderabad India

Nowadays, thyroid disease is increasing rapidly all over the world. Significantly, one out of ten people is affected by the thyroid in India. In recent years, many researchers have done various research works on thyroid disease detection. Therefore, the early stage of thyroid disease prediction is difficult to protect and avoid the worst health condition. In this regard, the machine learning plays a crucial role to detect the disease accurately. We consider the UC Irvin knowledge discovery dataset. So, this paper proposes the XGBoost algorithm to predict thyroid disease accurately. The best features are selected using XGBoost function. The proposed XGBoost algorithm’s efficacy is compared to decision tree, logistic regression, k-Nearest Neighbor (kNN) methods. The performance of all four algorithms is compared and analyzed. It is observed that the accuracy of the XGBoost algorithm increases by 2% than the KNN algorithm. © 2022 River Publishers.

关键词： classification algorithm Disease prediction Hormone K-nearest neighbor Machine leaning Thyroid disease XGBoost

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A Review Paper on Machine Learning Techniques and Its Applications in Health Care Sector 1

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2nd International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Gautam, Priya Dehraj, Pooja Noida Inst Engn & Technol Greater Noida India

ISBN: (数字)9789811925412

ISBN: (纸本)9789811925412;9789811925405

Healthcare data analysis has seen a significant rise in recent years and thus becoming a prominent research field. Healthcare data types include Sensor data, Clinical data, and Omics data. Clinical data are generally collected whereas a patient is being treated whereas multiple wearable and wireless sensor devices are used for collecting the Sensor data. Omics data includes transcriptome, genome, and proteome data types, all of which are complicated and high-dimensional. Since the data type varies and manually dealing with the raw data becomes a challenging task. This is where Machine Learning emerges as a significant tool for analyzing the data. To precisely forecast the healthcare data results, Machine Learning utilizes varied statistical techniques and advanced algorithms. For analysis ML utilizes different algorithms such as Reinforcement Learning, Supervised Learning, and Unsupervised Learning. This paper discusses the Machine learning algorithms of many forms that can be used to analyse and survey enormous volumes of healthcare data.

关键词： classification algorithm Disease diagnostics Healthcare data Machine learning

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Classifying multiclass relationships between ASes using graph convolutional network

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Frontiers of Engineering Management 2022年第4期9卷 653-667页

作者： Songtao PENG Xincheng SHU Zhongyuan RUAN Zegang HUANG Qi XUAN Institute of Cyberspace Security College of Information EngineeringZhejiang University of TechnologyHangzhou 310023China Department of Electrical Engineering City University of Hong KongHong KongChina

Precisely understanding the business relationships between autonomous systems(ASes)is essential for studying the Internet *** date,many inference algorithms,which mainly focus on peer-to-peer(P2P)and provider-to-customer(P2C)binary classification,have been proposed to classify the AS relationships and have achieved excellent ***,business-based sibling relationships and structure-based exchange relationships have become an increasingly nonnegligible part of the Internet market in recent *** algorithms are often difficult to infer due to the high similarity of these relationships to P2P or P2C *** this study,we focus on multiclassification of AS relationship for the first *** first summarize the differences between AS relationships under the structural and attribute features,and the reasons why multiclass relationships are difficult to be *** then introduce new features and propose a graph convolutional network(GCN)framework,AS-GCN,to solve this multiclassification problem under complex *** proposed framework considers the global network structure and local link features *** on real Internet topological data validate the effectiveness of our method,that is,*** proposed method achieves comparable results on the binary classification task and outperforms a series of baselines on the more difficult multiclassification task,with an overall metrics above 95%.

关键词： autonomous system multiclass relationship graph convolutional network classification algorithm Internet topology

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Motor Imagery Intention Recognition Based on Common Spatial Pattern for Manipulator Grasping 15th

Motor Imagery Intention Recognition Based on Common Spatial ...

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15th International Conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

作者： Li, Wenjie Xu, Jialu Yan, Xiaoyu Lin, Chengyu Fu, Chenglong Shenzhen Foreign Languages Sch Shenzhen Peoples R China Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen Peoples R China

ISBN: (纸本)9783031138225;9783031138218

With further development of brain-computer interface (BCI) technology and the BCI medicine field, people with movement disorders can be treated by using artificial limbs or some external devices such as an exoskeleton to achieve sports function rehabilitation. At present, brain-computer interfaces (BCI) based on motor imagination mainly have problems such as accuracy that need to be improved. The key to solving such problems is to extract high-quality EEG signal features and reasonably select classification algorithms. In this paper, the efficient decoding of dichotomy EEG intentions is verified by combining features such as Common Spatial Pattern (CSP) with different classifiers in the PhysioNet public data set. The results show that: CSP is used as the feature, and the support vector machine (SVM) has the best classification effect. The highest classification accuracy of five subjects reaches 87% and the average accuracy reaches 80%. At the same time, the CSP-SVM algorithm was used in the qbrobotics manipulator to conduct a grasping experiment to verify the real-time performance and effectiveness of the algorithm. This provides a new solution for the brain-computer interface to control robotic auxiliary equipment, which is expected to improve the daily life of the disabled.

关键词： Brain-computer interfaces Motor imagery Feature extraction Common spatial pattern classification algorithm

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Development of a deep learning system for preventive detection of heart failure due to pulmonary disease 11

Development of a deep learning system for preventive detecti...

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11th International Conference on Software Process Improvement (CIMPS) - Applications in Software Engineering

作者： Felix Jimenez, Axel Frederick Ibarra Belmonte, Isaul Sanchez Lee, Vania Stephany Jaramillo Avila, Uziel Parra Gonzalez, Ezra Federico Natl Polytech Inst Comp Syst Engn Zacatecas Zacatecas Mexico Ctr Res Math Zacatecas Zacatecas Mexico Ctr Res Math Dept Comp Sci Zacatecas Zacatecas Mexico

ISBN: (纸本)9798350398960

In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.

关键词： heart failure pulmonary conditions condition prediction preventive diagnosis classification algorithm

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