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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Diptho, Rakib Ahammed Jahan, Nusrat Istiyaq, Tahsin Sifat-E-Sadakin Anika, Fairuz Hossain, Muhammad Iqbal Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Polycystic Ovary Syndrome (PCOS) emerges as a prevalent endocrine aberration afflicting women in their reproductive prime. This disorder shows up as a complicated messing up of the production of androgens in the ovaries, which are normally present in small amounts in a woman's body. The hallmark of PCOS lies in its pronounced hormonal imbalance, a feature conspicuously absent in conventional ovarian cysts. Recent studies posit that approximately 15 percent of women of reproductive age grapple with this condition, a substantial contributor to female infertility. Despite its global pervasiveness, the diagnostic conundrum surrounding PCOS persists, posing a formidable challenge. The worldwide discourse on this matter remains inconclusive, and the elusive nature of an accurate diagnosis compounds the predicament. Notably, the complexity of PCOS is exacerbated by its symptomatic overlap with other medical conditions, further confounding the diagnostic process. Our research endeavors are driven by an ardent interest in unraveling the intricacies of this enigmatic syndrome, employing sophisticated models such as Long Short-Term Memory (LSTM), Bidirectional LSTM (BI LSTM), Convolutional Neural Network (CNN) with LSTM, Convolutional Neural Network with Bidirectional LSTM (CNN+BI LSTM), and CNN to illuminate novel insights into this pervasive health challenge, where CNN came up with an accuracy of 97.74%. © 2023 IEEE.

关键词： Androgens Follicles knn algorithm Linear Regression Analysis Machine Learning Otsu threshold PCO biomarkers

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Assessing field scale spatiotemporal heterogeneity in salinity dynamics using aerial data assimilation

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AGRICULTURAL WATER MANAGEMENT 2024年 305卷

作者： Ebrahimi, Saman Khorram, Mahdis Palmate, Santosh Chaganti, Vijaya Ganjegunte, Girisha Kumar, Saurav Arizona State Univ Sch Sustainable Engn & Built Environm 660 S Coll Ave Tempe AZ 85281 USA Texas A&M Agrilife Res El Paso TX 79927 USA Virginia Tech Blacksburg VA 24060 USA

Soil moisture and salinity shifts within the root zone can significantly alter crop yields. Thus, spatiotemporal dynamics of these parameters are essential for precision crop management. Airborne or spaceborne earth observation methods based on vegetation and soil observations have sometimes been used with limited success to indirectly understand parameters like soil salinity. These datasets lack spatiotemporal resolution to discern fieldscale heterogeneities, and estimates' accuracy is poor. A Metropolis-Hasting Markov Chain-Monte Carlo (MCMC) based method was developed to estimate field-scale soil salinity by assimilating estimated evapotranspiration (ET) data obtained from aerial canopy temperature sensing with ET outputs from a one-dimensional soil-water transport model. By aligning the two estimated ET values, we inferred anticipated soil salinity levels in a mature pecan orchard (28,951 m2). Our results aligned closely with in-situ measurements with a spatial cross-correlation more than 0.86 and highlighted the expected heterogeneities and nonlinearities. This research offers an approach to refine the current state-of-the-art crop models by accounting for field scale heterogeneities using remotely sensed data. This assimilation method will pave the way for a more inclusive agricultural system modeling that can infer critical but hard-to-measure soil properties from easier-to-obtain remotely sensed datasets. Though this paper concentrates on aerial observations, we anticipate similar methods can be used for satellite-based imagery, especially those with high spatial, temporal, and spectral resolutions.

关键词： Soil salinity Data assimilation Optically inactive properties Remote sensing knn algorithm Markov Chain-Monte Carlo ET estimation

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An IoT Healthcare System Based on Fog Computing and Data Mining: A Diabetic Use Case

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7924页

作者： Karimi, Azin Razi, Nazila Rezazadeh, Javad Azad Univ North Tehran Branch Fac IT Tehran 1876 Iran Crown Inst Higher Educ CIHE Fac IT Sydney NSW 2060 Australia

The advent of the Internet of Things (IoT) has revolutionized numerous sectors, with healthcare being particularly significant. Despite extensive studies addressing healthcare challenges, two persist: (1) the need for the swift detection of abnormalities in patients under medical care and timely notifications to patients or caregivers and (2) the accurate diagnosis of abnormalities tailored to the patient's condition. Addressing these challenges, numerous studies have focused on developing healthcare systems, leveraging technologies like edge computing, which plays a pivotal role in enhancing system efficiency. Fog computing, situated at the edge of network hierarchies, leverages multiple nodes to expedite system processes. Furthermore, the wealth of data generated by sensors connected to patients presents invaluable insights for optimizing medical care. Data mining techniques, in this context, offer a means to enhance healthcare system performance by refining abnormality notifications and disease analysis. In this study, we present a system utilizing the K-Nearest Neighbor (knn) algorithm and Raspberry Pi microcomputer within the fog layer for a diabetic patient data analysis. The knn algorithm, trained on historical patient data, facilitates the real-time assessment of patient conditions based on past vital signs. A simulation using an IBM SPSS dataset and real-world testing on a diabetic patient demonstrate the system's efficacy. The results manifest in prompt alerts or normal notifications, illustrating the system's potential for enhancing patient care in healthcare settings.

关键词： Internet of Things fog computing data mining knn algorithm healthcare system

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Intelligent Evaluation Framework of English Composition Based on Intelligent algorithm 4th

Intelligent Evaluation Framework of English Composition Base...

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4th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND)

作者： Wang, Lingling Jilin Int Studies Univ Changchun 130117 Jilin Peoples R China

ISBN: (纸本)9783031243516;9783031243523

The English composition intelligent evaluation system is a tool for automatic writing scoring using computer and natural language processing technology. At present, more and more practical English composition intelligent evaluation systems have been put into use, which can reduce a large number of teachers and evaluation agencies. Manual repetition of labor realizes the scale of composition correction. This paper aims to study the intelligent evaluation framework of English composition based on intelligent algorithm. This paper researches and integrates the features of word vector, paragraph vector, part-of-speech vector, and LDA. This paper researches and integrates the characteristics of word vector, paragraph vector, part-of-speech vector, and LDA, and uses the knn semantic algorithm similar to the model to obtain the abstract tag label;The regression model of XGBoost calculates the evaluation scores of English compositions;and uses the summaries of 900 English students as samples to construct an intelligent composition evaluation system. Experiments show that the user satisfaction of the English composition intelligent evaluation framework constructed in this paper has reached 100%, and the mean square error is about 10, the error is small, which meets the needs of intelligent evaluation system.

关键词： Intelligent algorithm English composition Intelligent evaluation knn algorithm

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Image segmentation method for coal particle size distribution analysis

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Particuology 2021年第3期19卷 163-170页

作者： Feiyan Bai Minqiang Fan Hongli Yang Lianping Dong College of Mining Engineering Taiyuan University of TechnologyTaiyuan 030024China

Particle size distribution is extremely important in the coal preparation *** is traditionally analysed by a manual screening method,which is relatively time-consuming and cannot immediately guide *** this paper,an image segmentation method for images of coal particles is *** employs the watershed algorithm,k-nearest neighbour algorithm,and convex shell method to achieve preliminary segmentation,merge small pieces with large pieces,and split adhered particles,*** the automated segmentation using this method with manual segmentation,it is found that the results are *** size distributions obtained by the automated and manual segmentation methods are nearly identical,and the standard deviation is less than 3%,indicating good *** automated image segmentation method provides a new approach for rapidly analysing the size distribution of coal particles with size fractions defined according to consumer requirements.

关键词： Particle size distribution Image segmentation Watershed algorithm using gradient knn algorithm Region merging Convex shell

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Disease Recognition of Maize Leaf Based on knn and Feature Extraction

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年第11期36卷 2257004-2257004页

作者： Li, Yawen Chen, Yuexing Wang, Yang Shangluo Univ Elect Informat & Elect Coll Engn Shangluo 726000 Shaanxi Peoples R China Smart Agr Technol & Applicat Res Ctr Shangluo Shangluo 726000 Shaanxi Peoples R China Shangluo Univ Sch Biomed & Food Engn Shangluo 726000 Shaanxi Peoples R China Shangluo Technol & Res Inst Chinese Med Mat Integ Shangluo 726000 Shaanxi Peoples R China

Maize is one of the main crops in Shangluo. The maize yield and quality are adversely affected by leaf spot and rust. In order to realize the early prevention of maize leaf spot and rust and avoid problems of environmental pollution induced by the conventional chemical reagents, computer vision technology was proposed for disease detection in this research. The algorithm of K-means was used to process the image samples obtained from the test field. The healthy area, disease area and background area were separated. Based on the healthy area and disease area, ten parameters were extracted, including four parameters of color characteristic, four parameters of texture feature and two parameters of shape feature, which were taken as the classification criteria in the classification training by knn algorithm. In total, 200 test image samples (100 samples of leaf spot and 100 samples of maize rust) were sent into the training model for disease identification. The results showed that the algorithm proposed can efficiently and nondestructively identify maize leaf spot and rust based on image segmentation and multi-feature fusion. The method was convenient and environmentally friendly. It can provide technical support for plant protection and can supply research ideas of precise control of crop diseases.

关键词： Parameter extraction leaf spot rust K-means knn algorithm

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The Hourly Energy Consumption Prediction by knn for Buildings in Community Buildings

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BUILDINGS 2022年第10期12卷 1636-1636页

作者： Hong, Goopyo Choi, Gyeong-Seok Eum, Ji-Young Lee, Han Sol Kim, Daeung Danny Kangwon Natl Univ Dept Architectural Engn Samcheok 25913 South Korea Korea Inst Civil Engn & Bldg Technol Dept Bldg Energy Res Goyang 10223 South Korea King Fahd Univ Petr & Minerals KFUPM Architectural Engn Dept Dhahran 31261 Saudi Arabia

With the development of metering technologies, data mining techniques such as machine learning have been increasingly used for the prediction of building energy consumption. Among various machine learning methods, the knn algorithm was implemented to predict the hourly energy consumption of community buildings composed of several different types of buildings. Based on the input data set, 10 similar hourly energy patterns for each season in the historic data sets were chosen, and these 10 energy consumption patterns were averaged. The prediction results were analyzed quantitatively and qualitatively. The prediction results for the summer and fall were close to the energy consumption data, while the results for the spring and winter were higher than the energy consumption data. For accuracy, a similar trend was observed. The values of CVRMSE for the summer and fall were within the acceptable range of ASHRAE guidelines 14, while higher values of CVRMSE for the spring and winter were observed. In sum, the total values of CVRMSE were within the acceptable range.

关键词： hourly energy consumption knn algorithm energy pattern community buildings

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Detection of Diabetics Using Support Vector Machine algorithm in Comparison With K Nearest Neighbour algorithm to Measure Accuracy, Sensitivity and Specificity 14

Detection of Diabetics Using Support Vector Machine Algorith...

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14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Aashiq, Shaik Fayaz Kinol, A. Mary Joy Ramani, Pratibha Saveetha Sch Engn Dept Elect & Commun Engn Chennai Tamil Nadu India Saveetha Univ Saveetha Inst Med & Tech Sci SIMATS Chennai Tamil Nadu India

ISBN: (纸本)9781665460712

The aim of this article is to measure the performance metrics of an accuracy, sensitivity and specificity of the Novel Support Vector Machine (SVM) algorithm for diabetics detection and compared with a K Nearest Neighbour (knn) algorithm. Materials and Methods: A total of 768 samples are collected from diabetics detection datasets found in kaggle. These samples are divided into a training dataset (n=500) and test dataset (n=268). Results: By comparing the Novel Support Vector Machine with knn. The SVM has achieved better accuracy and specificity than the knn and the knn has better Sensitivity than the SVM algorithm. The output values of accuracy, specificity and sensitivity of the SVM are 79%, 93% and 58%. The output values of accuracy, specificity and sensitivity of the knn algorithm are 63%, 39% and 100%. The significance value is p!0.05. The G power is taken as 0.8. Conclusion:The output values of accuracy, specificity and sensitivity of the Novel SVM are 79%, 93% and 58%. The output values of accuracy, specificity and sensitivity of the knn algorithm are 63%, 39% and 100

关键词： Diabetics Detection Novel Support Vector Machine Accuracy Sensitivity Specificity knn algorithm Machine learning

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Optimum NN algorithms Parameters on the UJIIndoorLoc for Wi-Fi Fingerprinting Indoor Positioning Systems 32

Optimum NN Algorithms Parameters on the UJIIndoorLoc for Wi-...

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32nd International Telecommunication Networks and Applications Conference (ITNAC)

作者： Ebaid, Emad Navaie, Keivan Univ Lancaster Sch Comp & Commun Lancaster England

ISBN: (纸本)9781665471039

Wi-Fi fingerprinting techniques are commonly used in Indoor Positioning Systems (IPS) as Wi-Fi signal is available in most indoor settings. In such systems, the position is estimated based on a matching algorithm between the enquiry points and the recorded fingerprint data. In this paper, our objective is to investigate and provide quantitative insight into the performance of various Nearest Neighbour (NN) algorithms. The NN algorithms such as knn are also often employed in IPS. We extensively study the performance of several NN algorithms on a publicly available dataset, UJIIndoorLoc. Furthermore, we propose an improved version of the Weighted knn algorithm. The proposed model outperforms the existing works on the UJIIndoorLoc dataset and achieves better results for the success rate and the mean positioning error.

关键词： Indoor positioning Wi-Fi fingerprinting knn algorithm Wknn algorithm data-driven knn

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Research on the application of situation form judgment in command training based on K nearest neighbor data mining algorithm 2

Research on the application of situation form judgment in co...

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2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC)

作者： Guo, Weilan Zhang, Yujie Wang, Xin Zhang, Jieqiang Wangchao, Luomeng Lu, Fengyun China Peoples Police Univ Fac Grad Studies Langfang 065000 Hebei Peoples R China China Peoples Police Univ Coll Police Command Langfang 065000 Hebei Peoples R China

ISBN: (纸本)9781510655201;9781510655195

Based on k nearest neighbor algorithm, this paper discusses the training method of assistant commander to judge the situation in command training, and focuses on judging the situation level of a certain area in a specific event. Firstly, knn model is introduced. Then, after understanding the principle of the algorithm, python tool is used to calculate, classify and visualize the test samples of 13 regions. Finally, according to the distance between the test sample and the test sample, the level ' risk ' of the test area X is calculated, and then a reference training method is proposed for the training of decision-making ability in command training to assist command training.

关键词： knn algorithm command training training methods decision making

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