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Early Diabetes prediction with optimal feature selection using ML based Prediction Framework

Early Diabetes prediction with optimal feature selection usi...

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international conference on Signal Processing and Communication (ICSPC)

作者： G. R. Ashisha X. Anitha Mary H. Mohamed Ashif I. Karthikeyan J. Roshan Electronics and Instrumentation Engineering Karunya Institute of Technology and Sciences Coimbatore India Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore India Artificial Intelligence and Data Science Karunya Institute of Technology and Sciences Coimbatore India

the notable developments in healthcare sciences and biotechnology have led to a significant increase in information technology, for instance, clinical data and high efficiency genetic information, produced from intensive Electronic Medical data. the use of data mining and machine learning techniques are essential in the biomedical sciences to distinctly transform all accessible health records into useful knowledge. Early detection of diseases like diabetes is crucial as the growing number of people with diabetes is rising rapidly. According to an international Diabetes Federation report, the number of diabetic cases globally is expected to reach 642 million by 2040. this growing disease requires a lot of attention. machine learning has quickly advanced, and many facets of medical health have benefited from its use. Due to their capacity for prediction, machine learning algorithms are presently significant in the healthcare industry. the major goal of this work is to design a model that can more accurately predict patients’ diabetes. the research work analyzed the hospital clinical examination data obtained from Sree Guru Hospital, Kanyakumari district, TamilNadu. To improve the accuracy of the system, imbalanced data in the real time dataset were eliminated using oversampling technique. Extra Trees classifier model, and Random Forest (RF) was used to predict diabetes. the result of this work shows that prediction of diabetes with the Extra Trees classifier had the highest accuracy of 98%. Finally, this work effectively determines the pervasiveness and detection of diabetes.

关键词： machine learning algorithms Hospitals Transforms Predictive models Signal processing Real-time systems Diabetes

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Survey on Sentiment Analysis of User Reviews 4

Survey on Sentiment Analysis of User Reviews

引用

4th international conference on Innovative Computing (ICIC)

作者： Naseem, Saba Mahmood, Toqeer Asif, Muhammad Rashid, Junaid Umair, Muhammad Shah, Mohsin Natl Text Univ Dept Comp Sci Faisalabad Pakistan Lahore Garrison Univ Dept Comp Sci Lahore Pakistan Air Univ Islamabad Dept Comp Sci Kamra Campus Kamra Pakistan Univ Lahore Dept Software Engn Lahore Pakistan Hazara Univ Dept Informat Technol Mansehra Pakistan

ISBN: (纸本)9781665400916

User's sentiments or opinions stated on any website or app have enormous impact on the product sellers, customers, and readers. the formless data from the sites or apps is required to be explored and analyzed. To do this, sentiment analysis has familiar major interest. Sentiment Analysis (SA) is sometimes described as archeology or opinion mining as a research field to analyze people's feelings or ideas about organizations such as topics, events, individuals, issues, services, products, organizations, and their characteristics. As the number of reviews rapidly increases, online reviews' sentiment analysis has become a trending topic in research. In this paper, survey is done which highlights the latest research about the implementation of machine learning and deep learning models like support vector machine (SVM), linear regression (LR), Naive Bayes (NB), convolutional neural networks (CNN), Long short-term memory (LSTM), deep neural networks (DNN), and many others proposed for resolving various challenges in sentiment analysis such as product and app(s) review analysis, textual and visual analysis, classification of sentiments, and cross lingual problems.

关键词： Sentiment analysis opinion mining review analysis CNN DNN SVM LR NB LSTM machine learning Deep learning

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Attentive Visual Semantic Specialized Network for Video Captioning 25

Attentive Visual Semantic Specialized Network for Video Capt...

引用

25th international conference on pattern recognition (ICPR)

作者： Perez-Martin, Jesus Bustos, Benjamin Perez, Jorge Univ Chile Dept Comp Sci Millennium Inst Fdn Res Data Santiago Chile

ISBN: (纸本)9781728188089

As an essential high-level task of video understanding topic, automatically describing a video with natural language has recently gained attention as a fundamental challenge in computer vision. Previous models for video captioning have several limitations, such as the existence of gaps in current semantic representations and the inexpressibility of the generated captions. To deal with these limitations, in this paper, we present a new architecture that we call Attentive Visual Semantic Specialized Network (AVSSN), which is an encoder-decoder model based on our Adaptive Attention Gate and Specialized LSTM layers. this architecture can selectively decide when to use visual or semantic information into the text generation process. the adaptive gate makes the decoder to automatically select the relevant information for providing a better temporal state representation than the existing decoders. Besides, the model is capable of learning to improve the expressiveness of generated captions attending to their length, using a sentence-length-related loss function. We evaluate the effectiveness of the proposed approach on the Microsoft Video Description (MSVD) and the Microsoft Research Video-to-Text (MSR-VTT) datasets, achieving state-of-the-art performance with several popular evaluation metrics: BLEU-4, METEOR, CIDEr, and ROUGE(L).

关键词： Specialized long-short term memory (S-LSTM) adaptive attention teacher forcing video captioning

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Performance Comparison of Adaptive Neuro Fuzzy Inference System and Support Vector machine Algorithm in Balanced and Unbalanced Multiclass data Classification 4

Performance Comparison of Adaptive Neuro Fuzzy Inference Sys...

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4th IEEE international conference on Computer and Informatics Engineering (IC2IE)

作者： Saputra, Muhammad Irfan Budiman, Irwan Kartini, Dwi Nugrahadi, Dodon Turianto Faisal, Mohammad Reza Lambung Mangkurat Univ Comp Sci Banjarbaru Indonesia

ISBN: (纸本)9781665442886

data is a record collection of facts. At first the data in the real world were largely unbalanced. Although, the existence of data on fewer categories is much more important to know data on more categories. However, there are some balanced data. this balanced data is the possibility of a ratio of 1:1 in which, the data in the dataset is the same. In this study, using the ANFIS algorithm and SVM to see affected performance on balanced and imbalanced data with multiclass. data is taken from the UCI machine learning named Iris dataset and Wine dataset. there are four step taken from this research which is selection, preprocessing, data mining, and conclusion. From the research conducted using SVM and ANFIS, it is known that the SVM method on the Wine dataset has an accuracy of 96.6 percent and the ANFIS method on the Iris dataset has an accuracy of 94.7 percent.

关键词： ANFIS SVM balanced data imbalanced data multiclass

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An Efficient Particle YOLO Detector for Urine Sediment Detection 4th

An Efficient Particle YOLO Detector for Urine Sediment Dete...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Chen, Zejian Hu, Rong Chen, Fukun Fan, Haoyi Ching, Fum Yew Li, Zuoyong Su, Shimei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China School of Electrical Engineering Zhengzhou University Henan Zhengzhou450001 China

ISBN: (纸本)9783031201011

Urine sediment detection is an essential aid in assessing kidney health. Traditional machine learning approaches treat urine sediment particle detection as an image classification task, segmenting particles for detection based on information such as edges or thresholds. However, the segmentation of sediment particles is complex due to the low contrast and weak edge characteristics of urine sediment images. In this paper, we consider urine sediment particle detection as a object detection task and propose the YOLOv5s-CBL, a detector dedicated to particle detection. Specifically, to mitigate the impact of background noise on detection accuracy, we inherit CBAM on the YOLOv5s model to help the network filter useless noise information and find regions of interest to extract target features. then, we expand the original three-scale feature layer to improve the sensitivity of the model to larger-scale target sediment particles. Finally, we use the BiFPN structure instead of the original PANet combined with the FPN structure, which can more effectively fuse multiple different scales of information to improve the detection performance of the model. We compared state-of-the-art methods on two real-world datasets, and the experimental results demonstrated the effectiveness of YOLOv5s-CBL. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Document Detection using Probabilistic Principal Component Analysis-Based Naïve Bayes Model

Document Detection using Probabilistic Principal Component A...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： T. Elavarasi R. Nagarajan Department of computer and information science Annamalai University

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Document detection is where algorithms are trained to detect and classify document types such that automating sorting and data extraction becomes more efficient in document management. Common limitations of existing methods include reliance on large volumes of labelled training data, sensitivity to differences in document formats, and problems with low-quality images. therefore, this research performs the document detection using machine learning methodology. Initially, the data is manually collected from online sources in the form of documents together with their categories. these collected data are subjected to the pre-processing using the log-linear lemmatization approach. the features are extracted from these pre-processed data using the probabilistic Principal Component Analysis (PCA) approach. Finally, the document detection is accomplished using the Naive Bayes (NB) model that produces the final outcome. Simulation findings revealed that the proposed Probabilistic PCA-NB model returns better results than the other considered conventional methods respectively. the proposed Probabilistic PCA-NB for the document detection model is 0.62%, 0.72%, 0.71%, and 1.59% better than the other considered traditional methods in terms of accuracy, precision, sensitivity, and specificity respectively.

关键词： Analytical models Accuracy Computational modeling machine learning Sensitivity and specificity Probabilistic logic Feature extraction Bayes methods data mining Principal component analysis

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A component search method by feature Mixing based Continual learning 23

A component search method by feature Mixing based Continual ...

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Proceedings of the 2023 4th international conference on machine learning and Computer Application

作者： Xiaoyu Fang Liu Yuan School of Computer Science Shanxi Normal University China

ISBN: (纸本)9798400709449

Component platforms are widely used in developing large web and mobile applications to improve development efficiency and test coverage. Searching for the appropriate component in the component platform and introducing it into the project for use makes development efficient and reduces redundant code. machine learning methods are now widely used in large component platforms to enhance automatic retrieval or identification. However, current machine learning models are less accurate in identifying components or icons with differences due to the difficulty of collecting a large amount of label data in online environments that can help learning. To address this problem, this paper improves the feature mixing method in active learning and embeds it into a classical continuous learning model to solve the problem of accuracy degradation when the model handles discrepant tasks and tasks with limited training data. the model proposed in this paper ensures that the accuracy and performance of the model do not degrade significantly when facing a series of tasks with less data in the dataset after training on the first few tasks. Compared with large neural network recognition tasks, the improved continuous learning model is more lightweight and can learn multiple tasks using a single model. Easy to use when combined with front-end engineering and can be added to projects through dependency libraries. Compared to various classical benchmark models in continual learning, the accuracy of the proposed model can be improved by about fifteen percent.

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Ensemble Classification for Human Activity recognition with Feature Reduction

Ensemble Classification for Human Activity Recognition with ...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Dinesh Kumar Vishal Bharti Department of Computer Science and Engineering Chandigarh University Mohali India

Recognizing human action is an efficient method for identifying, deciphering, and identifying human patterns and activities. the hardest part is to obtain data about human activities. the ability to recognize another person's behaviours is the primary difficulty that existed prior to the methodical disciplines of artificial intelligence and machine learning. the technique for recognizing human activity is put into action in a variety of steps, including pre-processing the data, extracting the attributes, and classifying the data. this study suggests a hybrid version for recognizing human behavior that combines KMC, Principal Component Analysis, and several algorithms that are combined using voting mechanism. Python is used to test the methodology that is presented, and several metrics like accuracy, precision, and recall are taken into account to study the outcomes.

关键词： Measurement machine learning algorithms Clustering algorithms machine learning Classification algorithms Behavioral sciences data mining

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A deep learning based fracture detection in arm bone X-ray images 4

A deep learning based fracture detection in arm bone X-ray i...

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4th international conference on Multimedia Analysis and pattern recognition, MAPR 2021

作者： Nguyen, Hoai Phuong Hoang, thi Phuong Nguyen, Huy Hoang Hanoi University of Science and Technology School of Electronics and Telecommunications Hanoi Viet Nam

ISBN: (纸本)9781665419109

A large number of arm fracture-related injuries are reported in hospitals and clinics around the world. In this paper, we propose a novel deep learning based fracture detection in arm bone X-ray images. First, we preprocess the Xray image by using an algorithm that is a combination of the YOLACT++ for image segmentation and Contrast Limited Adaptive Histogram Equalization for image contrast enhancement. then, YOLOv4 is trained on a small dataset with four data augmentation techniques to identify and locate the position of bone fracture on X-ray images. the topmost result obtained is 81.91% by using our proposed method. Experimental results also confirm that our method outperforms the Faster-RCNN based solution while implementing on the small dataset. © 2021 IEEE.

关键词： Image segmentation

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Automatic Disease Classification of Paddy Leaf Images Using Deep learning Algorithms

Automatic Disease Classification of Paddy Leaf Images Using ...

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international conference on Signal Processing and Communication (ICSPC)

作者： Durgadevi Velusamy A Mounika D Ragulraj K Vigneshwaran Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur Tamilnadu India

Agriculture is considered one of the primary sectors in India. Especially rice is the primary stable food widely cultivated and consumed by 50% of the Indian population. Paddy crop is susceptible to several diseases like other crops that indirectly affect agricultural development and economic loss. To address this limitation, in this study, we have applied deep learning algorithms for the recognition and classification of diseases at an earlier stage. the paddy plant disease diagnosis is made by applying image processing techniques to identify the changes in leaves and its texture. We have used pre-processing techniques for segmentation, feature extraction and classification of disease. First, we have to perform data augmentation to improve the number of images in the dataset. Second, the segmentation of plant leaves are performed to identify the test infected region. then, we applied CNN algorithm to extract features from the data. After feature extraction, we trained different classification algorithms to recognize and classify the images into disease and non-disease images. the application of machine learning and deep learning algorithms helps in the precise location and identification of infectious leaves that help farmers to use automated methods for disease diagnosis.

关键词： Deep learning Support vector machines Image segmentation machine learning algorithms Signal processing algorithms Crops Feature extraction

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