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Early Diabetes prediction with optimal feature selection using ML based Prediction Framework 4

Early Diabetes prediction with optimal feature selection usi...

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4th international conference on Signal Processing and Communication, ICSPC 2023

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

ISBN: (纸本)9798350300772

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. © 2023 IEEE.

关键词： Forecasting

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theoretical Evaluation of Ensemble machine learning Techniques 5

Theoretical Evaluation of Ensemble Machine Learning Techniqu...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Shah, Milind Kantawala, Harsh Gandhi, Kinjal Patel, Rohini Patel, Kinjal A. Kothari, Ankita Department of Computer Science and Engineering Gujarat Vadodara India Department of Computer Engineering G H Patel College of Engineering and Technology Gujarat Vallabh-Vidhyanagar India Gujarat Law Society University Faculty of Computer Applications and Information Technology Gujarat Ahmedabad India

ISBN: (纸本)9781665474672

the use of ensemble techniques is widely recognized as the most advanced approach to solving a variety of problems in machine learning. these strategies train many models and combine the results from all of those models in order to enhance the predictive performance of a single model. During the period of the last several years, the disciplines of artificial intelligence, pattern recognition, machine learning, neural networks, and data mining have all given a considerable consideration to the concept of ensemble learning. Ensemble learning has shown both effectiveness and usefulness across a broad range of problem domains and in significant real-world applications. Ensemble learning is a technique thatinvolves the construction of many classifiers or a group of base learneis and the merging of their respective outputs in order to decrease the total variance. When compared to using only one classifier or one base learner at a time, the accuracy of the results achieved by combining numerous dassifiers or the set of base learners is greatly improved. It has been shown that the use of ensemble methods may increase the predicted accuracy of machine learning models for a range of tasks, including classification, regression, and the identification of outliers. this study will discuss about ensemble machine learning techniques and its various methods such as bagging, boosting, and stacking. finally, all the factors involved in bagging, boosting, and stacking are compared. © 2023 IEEE.

关键词： Adaptive boosting

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Crime Hotspot Detection using Optimized K-means Clustering and machine learning Techniques 4

Crime Hotspot Detection using Optimized K-means Clustering a...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Bonam, Janakiramaiah Burra, Lakshmi Ramani Susheel, G Sai Venkata Naga Sudarshan Narendra, Kadiyala Sandeep, MacHarla Nagamani, Gali Pvp Siddhartha Institute of Technology Department of CSE APVijayawada Kanuru India Koneru Lakshmaiah Education Foundation Department of CSE AP Vaddeswaram India

ISBN: (纸本)9798350300093

Over the world, crimes are becoming increasingly complicated and technologically advanced. It has become essential to prioritize actions targeting crime categories within every area. Nowadays, people are more concerned and stressed by the unprecedented increase in city crimes and violations. Large numbers of crimes are perpetrated frequently each day. Crime data analysts can aid law enforcement officials in discovering criminals more quickly. Crime analysis is an organized method of categorizing locations into crime hotspots and non-hotspot zones. the dataset utilized is the Kaggle-obtained UCI Crime and Communities dataset. the algorithm can categorize the areas into hotspots and non-hotspot areas by examining crime records and associated variables. the suggested system uses data mining and machine learning techniques, such as optimized K-means clustering and the elbow approach, to explore datasets and analyze crimes committed. three classification algorithms - Decision Tree Algorithm, Support Vector machine, and Random Forest Algorithm are included in the model. they are trained on the dataset used to determine if an area is a hotspot based on the criteria that influence the occurrence of crimes in a region. © 2023 IEEE.

关键词： Support vector machines

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PRNU-Net: a Deep learning Approach for Source Camera Model Identification based on Videos Taken with Smartphone 26

PRNU-Net: a Deep Learning Approach for Source Camera Model I...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Akbari, Younes Almaadeed, Noor Al-Maadeed, Somaya Khelifi, Fouad Bouridane, Ahmed Qatar Univ Dept Comp Sci & Engn Doha Qatar Northumbria Univ Dept Comp & Informat Sci London England Univ Sharjah Ctr Data Analyt & Cybernet Sharjah U Arab Emirates

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recent advances in digital imaging have meant that every smartphone has a video camera that can record high-quality video for free and without restrictions. In addition, rapidly developing Internet technology has contributed significantly to the widespread distribution of digital video via web-based multimedia systems and mobile applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital video has become affordable nowadays, security issues have become threatening and have spread worldwide. One of the security issues is the identification of source cameras on videos. Generally, two common categories of methods are used in this area, namely Photo Response Non-Uniformity (PRNU) and machine learning approaches. To exploit the power of both approaches, this work adds a new PRNU-based layer to a convolutional neural network (CNN) called PRNU-Net. To explore the new layer, the main structure of the CNN is based on the MISLnet, which has been used in several studies to identify the source camera. the experimental results show that the PRNU-Net is more successful than the MISLnet and that the PRNU extracted by the layer from low features, namely edges or textures, is more useful than high and mid-level features, namely parts and objects, in classifying source camera models. On average, the network improves the results in a new database by about 4%.

关键词： Deep learning Video on demand Social networking (online) databases Feature extraction Cameras Propagation losses

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Predictive Analysis Techniques of Disease using machine learning 4

Predictive Analysis Techniques of Disease using Machine Lear...

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4th international conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2022

作者： Naaz, Rohaila Karunakaran, V. Teerthanker Mahaveer University Department of Computing Science and IT Moradabad India Jain University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665456357

Electronic data have collected because of the health care area's boundless reception of PC based innovation. the primary goal of this essay is to discuss data mining's application to the provision of medical care. these data mining methods can also be applied in a number of examination and educational settings. the quickest arising area of clinical science is the Smart Health Prediction System. One area of computer science called data mining uses the information already available in the clinical field to estimate the event of diseases. We can learn from groups of large datasets by extracting new patterns using database management and machine learning tools. In this research paper, we examine different algorithmic data mining strategies that have been applied to the prediction of illness. Health organizations frequently use data mining to categorize diseases like diabetes and cancer in bioinformatics research. the review on how data mining methods are joined with machine learning to anticipate diseases in light of client side effects is made in the accompanying paper. © 2022 IEEE.

关键词： Health care

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Emotion recognition Based on Baseline-corrected Differential Entropy Features and Sparse Graph Regularized Extreme learning machine 4

Emotion Recognition Based on Baseline-corrected Differential...

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4th international conference on Computers and Artificial Intelligence Technology, CAIT 2023

作者： Liu, Haoying Yan, Xin Guo, Yuzhu Beijing University of Technology Faculty of Information Techonology Beijing China Beihang University Sino-French Engineer School Beijing China Beihang University School of Automation Science and Electrical Engineering Beijing China

ISBN: (纸本)9798350326710

In recent years, with the rapid development of brain science and information science, affective Brain Computer Interfaces (aBCI) have become a hot topic. EEG signals which record the electrical activity of neurons in the brain can provide direct evidence for emotional states. Compared with end-to-end deep learning methods, expert knowledge based emotion recognition methods, using hand-crafted features, are computationally cheaper and can have better generalization ability and interpretability. Due to the effort of balancing the energy spectrum of EEG signals, differential entropy features have been proved a powerful tool for emotion recognition. However, differential entropy of EEG signals still exhibits a decrease trend with increasing frequency, resulting in a low representation of the effect of high-frequency components in emotion recognition and do not perfectly solve the problem caused by the 1/f power law of EEG signals. In this paper, a decibel (dB) feature is extracted by introducing baseline normalization of EEG power spectral density (PSD) , which balances the effect of different rhythmic components better. On this basis, for the features are smoothed and a sparse graph regularized extreme learning machine (SGELM) is used to compare the dB feature with DE in emotion recognition. the proposed method is validated on DEAP data set, and the average classification accuracy increased by 11.33% over those of DE features with wild feature smoothing, and by 2.96% with high feature smoothness. the baseline normalized DE feature not only provide a new option for low-cost affective brain-computer interfaces can but also be used as an alternative input feature for deep learning models. © 2023 IEEE.

关键词： Emotion recognition

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Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks 26

Automatic Foot Ulcer Segmentation Using an Ensemble of Convo...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Mahbod, Amirreza Schaefer, Gerald Ecker, Rupert Ellinger, Isabella Med Univ Vienna Inst Pathophysiol & Allergy Res Vienna Austria Loughborough Univ Dept Comp Sci Loughborough Leics England TissueGnost GmbH Dept Res & Dev Vienna Austria

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pretrained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. the Dockerised guidelines, inference codes and saved trained models are publicly available at https://***/masih4/Foot Ulcer Segmentation.

关键词： Foot ulcer medical image analysis image segmentation machine learning deep learning ensemble

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Application of Big data Micro-Propagation Dynamic Sequence Mode in Education Informatization Transmission 4

Application of Big Data Micro-Propagation Dynamic Sequence M...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Wen, Dezhi Zhuhai City Polytechnic Guangdong Zhuhai519000 China

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

In this paper, the PrefixSpnan algorithm is improved, and a sequence dynamic simulation algorithm based on big data micro-propagation is proposed, which can greatly reduce the number of constructions of educational information transmission, thereby improving the mining efficiency. In addition, the algorithm proposes the concept of sequential pattern value, by calculating the sequential pattern value of each frequent sequence. Summarizing the development of sequential pattern mining algorithm and the challenges and requirements of data stream mining, a single-machine data stream based on sliding window and compressed prefix tree is proposed. Sequential pattern mining Algorithms. Apply the Map-Reduce-based BLSPM algorithm to educational information transmission. First, analyze the school's historical data, clean it, sample it, etc., to convert it into a sequence database, and then use the Map-Reduce-based BLSPM algorithm for mining. © 2022 IEEE.

关键词： Informatization

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Geometric Gait Clustering for Unobtrusive Analysis 19

Geometric Gait Clustering for Unobtrusive Analysis

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IEEE 19th international conference on Body Sensor Networks (IEEE BSN)

作者： Ellison, Grant Markovic, Milla Penelope Yazdansepas, Delaram Loyola Marymount Univ Dept Comp Sci Los Angeles CA 90045 USA

ISBN: (纸本)9798350338416

An important field within Human Activity recognition is the evaluation of disease and patient recovery through the assessment of gait patterns. Clustering has been used as a data-mining technique to find the prior patterns in subjects' gait patterns. Previous studies have shown the discriminative power of gait clustering on biometrics, and the ability to detect abnormal gait patterns and gait pathology. Previous techniques have relied on expensive machinery and closed environments for gait pattern extraction and/or simplistic featured approaches to clustering. Geometric time series clustering has developed in other fields as a method for incorporating the information from an entire time series sequence and comparing sequences with temporal distortions. We present a method for geometric gait clustering using accelerometer data from wearable sensors. Our methods include an approach to gait cycle averaging and a two-way clustering method for assessing the similarity of biometrics within gait cycle clusters. Our results demonstrate that our methods have significant discriminative efficacy for biometrics and may be a useful analytical tool for gait pathology.

关键词： gait analysis clustering DTW KMeans machine learning wearable sensors gait pathology

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Asphalt pavement raveling identification based on machine learning 4

Asphalt pavement raveling identification based on machine le...

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4th international conference on Image Processing and Intelligent Control, IPIC 2024

作者： Li, Zheng Peng, Yi Liu, Maoyi Jiang, Xin Yu, Xinyi Zhang, Zhengqi Wang, Bo Chen, Kunping Co. Ltd Chongqing400023 China Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System Chongqing Jiaotong University Chongqing400074 China National and Local Joint Laboratory of Traffic Civil Engineering Materials Chongqing Jiaotong University Chongqing400074 China School of Civil Engineering Chongqing Jiaotong University Chongqing400074 China School of Resources and Safety Engineering Chongqing University Chongqing400030 China

ISBN: (纸本)9781510682313

Raveling on asphalt surfaces can lead to the occurrence of diseases such as loose potholes on the road surface. Monitoring and real-time warning of the development status of pavement raveling helps formulate road maintenance plans in time and effectively curb the occurrence and development of pavement diseases. To realize the intelligent identification of asphalt pavement raveling, the LS-40 portable 3D surface analyzer was used to collect the image data of asphalt pavement raveling and normal asphalt pavement, and the recognition effects of traditional image classification and recognition models supported vector machine (SVM), random forest and convolutional neural network (CNN) were compared. the results show that the random forest model has a good accuracy of 82% for the recognition of pavement raveling images based on the traditional machine learning model on the high-precision texture data dataset, and the convolutional neural network has an accuracy of 93%, which is 11% higher than that of random forest. the results verify the efficiency of the convolutional neural network in the identification of pavement raveling diseases. © 2024 SPIE.

关键词： Convolutional neural networks

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