scholarly journals Using Transfer Learning and Hierarchical Classifier to Diagnose Melanoma From Dermoscopic Images

2021 ◽  
Vol 16 (2) ◽  
pp. 73-86
Author(s):  
Priti Bansal ◽  
Sumit Kumar ◽  
Ritesh Srivastava ◽  
Saksham Agarwal
Keyword(s):  
Skin Lesion ◽  
Transfer Learning ◽  
Intelligent Systems ◽  
Nearest Neighbor ◽  
Critical Role ◽  
K Nearest Neighbor ◽  
Melanoma Detection ◽  
Proposed Model ◽  
Medical Image Diagnosis ◽  
Hierarchical Classifier

The deadliest form of skin cancer is melanoma, and if detected in time, it is curable. Detection of melanoma using biopsy is a painful and time-consuming task. Alternate means are being used by medical experts to diagnose melanoma by extracting features from skin lesion images. Medical image diagnosis requires intelligent systems. Many intelligent systems based on image processing and machine learning have been proposed by researchers in the past to detect different kinds of diseases that are successfully used by healthcare organisations worldwide. Intelligent systems to detect melanoma from skin lesion images are also evolving with the aim of improving the accuracy of melanoma detection. Feature extraction plays a critical role. In this paper, a model is proposed in which features are extracted using convolutional neural network (CNN) with transfer learning and a hierarchical classifier consisting of random forest (RF), k-nearest neighbor (KNN), and adaboost is used to detect melanoma using the extracted features. Experimental results show the effectiveness of the proposed model.

scholarly journals Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

2021 ◽  
Vol 18 (12) ◽  
pp. 6429
Author(s):  
Irfan Ullah Khan ◽  
Nida Aslam ◽  
Malak Aljabri ◽  
Sumayh S. Aljameel ◽  
Mariam Moataz Aly Kamaleldin ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Computational Intelligence ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Detection And Identification ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
The World ◽  
Detection And Diagnosis

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.

scholarly journals A MODIFIED MODEL BASED ON FLOWER POLLINATION ALGORITHM AND K-NEAREST NEIGHBOR FOR DIAGNOSING DISEASES

2018 ◽  
Vol 19 (1) ◽  
pp. 144-157
Author(s):  
Mehdi Zekriyapanah Gashti
Keyword(s):  
Breast Cancer ◽  
Nearest Neighbor ◽  
Heart Diseases ◽  
Critical Role ◽  
Clinical Manifestations ◽  
Flower Pollination Algorithm ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Modified Model ◽  
Flower Pollination

Exponential growth of medical data and recorded resources from patients with different diseases can be exploited to establish an optimal association between disease symptoms and diagnosis. The main issue in diagnosis is the variability of the features that can be attributed for particular diseases, since some of these features are not essential for the diagnosis and may even lead to a delay in diagnosis. For instance, diabetes, hepatitis, breast cancer, and heart disease, that express multitudes of clinical manifestations as symptoms, are among the diseases with higher morbidity rate. Timely diagnosis of such diseases can play a critical role in decreasing their effect on patients’ quality of life and on the costs of their treatment. Thanks to the large data set available, computer aided diagnosis can be an advanced option for early diagnosis of the diseases. In this paper, using a Flower Pollination Algorithm (FPA) and K-Nearest Neighbor (KNN), a new method is suggested for diagnosis. The modified model can diagnose diseases more accurately by reducing the number of features. The main purpose of the modified model is that the Feature Selection (FS) should be done by FPA and data classification should be performed using KNN. The results showed higher efficiency of the modified model on diagnosis of diabetes, hepatitis, breast cancer, and heart diseases compared to the KNN models. ABSTRAK: Pertumbuhan eksponen dalam data perubatan dan sumber direkodkan daripada pesakit dengan penyakit berbeza boleh disalah guna bagi membentuk kebersamaan optimum antara simptom penyakit dan mengenal pasti gejala penyakit (diagnosis). Isu utama dalam diagnosis adalah kepelbagaian ciri yang dimiliki pada penyakit tertentu, sementara ciri-ciri ini tidak penting untuk didiagnosis dan boleh mengarah kepada penangguhan dalam diagnosis. Sebagai contoh, penyakit kencing manis, radang hati, barah payudara dan penyakit jantung, menunjukkan banyak klinikal simptom jelas dan merupakan penyakit tertinggi berlaku dalam masyarakat. Diagnosis tepat pada penyakit tersebut boleh memainkan peranan penting dalam mengurangkan kesan kualiti  hidup dan kos rawatan pesakit. Terima kasih kepada set data yang banyak, diagnosis dengan bantuan komputer boleh menjadi pilihan maju menuju ke arah diagnosis awal kepada penyakit. Kertas ini menggunakan Algoritma Flower Pollination (FPA) dan K-Nearest Neighbor (KNN), iaitu kaedah baru dicadangkan bagi diagnosis. Model yang diubah suai boleh mendiagnosis penyakit lebih tepat dengan mengurangkan bilangan ciri-ciri. Tujuan utama model yang diubah suai ini adalah bagi Pemilihan Ciri (FS) perlu dilakukan menggunakan FPA and pengkhususan data perlu dijalankan menggunakan KNN. Keputusan menunjukkan model yang diubah suai lebih cekap dalam mendiagnosis penyakit kencing manis, radang hati, barah payudara dan penyakit jantung berbanding model KNN.

Land Cover Classification Using the Proposed Texture Model and Fuzzy k-NN Classifier

2018 ◽  
pp. 226-261
Author(s):  
Jenicka S.
Keyword(s):  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Texture Feature ◽  
Image Texture ◽  
Kappa Statistics ◽  
Grey Level ◽  
Classification Problems ◽  
K Nearest Neighbor ◽  
Texture Model ◽  
Proposed Model

Texture feature is a decisive factor in pattern classification problems because texture features are not deduced from the intensity of current pixel but from the grey level intensity variations of current pixel with its neighbors. In this chapter, a new texture model called multivariate binary threshold pattern (MBTP) has been proposed with five discrete levels such as -9, -1, 0, 1, and 9 characterizing the grey level intensity variations of the center pixel with its neighbors in the local neighborhood of each band in a multispectral image. Texture-based classification has been performed with the proposed model using fuzzy k-nearest neighbor (fuzzy k-NN) algorithm on IRS-P6, LISS-IV data, and the results have been evaluated based on confusion matrix, classification accuracy, and Kappa statistics. From the experiments, it is found that the proposed model outperforms other chosen existing texture models.

scholarly journals Chicago Crime Analysis using R Programming

2019 ◽  
pp. 937-944
Author(s):  
Monish N
Keyword(s):  
Law Enforcement ◽  
Data Science ◽  
Nearest Neighbor ◽  
Crime Rates ◽  
K Nearest Neighbor ◽  
Proposed Model ◽  
The Us ◽  
Steep Decline ◽  
R Programming ◽  
And Behavior

In recent years law enforcement have improved by taking better strategies, computer aided technology, efficient use of resource, etc. As a result of these over the couple of years there has been a steep decline in crime rate in the US (United States). Law enforcement have turned to data science for insights (ranging from reports, corrective analysis and behavior modelling). There has been an overall drop in crime rates in Chicago in recent years. In fact, these rates are at the lowest when compared to the previous decades. This paper uses the criminal dataset found at “data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2” to describe historical trends, insights, etc. in Chicago from 1965 to 2018 and not to assign any casual interpretation of the vanguards of crime rates during this period. Here K-Nearest Neighbor (KNN) classification is used for training and crime predication. Discussions on future investigation can also be found. The proposed model has an accuracy of 83.2%.

scholarly journals Heart disease prediction model with k-nearest neighbor algorithm

2021 ◽  
Vol 10 (3) ◽  
pp. 225
Author(s):  
Tssehay Admassu Assegie
Keyword(s):  
Heart Disease ◽  
Prediction Model ◽  
Nearest Neighbor ◽  
Predictive Performance ◽  
Data Repository ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Proposed Model ◽  
K Nearest Neighbor Algorithm ◽  
Learning Data

<span>In this study, the author proposed k-nearest neighbor (KNN) based heart disease prediction model. The author conducted an experiment to evaluate the performance of the proposed model. Moreover, the result of the experimental evaluation of the predictive performance of the proposed model is analyzed. To conduct the study, the author obtained heart disease data from Kaggle machine learning data repository. The dataset consists of 1025 observations of which 499 or 48.68% is heart disease negative and 526 or 51.32% is heart disease positive. Finally, the performance of KNN algorithm is analyzed on the test set. The result of performance analysis on the experimental results on the Kaggle heart disease data repository shows that the accuracy of the KNN is 91.99%</span>

scholarly journals Prediction of Train Arrival Delay Using Hybrid ELM-PSO Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xu Bao ◽  
Yanqiu Li ◽  
Jianmin Li ◽  
Rui Shi ◽  
Xin Ding
Keyword(s):  
Nearest Neighbor ◽  
Pso Algorithm ◽  
Bayesian Optimization ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Delay Management ◽  
Buffer Time ◽  
Proposed Model ◽  
Learning Machine ◽  
Hidden Layer

In this study, a hybrid method combining extreme learning machine (ELM) and particle swarm optimization (PSO) is proposed to forecast train arrival delays that can be used for later delay management and timetable optimization. First, nine characteristics (e.g., buffer time, the train number, and station code) associated with train arrival delays are chosen and analyzed using extra trees classifier. Next, an ELM with one hidden layer is developed to predict train arrival delays by considering these characteristics mentioned before as input features. Furthermore, the PSO algorithm is chosen to optimize the hyperparameter of the ELM compared to Bayesian optimization and genetic algorithm solving the arduousness problem of manual regulating. Finally, a case is studied to confirm the advantage of the proposed model. Contrasted to four baseline models (k-nearest neighbor, categorical boosting, Lasso, and gradient boosting decision tree) across different metrics, the proposed model is demonstrated to be proficient and achieve the highest prediction accuracy. In addition, through a detailed analysis of the prediction error, it is found that our model possesses good robustness and correctness.

scholarly journals Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

2020 ◽  
Vol 6 (12) ◽  
pp. 129
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Neural Networks ◽  
Skin Lesion ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Image Features ◽  
Ensemble Classification ◽  
Deep Convolutional Neural Networks ◽  
Melanoma Detection ◽  
Statistical Measures ◽  
Different Types

Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.

scholarly journals K-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection

2021 ◽  
Vol 1 (2) ◽  
pp. 18-21
Author(s):  
Tsehay Admassu Assegie*
Keyword(s):  
Nearest Neighbor ◽  
Attack Detection ◽  
Experimental Result ◽  
Data Repository ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
K Value ◽  
Proposed Model ◽  
Public Data ◽  
Public Data Repository

Phishing causes many problems in business industry. The electronic commerce and electronic banking such as mobile banking involves a number of online transaction. In such online transactions, we have to discriminate features related to legitimate and phishing websites in order to ensure security of the online transaction. In this study, we have collected data form phish tank public data repository and proposed K-Nearest Neighbors (KNN) based model for phishing attack detection. The proposed model detects phishing attack through URL classification. The performance of the proposed model is tested empirically and result is analyzed. Experimental result on test set reveals that the model is efficient on phishing attack detection. Furthermore, the K value that gives better accuracy is determined to achieve better performance on phishing attack detection. Overall, the average accuracy of the proposed model is 85.08%.

scholarly journals Recommender System for Term Deposit Likelihood Prediction using Cross-validated Neural Network

2021 ◽  
pp. 21-28
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Fold Cross Validation

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and MSE of 0.1168.

scholarly journals K-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection

2021 ◽  
pp. 18-21
Author(s):  
Tsehay Admassu Assegie ◽  
Keyword(s):  
Nearest Neighbor ◽  
Attack Detection ◽  
Experimental Result ◽  
Data Repository ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
K Value ◽  
Proposed Model ◽  
Public Data ◽  
Public Data Repository

Phishing causes many problems in business industry. The electronic commerce and electronic banking such as mobile banking involves a number of online transaction. In such online transactions, we have to discriminate features related to legitimate and phishing websites in order to ensure security of the online transaction. In this study, we have collected data form phish tank public data repository and proposed K-Nearest Neighbors (KNN) based model for phishing attack detection. The proposed model detects phishing attack through URL classification. The performance of the proposed model is tested empirically and result is analyzed. Experimental result on test set reveals that the model is efficient on phishing attack detection. Furthermore, the K value that gives better accuracy is determined to achieve better performance on phishing attack detection. Overall, the average accuracy of the proposed model is 85.08%.

Sign in / Sign up

Export Citation Format

Share Document