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Computer-aided diagnostic system for breast cancer detection based on optimized segmentation scheme and supervised algorithm

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AUTOMATIKA 2023年第4期64卷 1244-1254页

作者： Balaji, S. Arunprasath, T. Rajasekaran, M. Pallikonda Vishnuvarthanan, G. Sindhuja, K. Kalasalingam Acad Res & Educ Dept Elect & Commun Engn Krishnankoil India VIT Bhopal Univ Sch Comp Sci & Engn Kothri Kalan Madhya Pradesh India Theni Kammavar Sangam Coll Technol Dept Elect & Commun Engn Theni India Kalasalingam Acad Res & Educ Dept Biomed Engn Krishnankoil 626126 India

Breast cancer is a serious threat to the womankind and it leads the susceptible kinds of cancer for women. The mortality rates due to breast cancer increases every single year and the World Health Organization (WHO) aims to reduce the occurrence of breast cancer by at least 2.5% per year. The occurrence of breast cancer can be minimized only when periodical screening is carried out. Mammography is one of the effective screening procedure, which can effectively locate earlier signs of breast cancer. As an aid, this work aims to present a system for the breast cancer detection and classification. This work is segregated into four phases and all these phases aim to enhance the classification performance. The efficiency of the proposed work is evaluated against the state-of-the-art approaches and the proposed contribution to the medical science. The computer-aided diagnostic system (CADS) proves 98.2% accuracy, with minimal false positive and false negative rates in a reasonable period of time.

关键词： Breast cancer detection classification CADS optimization supervised algorithm

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A Memristive Spiking Neural Network Circuit With Selective supervised Attention algorithm

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2023年第8期42卷 2604-2617页

作者： Deng, Zekun Wang, Chunhua Lin, Hairong Sun, Yichuang Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Spiking neural networks (SNNs) are biologically plausible and computationally powerful. The current computing systems based on the von Neumann architecture are almost the hardware basis for the implementation of SNNs. However, performance bottlenecks in computing speed, cost, and energy consumption hinder the hardware development of SNNs. Therefore, efficient non von Neumann hardware computing systems for SNNs remain to be explored. In this article, a selective supervised algorithm for spiking neurons (SNs) inspired by the selective attention mechanism is proposed, and a memristive SN circuit as well as a memristive SNN circuit based on the proposed algorithm are designed. The memristor realizes the learning and memory of the synaptic weight. The proposed algorithm includes a top-down (TD) selective supervision method and a bottom-up (BU) selective supervision method. Compared with other supervised algorithms, the proposed algorithm has excellent performance on sequence learning. Moreover, TD and BU attention encoding circuits are designed to provide the hardware foundation for encoding external stimuli into TD and BU attention spikes, respectively. The proposed memristive SNN circuit can perform classification on the MNIST dataset and the Fashion-MNIST dataset with superior accuracy after learning a small number of labeled samples, which greatly reduces the cost of manual annotation and improves the supervised learning efficiency of the memristive SNN circuit.

关键词： Circuit design image classification memristor selective attention sequence learning spiking neural network (SNN) supervised algorithm

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Graphic Representation With Knowledge Structure of Mathematics and Clustering With supervised algorithm

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International Journal of Intelligent Technologies and Applied Statistics 2018年第4期11卷 271-281页

作者： Yuan-Horng Lin Jeng-Ming Yih

Gustafson-Kessel (GK) clustering algorithm needs added constraint of fuzzy covariance matrix, Gath-Geva (GG) clustering algorithm can only be used for the data with multivariate Gaussian distribution. In GK-algorithm, modified Mahalanobis distance with preserved volume was used. However, the added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. Improved normalized Mahalanobis clustering algorithm based on fuzzy c-means (FCM) by taking a new threshold value and a new convergent process is proposed. The experimental results of real data sets show that our proposed new algorithm has the best performance. Not only replacing the common covariance matrix with the correlation matrix in the objective function in the normalized Mahalanobis clustering algorithm The method of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates fuzzy logic model of perception (FLMP) and interpretive structural modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. This integrated method should be feasible to represent concept structures. Besides, fuzzy clustering technique is adopted to provide features of concept structures based on homogeneity of sample. Applying the method of the cluster of FCM, we could distinguish characteristics of six groups. We analyzed the whole data and discuss the relationship between knowledge structures of the sample. The result and discoveries from the research can offer pupils' misconception of learning basic mathematics with reference of diagnosis.

关键词： Gustafson-Kessel algorithm Fuzzy c-means Interpretive structural modeling supervised algorithm

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RECENT ADVANCES ON DRUG DISCOVERY FOR ANTI-INFLAMMATORY ACTIVITY DETECTION: supervised AND UNsupervised algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2016年第5期12卷 1481-1503页

作者： Tian, Shengwei Gao, Shuangyin Yu, Long Shi, Xinyu Wang, Mei Li, Li Xinjiang Univ Coll Software 499 Xibei Rd Urumqi 830008 Peoples R China Xinjiang Univ Network Ctr 14 Shengli Rd Urumqi 830046 Peoples R China Xinjiang Med Univ Inst Med 8 Xinyi Rd Urumqi 830054 Peoples R China Xinjiang Med Univ Coll Med Engn Technol 393 Xinyi Rd Urumqi 830054 Peoples R China

Determining molecular activity from large and high-complexity drug molecules is a challenging task. Many methods have been tried to solve this difficult problem. However, there is not a comprehensive evaluation that covers the performance test of various molecular descriptors based on supervised and unsupervised methods. In practical application, unlabeled dataset is easier to get than labeled data, however, which cannot be a factor that has great influence on test results. Usually, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two common traditional supervised algorithms. Sparse AutoEncode (SAE) and Deep Belief Nets (DBN) are two typical unsupervised algorithms. In this paper, the two types of methods were adopted and extensive classification and evaluation strategies were performed to test and verify the IL-1B anti-inflammatory activity. The results reveal no matter what kind of descriptors, the unsupervised algorithm has more precise predictions than the supervised algorithm though the target output is the same. This research contributes to finding the best match algorithm to determine the reliability of drug activity discovery. It not only reduces the cost of research and improves the efficiency of anti-inflammatory drug discovery, but also has a great significance on promoting anti-inflammatory drug design.

关键词： Molecular activity supervised algorithm Unsupervised algorithm Classification IL-1B anti-inflammatory activity

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Comparative Analysis of Predictive algorithms for Performance Measurement

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IEEE ACCESS 2024年 12卷 33949-33958页

作者： Gupta, Swati Kishan, Bal Gulia, Preeti Maharshi Dayanand Univ MDU Dept Comp Sci & Applicat Rohtak 124001 Haryana India

Predictive algorithms, also known as mathematical models, utilize historical data to accurately predict future outcomes. These algorithms identify patterns and relationships within the data, resulting in precise predictions. The growing importance of predictive algorithms in various domains, such as finance, healthcare, marketing, weather forecasting, E-commerce, etc., has led to an increasing need for robust and accurate models. Machine learning (ML) and deep learning (DL) algorithms, including supervised, unsupervised, & reinforcement learning, play a crucial role in prediction. supervised algorithms include classification and regression, while unsupervised algorithms primarily focus on clustering. In this study, a detailed comparative analysis of eight classification algorithms, six regression algorithms, and five clustering algorithms is performed using diverse datasets and performance metrics. ROBERTA, ResNet, Random Forest Regression, and K-means clustering algorithms outperformed traditional algorithms in textual classification, image classification, regression, and clustering. This study enables data scientists and practitioners to make informed decisions when selecting appropriate models for their specific applications.

关键词： Predictive algorithms supervised algorithm unsupervised algorithm classification regression clustering performance metrics

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RETRACTED: Review of ensemble classification over data streams based on supervised and semi-supervised (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 3859-3878页

作者： Han, Meng Li, Xiaojuan Wang, Le Zhang, Ni Cheng, Haodong North Minzu Univ Sch Comp Sci & Engn Yinchuan Ningxia Peoples R China

Most data stream ensemble classification algorithms use supervised learning. This method needs to use a large number of labeled data to train the classifier, and the cost of obtaining labeled data is very high. Therefore, the semi supervised learning algorithm using labeled data and unlabeled data to train the classifier becomes more and more popular. This article is the first to review data stream ensemble classification methods from the perspectives of supervised learning and semi-supervised learning. Firstly, basic classifiers such as decision trees, neural networks, and support vector machines are introduced from the perspective of supervised learning and semi-supervised learning. Secondly, the key technologies in data stream ensemble classification are explained from the two aspects of incremental and online. Finally, the majority voting and weight voting are explained in the ensemble strategies. The different ensemble methods are summarized and the classic algorithms are quantitatively analyzed. Further research directions are given, including the handling of concept drift under supervised and semi-supervised learning, the study of homogeneous ensemble and heterogeneous ensemble, and the classification of data stream ensemble under unsupervised learning.

关键词： Review ensemble learning supervised algorithm semi-supervised algorithm

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A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method

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Big Data Mining and Analytics 2023年第4期6卷 513-525页

作者： Ankit Kumar Kamred Udham Singh Manish Kumar Department of Computer Engineering and Applications GLA UniversityMathura 281406India School of Computing Graphic Era Hill UniversityDehradun 248002India Department of Electronics and Communication Engineering GLA UniversityMathura 281406India.

The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be *** UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches,including deep *** used a dataset with 13 primary characteristics to carry out the *** vector machine and logistic regression algorithms are used to process the datasets,and the latter displays the highest accuracy in predicting coronary *** programming is used to process the *** research initiatives have used machine learning to speed up the healthcare *** also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection *** the accuracy and confusion matrix has resulted in some favorable *** get the best results,the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine(SVM)classification.

关键词： artificial intelligence support vector machine logistic regression cleveland dataset supervised algorithm human sensing

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Rapid Bacterial Detection and Identification of Bacterial Strains Using Machine Learning Methods Integrated With a Portable Multichannel Fluorometer

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IEEE ACCESS 2023年 11卷 86112-86121页

作者： Hasan, Md Sadique Sundberg, Chad Hasan, Hasibul Kostov, Yordan Ge, Xudong Choa, Fow-Sen Rao, Govind Univ Maryland Baltimore Cty Ctr Adv Sensor Technol Baltimore MD 21227 USA Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA Univ Maryland Baltimore Cty Dept Chem Biochem & Environm Engn Baltimore MD 21250 USA

Rapid and sensitive bioburden detection is of paramount importance in different applications including public health, and food and water safety. To overcome the traditional limitations of bacterial detection i.e., lengthy culture time, and complicated procedure, a low-cost, portable multichannel fluorometer coupled with machine learning (ML) has been implemented in this study. Five different strains of bacterial samples were tested along with the negative control for time-series fluorescence data collection and analysis. We applied different conventional unsupervised and supervised machine learning techniques with extracted features followed by preprocessing of the data. Initially, machine learning algorithms were applied for the qualitative detection of bacteria by binary classification followed by regression analysis to predict the level of contamination for E. coli. The multiclass classification was used to identify gram-positive, and gram-negative bacterial strains and differentiate all the bacterial strains tested. Our results show that around 97.9% accuracy can be achieved for bacterial contamination detection for as low as 1 CFU/mL while 92.1% accuracy can be achieved for differentiating the gram-positive and gram-negative strains. Additionally, with 1 minute of data, high accuracy is obtained for detecting bioburden, proving the multichannel fluorometer's rapid detection capability. The multichannel fluorometer integrated with ML analytics is capable of automating data analysis and determining accurate and rapid bacterial detection on-site with the prediction of bioburden levels and differentiating bacterial strains and the protocol can be applied to the biosensors with a similar data type.

关键词： Bioburden fluorescence machine learning supervised algorithm unsupervised algorithm features time-series

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A Model to Detect the Fraud of Electronic Payment Card Transactions Based on Stream Processing in Big Data

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2023年第12期95卷 1469-1484页

作者： Banirostam, Hamid Banirostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Islamic Azad Univ Dept Comp Engn Cent Tehran Branch Tehran Iran Kharazmi Univ Fac Engn Tehran Iran Islamic Azad Univ Dept Comp Engn Sci & Branch Tehran Iran

With the growing use of electronic cash cards, the number of transactions with these cards has also increased rapidly, so the importance of using fraud detection models has been paid attention to by financial organizations from various aspects. Electronic cash card fraud detection models often on a single algorithm, optimization of classifications and clusters, to find fraudulent patterns, which provide unsupervised or supervised methods. But the proposed model will use both unsupervised and supervised methods to detect fraud so that it will take advantage of the advantages of both methods. In the proposed method, by selecting the most important features of users' behavioral patterns such as transaction time and values, their behavioral modeling is done which includes extracting different profiles of users and determining threshold values for each profile. The proposed model will work in real-time by combining two filters to detect electronic card fraud. The first filter is a fast filter that includes a number of unsupervised algorithms, but the second filter is an explicit filter that consists of a number of supervised algorithms. The proposed model creates a profile of the cardholder and measures the degree of deviation of the cardholder's behavior pattern in new transactions through the Map/Reduce approach for parallel execution alongside the human observer. After then the transaction has been completed and the maximum difference between two consecutive orders of observations, the fraud or non-fraud label will be applied to the transaction and added to the relevant database for future use, in order to detect the deviation of the transaction. According to the simulation results of the proposed model, the accuracy criterion with All Variables, reducing the dimensions of PCA and LASSO is 0.985, 0.987, and 0.980, respectively. F1-Score criterion with All Variables, PCA, and LASSO dimension reduction will be 0.681, 0.676, and 0.669 respectively. The simulation results

关键词： Fraud Transaction E-card Unsupervised algorithm supervised algorithm

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Providing and evaluating a comprehensive model for detecting fraudulent electronic payment card transactions with a two-level filter based on flow processing in big data

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International Journal of Information Technology (Singapore) 2023年第8期15卷 4161-4166页

作者： Banirostam, Hamid Banirostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

Previous research on fraud detection modeling is often based on a single algorithm, optimizing categories and clusters to find fraudulent patterns that they have provided unsupervised or supervised methods alone and within the framework of Hadoop. The proposed model, a model based on big data analysis extracts important features of user behavior patterns such as time, device type, values, and type of transaction, and their behavioral modeling. By creating different profiles for users, threshold values will be set for each of them. The proposed model for real-time fraud detection of electronic cash payment cards includes two fast and explicit filters. Fast filtering is the combination of the Hidden Markov Model of the first order and Self Organizing Maps (SOM) for fast transaction processing and the Baum-Welch algorithm to find the local Maximum Likelihood. Explicit filter model training is a combination of multilayer Perceptron Neural Network algorithms and Logistic Regression to create a cardholder profile and measure the amount of new transaction deviation from the created profile with the reduction mapping approach or parallel execution of the model. Also, transactions performed for aggregation filters and prediction of the output results of the Schaffer Demister function with the parameter θ in order to detect the degree of deviation of the transaction behavior from the normal state according to the profile of the customer and the maximum difference between two consecutive sequences of observations is used. Finally, the fraudulent or non-fraudulent label on the transaction will be applied and added to the relevant database for storage for later use. According to the model simulation results, the proposed Accuracy, Precision, Recall, and F1-Score criteria are 0.999, 0.9834, 0.7906, and 0.9214, respectively. Simulation results show that the proposed model will perform better in each of the compared criteria against each of the single algorithms. © 2023, The Author

关键词： Accuracy Electronic card Fraud supervised algorithm Transaction Unsupervised

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