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Classification of Multi-view Digital Mammogram Images Using SMO-WkNN

Computer Systems Science & Engineering 2023年第8期46卷 1741-1758页

作者： P.Malathi g.charlyn pushpa latha Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiTamilnaduIndia Department of Information Technology Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiTamilnaduIndia

Breast cancer(BCa)is a leading cause of death in the female population across the *** 2.3 million new BCa cases are recorded globally in females,overtaking lung cancer as the most prevalent form of cancer to be ***,the mortality rates for cervical and BCa are significantly higher in developing nations than in developed *** diagnosis is the only option to minimize the risks of *** learning(DL)-based models have performed well in image processing in recent years,particularly convolutional neural network(CNN).Hence,this research proposes a DL-based CNN model to diagnose BCa from digitized mammogram *** main objective of this research is to develop an accurate and efficient early diagnosis model for BCa *** proposed model is a multi-view-based computer-aided diagnosis(CAD)model,which performs the diagnosis of BCa on multi-views of mammogram images like medio-lateral-oblique(MLO)and cranio-caudal(CC).The digital mammogram images are collected from the digital database for screening mammography(DDSM)*** preprocessing,median filter and contrast limited adaptive histogram equalization(CLAHE)techniques are utilized for image *** preprocessing,the segmentation is performed using the region growing(Rg)*** feature extraction is carried out from the segmented images using a pyramidal histogram of oriented gradients(PHOg)and the AlextNet ***,the classification is performed using the weighted k-nearest neighbor(WkNN)optimized with sequential minimal optimization(SMO).The classified images are evaluated based on accuracy,recall,precision,specificity,f1-score,and mathews correlation coefficient(MCC).Additionally,the false positive and error rates are *** proposed model obtained 98.57%accuracy,98.61%recall,99.25%specificity,98.63%precision,97.93%f1-score,96.26%MCC,0.0143 error rate,and 0.0075 false positive rate(FPR).Compared to the existing models,the research model ha

关键词： Breast cancer DDSM CLAHE median filter region growing PHOg AlexNet SMO-WkNN

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Content-Based Movie Recommendation System Using MBO with DBN

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Intelligent Automation & Soft Computing 2023年第3期35卷 3241-3257页

作者： S.Sridhar D.Dhanasekaran g.charlyn pushpa latha Department of CSE Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Saveetha Institute of Medical and Technical Sciences ChennaiIndia Department of IT Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia

The content-basedﬁltering technique has been used effectively in a variety of Recommender Systems(RS).The user explicitly or implicitly provides data in the Content-Based Recommender *** system collects this data and creates a proﬁle for all the users,and the recommendation is generated by the user proﬁ*** recommendation generated via content-basedﬁltering is provided by observing just a single user’s proﬁ*** primary objective of this RS is to recommend a list of movies based on the user’s preferences.A con-tent-based movie recommendation model is proposed in this research,which recommends movies based on the user’s proﬁle from the Facebook *** recommendation system is built with a hybrid model that combines the Mon-arch Butterﬂy Optimization(MBO)with the Deep Belief Network(DBN).For feature selection,the MBO is utilized,while DBN is used for classiﬁ*** datasets used in the experiment are collected from Facebook and *** dataset features are evaluated for performance evaluation to validate if data with various attributes can solve the matching ***ﬁle is com-pared with features that prove the features will support movie *** proposed model’s mean absolute error(MAE)and root-mean-square error(RMSE)values are 0.716 and 0.915,and its precision and recall are 97.35 and 96.60 percent,*** tests have demonstrated the advantages of the proposed method in terms of MAE,RMSE,Precision,and Recall compared to state-of-the-art algorithms such as Fuzzy C-means with Bat algorithm(FCM-BAT),Collaborativeﬁltering with k-NN and the normalized discounted cumulative gain method(CF-kNN+NDCg),User proﬁle correlation-based similarity(UPCSim),and Deep Autoencoder.

关键词： Movie recommendation monarch butterﬂy optimization deep belief network facebook movielens deep learning

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