Deep learning:Applications,architectures,models,tools,and frameworks:A comprehensive survey

Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new knowledge.By using DL,the extraction of advanced data representations and knowledge can be made possible.Highly effective DL techniques help to find more hidden knowledge.Deep learning has a promising future due to its great performance and accuracy.We need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the paper.Moreover,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on.

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.

Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing resources.Considering the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing services.In a localMCC,the SMDs are typically connected to a local Wi-Fi network.Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden.Though it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of MCC.To address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is finished.For this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of time.For experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access point.To build the prediction model,we presented a novel feature extraction method to be applied to the time-series data.The experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.

Generating A New Shilling Attack for Recommendation Systems

A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing.To keep the recommendation systems reliable,authentic,and superior,the security of these systems is very crucial.Though the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks,in this paper,we prove that they fail to detect a new or unknown attack.We develop a new attack model,named Obscure attack,with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as intended.The Obscure attack is able to push target items to the top-N list as well as remove the actual rated items from the list.Our proposed attack is more effective at a smaller number of k in top-k similar user as compared to other existing attacks.The effectivity of the proposed attack model is tested on the MovieLens dataset,where various classifiers like SVM,J48,random forest,and naïve Bayes are utilized.

Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation system.Several contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning system.In this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are surveyed.DEMATEL(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among them.The acquiring methods of these contexts are also defined.On the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system.In a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.