Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with ANFIS) based deep learning method is proposed in this paper for heart disease diagnosis. The proposed MKL with ANFIS based deep learning method follows two-fold approach. MKL method is used to divide parameters between heart disease patients and normal individuals. The result obtained from the MKL method is given to the ANFIS classifier to classify the heart disease and healthy patients. Sensitivity, Specificity and Mean Square Error (MSE) are calculated to evaluate the proposed MKL with ANFIS method. The proposed MKL with ANFIS is also compared with various existing deep learning methods such as Least Square with Support Vector Machine (LS with SVM), General Discriminant Analysis and Least Square Support Vector Machine (GDA with LS-SVM), Principal Component Analysis with Adaptive Neuro-Fuzzy Inference System (PCA with ANFIS) and Latent Dirichlet Allocation with Adaptive Neuro-Fuzzy Inference System (LDA with ANFIS). The results from the proposed MKL with ANFIS method has produced high sensitivity (98%), high specificity (99%) and less Mean Square Error (0.01) for the for the KEGG Metabolic Reaction Network dataset.

Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System

Human activity recognition (HAR) systems attempt to automatically identify and analyze human activities using acquired information from various types of sensors. Although several extensive review papers have already been published in the general HAR topics, the growing technologies in the field as well as the multi-disciplinary nature of HAR prompt the need for constant updates in the field. In this respect, this paper attempts to review and summarize the progress of HAR systems from the computer vision perspective. Indeed, most computer vision applications such as human computer interaction, virtual reality, security, video surveillance and home monitoring are highly correlated to HAR tasks. This establishes new trend and milestone in the development cycle of HAR systems. Therefore, the current survey aims to provide the reader with an up to date analysis of vision-based HAR related literature and recent progress in the field. At the same time, it will highlight the main challenges and future directions.

/pdf/vision-based-human-activity-recognition-a-survey-9vr3o8g5p7.pdf

Vision-based human activity recognition: a survey

The recent developments of internet technology have created premium space for recommender system (RS) to help users in their daily life. An effective personalized recommendation of a travel recommender system can reduce time and travel cost of the travellers. ProTrip RS addresses the personalization problem through exploiting user interests and preferences to generate suggestions. Data considered for the recommendations include travel sequence, actions, motivations, opinions and demographic information of the user. ProTrip is completely designed to be intelligent and in addition, the ProTrip is a health-centric RS which is capable of suggesting the food availability through considering climate attributes based on user’s personal choice and nutritive value. A novel functionality of ProTrip supports travellers with long-term diseases and followers of strict diet. The ProTrip is built on the pillars of ontological knowledge base and tailored filtering mechanisms. The gap between heterogeneous user profiles and descriptions is bridged using semantic ontologies. The effectiveness of recommendations is enhanced through a hybrid model of blended filtering approaches, and results prove that the proposed ProTrip to be a proficient system. The developed food recommendation approach is evaluated for the real-time IoT-based healthcare support system. We also present a detailed case study on the food recommendation-based health management. The proposed system is evaluated on real-time dataset, and analysis of the results shows improved accuracy and efficiency compared to existing models.

An ontology-driven personalized food recommendation in IoT-based healthcare system

The ‘You Only Look Once’ v3 (YOLOv3) method is among the most widely used deep learning-based object detection methods. It uses the k-means cluster method to estimate the initial width and height of the predicted bounding boxes. With this method, the estimated width and height are sensitive to the initial cluster centers, and the processing of large-scale datasets is time-consuming. In order to address these problems, a new cluster method for estimating the initial width and height of the predicted bounding boxes has been developed. Firstly, it randomly selects a couple of width and height values as one initial cluster center separate from the width and height of the ground truth boxes. Secondly, it constructs Markov chains based on the selected initial cluster and uses the final points of every Markov chain as the other initial centers. In the construction of Markov chains, the intersection-over-union method is used to compute the distance between the selected initial clusters and each candidate point, instead of the square root method. Finally, this method can be used to continually update the cluster center with each new set of width and height values, which are only a part of the data selected from the datasets. Our simulation results show that the new method has faster convergence speed for initializing the width and height of the predicted bounding boxes and that it can select more representative initial widths and heights of the predicted bounding boxes. Our proposed method achieves better performance than the YOLOv3 method in terms of recall, mean average precision, and F1-score.

https://www.mdpi.com/2079-9292/9/3/537/pdf

Object Detection Algorithm Based on Improved YOLOv3

Mining temporal association patterns from time-stamped temporal databases, first introduced in 2009, remain an active area of research. A pattern is temporally similar when it satisfies certain specified subset constraints. The naive and apriori algorithm designed for non-temporal databases cannot be extended to find similar temporal patterns in the context of temporal databases. The brute force approach requires performing $$2^{n }$$
 true support computations for ‘n’ items; hence, an NP-class problem. Also, the apriori or fp-tree-based algorithms designed for static databases are not directly extendable to temporal databases to retrieve temporal patterns similar to a reference prevalence of user interest. This is because the support of patterns violates the monotonicity property in temporal databases. In our case, support is a vector of values and not a single value. In this paper, we present a novel approach to retrieve temporal association patterns whose prevalence values are similar to those of the user specified reference. This allows us to significantly reduce support computations by defining novel expressions to estimate support bounds. The proposed approach eliminates computational overhead in finding similar temporal patterns. We then introduce a novel dissimilarity measure, which is the fuzzy Gaussian-based dissimilarity measure. The measure also holds the monotonicity property. Our evaluations demonstrate that the proposed method outperforms brute force and sequential approaches. We also compare the performance of the proposed approach with the SPAMINE which uses the Euclidean measure. The proposed approach uses monotonicity property to prune temporal patterns without computing unnecessary true supports and distances.

A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns

Big data applications are consuming most of the space in industry and research area. Among the widespread examples of big data, the role of video streams from CCTV cameras is equally important as other sources like social media data, sensor data, agriculture data, medical data and data evolved from space research. Surveillance videos have a major contribution in unstructured big data. CCTV cameras are implemented in all places where security having much importance. Manual surveillance seems tedious and time consuming. Security can be defined in different terms in different contexts like theft identification, violence detection, chances of explosion etc. In crowded public places the term security covers almost all type of abnormal events. Among them violence detection is difficult to handle since it involves group activity. The anomalous or abnormal activity analysis in a crowd video scene is very difficult due to several real world constraints. The paper includes a deep rooted survey which starts from object recognition, action recognition, crowd analysis and finally violence detection in a crowd environment. Majority of the papers reviewed in this survey are based on deep learning technique. Various deep learning methods are compared in terms of their algorithms and models. The main focus of this survey is application of deep learning techniques in detecting the exact count, involved persons and the happened activity in a large crowd at all climate conditions. Paper discusses the underlying deep learning implementation technology involved in various crowd video analysis methods. Real time processing, an important issue which is yet to be explored more in this field is also considered. Not many methods are there in handling all these issues simultaneously. The issues recognized in existing methods are identified and summarized. Also future direction is given to reduce the obstacles identified. The survey provides a bibliographic summary of papers from ScienceDirect, IEEE Xplore and ACM digital library.

/pdf/intelligent-video-surveillance-a-review-through-deep-4u0qpijyy7.pdf

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

ANFIS architecture for a multi-inputs and single output Sugeno model with fuzzy n rules. The GSD team-level service climate is one of the key determinants to achieve the outcome of global software development (GSD) projects.To evaluate the GSD team-level service climate and GSD project outcome relationship based on Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Hybrid Taguchi-Genetic Learning Algorithm (HTGLA).The applicability and capability of HTGLA-based ANFIS approach is investigated through the real data sets obtained from Indian software industries. The GSD team-level service climate is one of the key determinants to achieve the outcome of global software development (GSD) projects from the software service outsourcing perspective. The main aim of this study is to evaluate the GSD team-level service climate and GSD project outcome relationship based on adaptive neuro-fuzzy inference system (ANFIS) with the genetic learning algorithm. For measuring the team-level service climate, the Hybrid Taguchi-Genetic Learning Algorithm (HTGLA) is adopted in the ANFIS, which is more appropriate to determine the optimal premise and consequent constructs by reducing the root-mean-square-error (RMSE) of service climate criteria. For measuring the GSD team-level service climate, synthesizing the literature reviews and consistent with the earlier studies on IT service climate which is classified into three main criterion: managerial practices (deliver quality of service), global service climate (measure overall perceptions), service leadership (goal setting, work planning, and coordination) which comprises 25 GSD team-level service climate attributes. The experimental results show that the optimal prediction error is obtained by the HTGLA-based ANFIS approach is 3.26%, which outperforms the earlier result that is the optimal prediction errors 4.41% and 5.75% determined, respectively, by ANFIS and statistical methods.

An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm

Medical diagnosis processes vary in the degree to which they attempt to deal with different complicating aspects of diagnosis such as relative importance of symptoms, varied symptom pattern and the relation between diseases themselves. Rough set approach has two major advantages over the other methods. First, it can handle different types of data such as categorical, numerical etc. Secondly, it does not make any assumption like probability distribution function in stochastic modeling or membership grade function in fuzzy set theory. It involves pattern recognition through logical computational rules rather than approximating them through smooth mathematical functional forms. In this paper we use rough set theory as a data mining tool to derive useful patterns and rules for kidney cancer faulty diagnosis. In particular, the historical data of twenty five research hospitals and medical college is used for validation and the results show the practical viability of the proposed approach.

An intelligent knowledge mining model for kidney cancer using rough set theory

HCI Challenges and Privacy Preservation in Big Data Security

Cloud computing is a model where software applications and computing resources are accessed over Internet with minimal cost and effort by interacting with the service provider. Along with these benefits there are also some significant security concerns that need to be addressed for handling sensitive data and critical applications. The simultaneous use of multiple clouds can provide several potential benefits, such as high availability, fault tolerance and reduced infrastructural cost. The model proposed which is the implementation of a secured multi-cloud virtual infrastructure consists of a grid engine on top of the multi-cloud infrastructure to distribute the task among the worker nodes that are supplied with various resources from different clouds to enhance cost efficiency of the infrastructure set up and also to implement high availability feature. The Oracle grid engine is used to schedule the jobs to the worker nodes (in-house and cloud). Worker nodes will be acting like listeners to receive the job from the oracle grid engine master node. High security is provided at this point for data using AES algorithm and also a password protection key for privileged user’s access. Performance analysis, cost analysis and cost-performance ratio analysis are done by comparing different cluster configurations.

http://www.cit.iit.bas.bg/CIT_2012/v12-2/Thandeeswaran,Subhashini,Jeyanthi,Durai-pp-11-22.pdf

M.A. Saleem Durai

Papers

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm

An intelligent knowledge mining model for kidney cancer using rough set theory

HCI Challenges and Privacy Preservation in Big Data Security

Secured Multi-Cloud Virtual Infrastructure with Improved Performance