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The usage of cloud-based grid computing services and Internet of Things (IoT) devices in medical diagnoses is increasing enormously. The cloud service provider's data centers store vast amounts of data without processing it. This big data need some intelligent technique to analyze and classify heart disease from the considerable volume of data; it is a challenging task. Many deep learning techniques are introduced earlier for heart disease diagnosis in the literature study. Still, all other classification techniques failed to achieve the minimum loss in heart disease classification with the highest accuracy and faster performance. This research introduces a new classification approach to overcome these issues: elephant herding optimizer turned restricted Boltzmann machine EHO-RBM network. The optimizer is used in this network to optimize the number of neuron utilization during the learning process by updating the network weight without compromising the loss. The previous research proves that the optimizer is performed well in reaching global minima efficiently. Therefore, the new classifier incorporates the optimizers instead of the classical stochastic gradient descent optimizer to improve the network performance by minimizing the global minima faster with less loss in predicting heart disease. The simulation result of the new heart disease classification framework shows that the elephant herding optimizer-trained classification model has reduced the loss rate and maximized the accuracy rate up to 0.0027 then the comparison method. As a result, the new classifier has obtained a maximum accuracy of up to 99.96%. [ABSTRACT FROM AUTHOR]

The current research work proposed a novel optimization-based 2DSIMM (Two-Dimensional Sine Iterative chaotic map with infinite collapse Modulation Map) model for image encryption. The proposed 2D-SIMM model is derived out of sine map and Iterative Chaotic Map with Infinite Collapse (ICMIC). In this technique, scrambling effect is achieved with the help of Chaotic Shift Transform (CST). Chaotic Shift Transform is used to change the value of pixels in the input image while the substituted value is cyclically shifted according to the chaotic sequence generated by 2D-SIMM model. These chaotic sequences, generated using 2D-SIMM model, are sensitive to initial conditions. In the proposed algorithm, these initial conditions are optimized using JAYA optimization algorithm. Correlation coefficient and entropy are considered as fitness functions in this study to evaluate the best solution for initial conditions. The simulation results clearly shows that the proposed algorithm achieved a better performance over existing algorithms. In addition, the VLSI implementation of the proposed algorithm was also carried out using Xilinx system generator. With optimization, the correlation coefficient was -0.014096 and without optimization, it was 0.002585. [ABSTRACT FROM AUTHOR]

Recently, a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes. At the same time, clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it. Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset. Big data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce tool. With this motivation, this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics (MR-HDBCC). The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big data. In addition, the MR-HDBCC technique involves three distinct processes namely pre-processing, clustering, and classification. The proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique which is capable of detecting random shapes and diverse clusters with noisy data. For improving the performance of the DBSCAN technique, a hybrid model using cockroach swarm optimization (CSO) algorithm is developed for the exploration of the search space and determine the optimal parameters for density based clustering. Finally, bidirectional gated recurrent neural network (BGRNN) is employed for the classification of big data. The experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures. [ABSTRACT FROM AUTHOR]

Cyber Physical Systems (CPSs) are network systems containing cyber (computation, communication) and physical (sensors, actuators) components that interact with each other through feedback loop with the help of human intervention. The dynamic and disseminated characteristics of CPS environment makes it vulnerable to threats that exist in virtualization process. Due to this, several security issues are presented in CPS. In order to address the challenges, there is a need exists to extend the conventional security solutions such as Intrusion Detection Systems (IDS) to handle high speed network data traffic and adaptive network pattern in cloud. Additionally, the identification of feasible network traffic characteristics is the main issue in precise detection of attacks in the network. With this motivation, the current research paper presents an Optimal Deep Belief Networkbased distributed Intrusion Detection System (ODBN-IDS) for secure CPS environment. The proposed model pre-process the cloud network traffic data to improve its quality to next level. Here, a Binary Flower Pollination Algorithm (BFPA) is employed for feature selection process. The attained characteristics are used in optimal Deep Belief Networks (DBN) to detect the presence of intrusion in cloud data and produce alarms, in case of presence of intrusions. Equilibrium Optimizer Algorithm (EOA) is used to fine tune the hyperparameters in DBN model. A detailed set of simulations was conducted on benchmark datasets and the analysis results were compared. A detailed comparison was conducted for various models to satisfy the security requirements of cloud network and the results established the supremacy of the proposed ODBN-IDS model. [ABSTRACT FROM AUTHOR]

Early detection of preterm labor is important to avoid neonatal death and mortality. Uterine electromyography (UEMG) or electrohysterography is a non-invasive method of extracting electrical activity signal from the abdominal part during pregnancy, which helps in early detection. This signal can be used to classify term and preterm labors. Herein, the performances of four classifiers have been evaluated using seven nonlinear features extracted from UEMG signals. They were then compared with four features analyzed from different literature. The results show that with the Elman neural network classifier, the bi-spectrum feature, which has phase information, outperforms other features with 99.8875% accuracy, 100% sensitivity, and 99.77% specificity. [ABSTRACT FROM AUTHOR]

Precision Agriculture is a concept of farmmanagement which makes use of IoT and networking concepts to improve the crop. Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops. Recognition of diseases fromthe plant images is an active research topicwhich makes use of machine learning (ML) approaches. A novel deep neural network (DNN) classification model is proposed for the identification of paddy leaf disease using plant image data. Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm (CSA) during both the standard pre-training and fine-tuning processes. This DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload, ensuring high classification accuracy. Paddy leaf images were first preprocessed, and the areas indicative of disease were initially extracted using a k-means clustering method. Thresholding was then applied to eliminate regions not indicative of disease. Next, a set of features were extracted from the previously isolated diseased regions. Finally, the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations. [ABSTRACT FROM AUTHOR]

Human computer interaction (HCI) systems are increasing due to the demand for non-intrusive methods for communicating with machines. In this research article, vision based hand gesture recognition (HGR) System has been proposed using machine learning. This proposed system consists of three stages: segmentation, feature extraction and classification. The developed system is to be trained and tested using Sebastian Marcel static hand posture database which is available online. Discrete wavelet transform (DWT) along with modified Speed Up Robust Feature extraction technique has been used to extract rotation and scale invariant key descriptors. Then Bag of Word technique is used to develop the fixed dimension input vector that is required for the support vector machine. The classification accuracy of class 2 and class 4 which corresponds to the 'No' and 'grasp' gesture has reached 98%. The overall classification accuracy of the HGR system using SVM classifier is 96.5% with a recognition time of 0.024 s. Due to fast recognition time, this system can be employed in real time gesture image recognition system. Our HGR system addresses the complex background problem and also improves the robustness of hand gesture recognition. [ABSTRACT FROM AUTHOR]

This investigation practices a grey wolf optimization algorithm to optimize the filter coefficients of the two-dimensional wavelet transform to increase the quality of the decompressed image with lossy compression. The reason for the decrease in image quality in wavelet-based compression is nothing more than a quantization error. Our approach reduces the effect of this quantization error by using an adaptive filtering technique with optimization of the wavelet filter coefficients. Experimental results showed that this optimized filter outperforms existing wavelet filters. [ABSTRACT FROM AUTHOR]

In recent days, total hip arthroplasty (THP) become a very victorious process which relieve the pain and enhances its functioning. The main motive of the paper is to design and assess a synergic deep learning (SDL) for learning and predicting various metrics like duration of stay (DOS), discharge disposition (DD) and inpatient expenses for THA. The next motive is to develop a patient specific payment (PSP) model reporting the complexity level of the patient. By the use of 15 preoperative parameters from 78,335 THA patients for osteoarthritis from National Inpatient Sample and OME databases, the prediction of DOS, DS and inpatient expenses takes place. A set of two evaluation parameters namely accuracy and receiver operating characteristic curve is used for experimentation. In addition, a predictive uncertainty is employed. All patient refined comorbidity cohort for establishing the PSP model. The presented SDL model exhibited better learning with high trustworthiness, receptiveness, and validity in its prediction outcome. The presented model can be applied for the implementation of PSP model for tiered payments depending upon the case complexities. [ABSTRACT FROM AUTHOR]

The modern world uses digital data invariably and the advent of smart phones and cameras add to images and communicating through images in social media is a very common happening due to the exposure to internet and the economical availability of data. Moreover, people explore the web for much news and on different topics. They feel satisfied to look into image type info than textual information. So, image retrieval is paramount nowadays and it is very much useful in many societal and defence applications. The methods used for retrieval started through text based and query based is popularly used now. The content based retrieval uses many stages, approaches, technologies, algorithms etc. It is intended to provide a broad perspective about CBIR in this work. Moreover, the computational and time complexities involved during retrieval relies on the performance of the entire system. One of the aspects that can improve the precision is the reduction of semantic gap. Taking this as the base, we have explored a literature and presented the points related to various points including relevance feedback so as to enable other researchers to carry out experimental work on the suggested areas. [ABSTRACT FROM AUTHOR]

The data hiding method embeds the data into covers such as video, audio, and images, which are used for integrity authentication, media protection, communication covert, copyright protection etc. In this work, several reversible data hiding (RDH) algorithms are analysed. Here, RDH algorithms are classified into six categories. They are histogram shifting centred RDH, code division multiplexing‐based RDH, compression‐based RDH, contrast enhancement with RDH, and expansion‐based RDH, and RDH for encrypted images. In RDH,no information is lost when the message is recovered as the message is embedded into the host image. The embedded message is extracted using an extraction procedure. This study critically reviews the technological advancement for the past decade on the aspects of classification, pre‐processing, feature extraction, and analysis technique. The current trend in RDH is to insert confidential data into a video by using RDH in the encoded area that has a wide selection of applications on the confidential communication inside the area of cybersecurity. [ABSTRACT FROM AUTHOR]

In the MANET, mobile nodes have restricted energy level, so it is very difficult to proceed the communication in a stable manner. The restricted energy level of the node causes poor lifetime of network. These mobile nodes are unrelated with each other, so very difficult to maintain the routing process. The optimized routing only focuses on packet success but does not focus the packet transmission latency, causes the load. A few nodes uses maximum energy causes the network fold down. So, Proposed Improved multi objective data transmission (IMD) technique is applied to balance the load. This technique forwards data in multiple flow manners to various paths. This scheme uses the node for routing process has similar energy node. The conventional route selection algorithm is constructed for data packets are transmitted to different addresses may pursue dissimilar paths. The dissimilar paths are many paths which activities are varied. Every individual path having similar energy nodes, these will manage the overload during communication period. This minimize packet loss rate, and energy consumption, also improves the packet delivery ratio. [ABSTRACT FROM AUTHOR]

Periodic noise corrupts digital images during acquisition and transmission stages by adding repetitive patterns. This study introduces a new adaptive Gaussian notch filter (AGNF) in Fourier transform domain for effectively restoring images contaminated with periodic, quasi‐periodic and Moiré pattern noises. Since periodic noises are sinusoidal functions added to the uncorrupted images, Fourier transform of images make these noisy functions to concentrate as easily distinguishable conjugate peaks in frequency domain. The proposed AGNF effectively identifies the noisy peak positions and adaptively quantifies the associated noisy areas by analysing the ratio of averages of frequencies from adaptively varying neighbourhoods. These identified noisy peaks are then diffused by Gaussian notch filter of adaptively varying sizes. The proposed filter ensures maximum diffusion of identified noisy peak areas by maintaining the minimum frequency values from the outputs of overlapping notch filters. Visual and quantitative experimental analysis of the proposed algorithm with mean absolute error, peak signal‐to‐noise ratio, mean structural similarity index measure and computation time reveals that AGNF is better in restoring images contaminated with periodic noises when compared to other methods. [ABSTRACT FROM AUTHOR]

Antinuclear antibody (ANA) testing is best performed using the indirect immunofluorescence (IIF) method with human epithelial type-2 (HEp-2) cells as the substrate. IIF is a subjective procedure in which HEp-2 patterns are analyzed manually from the microscope. Therefore, ANA test results greatly rely on the experience and expertise of pathologists. Hence, complete automation of the ANA test is required to avoid incorrect diagnoses. This paper represents an algorithm for the complex HEp-2 cell classification problem. The proposed algorithm used a small hybrid feature set that characterizes the texture and morphology of the HEp-2 cells along with artificial neural network (ANN). The hybrid features were extracted by breaking up the image into eight binary images. The proposed hybrid descriptors were more efficient than the popular co-occurrence matrix descriptor and local binary pattern descriptors for texture analysis. The proposed algorithm was evaluated on the ICPR 2016 IIF HEp-2 cell image dataset. The results indicated that the hybrid descriptor with an ANN approach achieved improved performance, with "96.8%" mean class accuracy. [ABSTRACT FROM AUTHOR]

One of the most interesting topics in the field of rehabilitation is that of upper-limb myoelectric prosthetic control. It is a technique by which prostheses are controlled by means of surface electromyogram (sEMG) signals collected from remnant muscle tissues at the residual limb of an amputee. Intuitive control of multifunctional upper-limb prosthesis can be accomplished using pattern recognition (PR) of sEMG signals. In spite of the tremendous progress made in the research of the so-called mind-controlled artificial arm, none of the academic achievements has yet reached the end users. This review paper portrays the current state-of-the-art approach in sEMG pattern classification-based control, identifies the factors that hinder the clinical usability of the system and focuses on the recent research directions toward translating the academic findings into a commercially acceptable robust myoelectric prosthesis. Control strategies proposed for simultaneous and proportional control (SPC) of multiple degrees of freedom (DoFs), which is identified as the most significant barrier for the transition from laboratory to clinical practice, are discussed. Directions for future research are also briefly outlined. [ABSTRACT FROM AUTHOR]

In this paper, Emotional recognition in Parkinson's disease (PD) has been analyzed in both time and frequency domain using Entropy, Energy-Entropy and Teager Energy-Entropy features. Classification results were observed using three classifiers namely Probabilistic Neural Network, K-Nearest Neighbors Algorithm and Support Vector Machine. Emotional EEG stimuli such as happiness, sadness, fear, anger, surprise, and disgust were used to categorize the PD patients and healthy controls (HC). For each EEG signal, the alpha, beta and gamma band frequency features are obtained for six different feature extraction methods (Entropy, Energy-Entropy, Teager Energy-Entropy, Spectral Entropy, Spectral Energy-Entropy and Spectral Teager Energy-Entropy). The proposed Energy-Entropy feature performs evenly for all six emotions with accuracy of above 80% when compared to other features, whereas different features with classifier gives variant results for few emotions with highest accuracy of above 95%. [ABSTRACT FROM AUTHOR]

The survival rate of preterm infants has been increased by the development in treatment methods, still premature birth increases globally. These infants are at risk of medical condition that affects the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. Better understanding of preterm birth can be utilized for the prevention of such birth. This paper explores the significance of entropy features and classification using machine learning approach. Classification of spectral energy entropy feature using Elman neural network shows better result with specificity 99.85%, sensitivity 99.92% and accuracy of 99.8875%. [ABSTRACT FROM AUTHOR]

During the last decade internet users, researchers and industries have focused on the cloud based technologies. Cloud technology is a distributed and scalable service model and in this model service providers offer the services depending on the cloud users. Security in cloud is a challenge for the service providers, data owners and the service requesters because the cloud providers provide various services such as storage, computing, database and network. All the services focus on data processing in a network to share the information from cloud to users, data owner to cloud and cloud to authenticator. To solve these security issues, in this paper we define a new approach for data sharing issues and service management of Trusted Third Party Authentication (TPA) based on multilevel security framework and bilinear pairing techniques. This framework consists of three modules and these modules are service module, transaction processing module and security module. The next thing is bilinear pairing technique, which uses the pairing techniques to achieve the secure cloud service and improve the computing utility power, store large amount of data and execute the efficient encryption process securely in cloud environment. In this work, the cloud ssecurity requirement has been enhanced using bilinear mapping and multilevel security framework. The result of multilevel security framework is comparatively better than the present day cloud security. [ABSTRACT FROM AUTHOR]