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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]

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]

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]

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]

Mobile Adhoc network is a network, which has advances of dynamic topology, mobility of nodes and congestion control. It is also an infrastructure less networks. This paper "Congestion control and packet Recovery for cross layer approach" is proposed. Traffic of packets occurred due to congestion and contention problems are reduced in datalink layer and transport layer. Also ensuring that, the superior performance of congestion is a challenging model due to changes in link layer contentions, topological changes and characteristic of wireless channel. The features of this model were localized packet recovery, deterministic, ability to trade of efficiency and peer to peer recovery. It can retrieve the missing packets by storing the copy of packet. Also by assigning priority order to the packets, data traffic congestion rate of higher and lower priority packets are maintained. This will develop the high priority flow to get desired access to the medium and its flow rate of low priority is adjusted, in order to have high flow rate of higher priority. Therefore the overall performance gives the better result than existing method. The simulation of CCPRCLA is verified using ns2 simulator, then graph and experimental results are discussed. [ABSTRACT FROM AUTHOR]

Nowadays, cloud storage services increased the popular for data storage in the cloud and retrieve from any location without any time limitations. In recent days one of the most important demands required in cloud is secured data transmission in un-trusted cloud applications. Due to user's data security, the encrypted data is stored in cloud server to protect from unauthorized users. Existing methods offer either data transformation efficiency or security. They fail to maintain end to end security during massive transformations. However, existing methods are not capable of solving the key complexity and avoiding key secrecy disclosure. The main objective of this study is to design and develop a secured efficient data forwarding algorithm for increasing the security level. It is developed specially for untrusted cloud environment. In order to provide a better solution, an efficient framework is proposed for forwarding and retrieving the content in the untrusted cloud environment. Proposed system implements dual privacy for reliable data transmission in an untrusted cloud environment. It develops efficient secret key exposure to minimize the key complexity during data transmission. SEDFA is used for one to many communications as a public key used for encryption and decryption. This scheme provides reliable data transmission between the data owner and end user in untrusted Cloud Environment. Proposed mechanisms minimized the data encryption time, decryption time and improved the communication cost. Based on experimental results, SEDFA reduces the communication cost 5%, encryption time (ET), 2 s, decryption time (DT) 0.5 s. [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]

Regular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence. Diabetic patients now have an exceptional improvement in their quality of life with the development of cutting-edge sensors and healthcare technologies. intensive care unit (ICU) and pregnant women also have facing challenges including hyperglycemia and hypoglycemia. The worldwide diabetic rate has incited to develop a wearable and accurate non-invasive blood glucose monitoring system. This research developed an Internet of Things (IoT) - enabled wearable blood glucose monitoring (iGM) system to transform diabetes care and enhance the quality of life. The TTGOT-ESP32 IoT platform with a red and near-infrared (R-NIR) spectral range for blood glucose measurement has integrated into this wearable device. The primary objective of this gadget is to provide optimal comfort for the patients while delivering a smooth monitoring experience. The iGM gadget is 98.82 % accuracy when used after 10 hours of fasting and 98.04 % accuracy after 2 hours of breakfast. The primary objective points of the research were continuous monitoring, decreased risk of infection, and improved quality of life. This research contributes to the evolving field of IoT-based healthcare solutions by streaming real-time glucose values on AWS IoT Core to empower individuals with diabetes to manage their conditions effectively. The iGM Framework has a promising future with the potential to transform diabetes management and healthcare delivery. [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]

In automobiles, tires play an important role in ensuring safe driving. Hence tire pressure monitoring system (TPMS) has become the most important safety system in automobile vehicles. In this work, a new indirect TMPS is proposed that measures the pressure of all the tyres indirectly using the available sensors already mounted for typical vehicle dynamics control purposes. The available sensors in vehicles usually contain a power supply module, a microcontroller, and transceiver and resonator circuitry. The available wheel speed sensor is modified by reducing the wireless receiver system in communication protocols to minimize the power consumption. The transceiver structure is modified using a suitable local oscillator (LO) frequency plan based on a temperature-compensated thin film bulk acoustic wave resonator (FBAR). In addition, the proposed TPMS SoC uses a deep-stacked autoencoder to detect the adherence conditions of the tires and a pressure loss based on wheel speed. This module will initially pre-process the speed signals to remove the speed error caused by manufacturing error. After pre-processing, different features such as statistical, frequency-domain and the fitted frequency-domain characteristics are extracted from the wheel speed signal and fed to the learning model for the judgment of the tire state. The proposed system was designed in Verilog synthesis and layout were made in cadence tool. For ASIC implementation, we use TSMC 65 nm CMOS Library for LNA/hybrid mixer design. The performance measures of the pressure detection model in terms of accuracy and errors are evaluated. Also, the performance metrics of the transceiver architecture in terms of gain, NF, IIP3, S11 and FOM are evaluated. The experimental evaluation describes that the model procures a maximum gain of 58 dB, NF of 2.15 dB and IIP3 of 6.6 dBm respectively. The die area is 0.45 m m 2 with 0.8 supply voltage and 0.83 mW power. In addition to this, the relationship between peak amplitude and speed vehicle regarding time and frequency domain features are evaluated. [ABSTRACT FROM AUTHOR]

Field-programmable gate arrays (FPGAs) have travelled far from just being utilized as glue logic to an entire system solution. This is mostly due to their generalized re-configurable nature, lower non-recurring engineering (NRE) expense, and also fast time to market. Owing to the reconfigurable nature of FPGA, a new field called reconfigurable computing that can change the circuit configuration after hardware production came into existence. Application of re-configurable computing for self-adaptive hardware allows hardware to get adapt to various environmental conditions and different needs by swapping or loading disparate computational modules. This work proposes an effectual design methodology (enhanced DPR security system (EDPRSS)) utilized to execute high performance FPGA device in respect of low power consumption along with security for the area reduction. In the proposed technique, hash code generation (HCG) and encryption hardware accelerators can well be dynamically produced on FPGA utilizing partial re-configuration as stated by the application requisites. The system is competent to swap in or swap out the equivalent hardware accelerator during run time, which in turn diminishes the power and area. Here, 2 re-configurable partitions are produced for encryption and also HCG algorithm. Experiential outcomes proved that the proposed technique proffers better performance when contrasted to the other conventional systems. [ABSTRACT FROM AUTHOR]