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• To propose a novel hybridized deep learning technique (HyKBRNet-DLSTM) for automatic detection of varicose vein (V2) with the aid of leg vein features. • To pre-process the raw images using Median Kuan Filtering (Me_KF) technique for enhancing the visual quality of the image. • To extract the vein related features using Extended Twofold Autoencoder (ExT_AE) technique for enhancing the accuracy performance. • To perform dimensionality reduction using Tent Chaotic Zebra Optimizer (TCh-ZO) algorithm for reducing data redundancy and overfitting issues in the proposed model. • To detect the presence and absence of varicose veins with the use of novel hybridized Kernel Boosted ResNet-Dropped Long Short Term Memory (HyKBRNet-DLSTM) technique. Varicose Veins (V2) are the most harmful chronic disease when blood circulation of leg veins is not working precisely. This results in serious blood circulation problems from the heart to the leg. Diagnosing the V2 problem is highly cost-effective; earlier detection is necessary for the patients to relieve pain effectively. Hence, this article brings a novel hybridized deep learning (DL) technique for efficiently detecting the presence/absence of V2. The Median Kuan Filtering (Me_KF) based pre-processing technique is introduced to filter the noises from the raw leg vein images. After pre-processing, the Extended Twofold Autoencoder (ExT_AE) based DL technique is proposed to extract leg vein features. Then, Tent Chaotic Zebra Optimizer (TCh-ZO) algorithm is proposed to eliminate redundant features and enhance accuracy performance. Finally, this study introduces the Hybridized Kernel Boosted ResNet-Dropped Long Short Term Memory (HyKBRNet-DLSTM) based DL technique to detect the presence and absence of varicose veins. The experiment is processed with 11,350 leg vein images, and measures like accuracy, precision, kappa, mean square error (MSE) and time complexity are analyzed. The experimental section proposes an accuracy of 98.5%, a precision of 98.69%, a kappa of 95.77%, an MSE of 1.73 and a time complexity of 25.36 ms. [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]

The dependency on computers and machines have progressively increased in the past few years and hence human interaction with computers is one of the most actively researched area in science. Many Hand Gesture Recognition (HGR) systems have been developed and they continue to evolve, where in which the gesture interaction becomes smoother and smarter. Gesture recognition is usually implemented in three phases- gesture segmentation, feature extraction and gesture classification, and it's important that the processes involved in these stages are chosen appropriately such that the misrecognition rate will be kept to a minimum. This paper has proposed a novel algorithm that combines 2D-Discrete Wavelet Transform along with Speed Up Robust Feature (SURF) extraction technique to achieve a robust HGR system that is rotation and scale invariant. The proposed method has achieved an overall classification accuracy of 96.9% with the Radial Basis Function Neural Network (RBFNN) classifier. [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]

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]