ANT colony optimization based optimal path selection and data gathering in WSN

A data aggregation is an essential process in the field of wireless sensor network to deal with base station and sink node. In current data gathering mechanism, the nearest nodes to the sink receives data from all the other nodes and shares it to the sink. The data aggregation process is utilized to increase the capability and efficiency of the existing system. In existing technique, the possibility of data loss is high this may leads to energy loss therefore; the efficiency and performance are damaged. In order to overcome these issues, an effective cluster based data gathering technique is developed. Here the optimal cluster heads are selected which is used for transmission with low energy consumption. The optimal path for mobile sink (MS) is done by Ant Colony Optimization (ACO) algorithm. It provides efficient path along with MS to collect the data along with Cluster centroid. The performance of the proposed method is analyzed in terms of delay, throughput, lifetime, etc.</p

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

Introduction: Essential genes are essential for the survival of various species. These genes are a family linked to critical cellular activities for species survival. These genes are coded for proteins that regulate central metabolism, gene translation, deoxyribonucleic acid replication, and fundamental cellular structure and facilitate intracellular and extracellular transport. Essential genes preserve crucial genomics information that may hold the key to a detailed knowledge of life and evolution. Essential gene studies have long been regarded as a vital topic in computational biology due to their relevance. An essential gene is composed of adenine, guanine, cytosine, and thymine and its various combinations.Methods: This paper presents a novel method of extracting information on the stationary patterns of nucleotides such as adenine, guanine, cytosine, and thymine in each gene. For this purpose, some co-occurrence matrices are derived that provide the statistical distribution of stationary patterns of nucleotides in the genes, which is helpful in establishing the relationship between the nucleotides. For extracting discriminant features from each co-occurrence matrix, energy, entropy, homogeneity, contrast, and dissimilarity features are computed, which are extracted from all co-occurrence matrices and then concatenated to form a feature vector representing each essential gene. Finally, supervised machine learning algorithms are applied for essential gene classification based on the extracted fixed-dimensional feature vectors.Results: For comparison, some existing state-of-the-art feature representation techniques such as Shannon entropy (SE), Hurst exponent (HE), fractal dimension (FD), and their combinations have been utilized.Discussion: An extensive experiment has been performed for classifying the essential genes of five species that show the robustness and effectiveness of the proposed methodology

A Novel Cancelable FaceHashing Technique Based on Non-invertible Transformation with Encryption and Decryption Template

A novel cancelable FaceHashing technique based on non-invertible transformation with encryption and decryption template has been proposed in this paper. The proposed system has four components: face preprocessing, feature extraction, cancelable feature extraction followed by the classification, and encryption/decryption of cancelable face feature templates. During face preprocessing, the facial region of interest has been extracted out to speed the process for evaluating discriminant features. In feature extraction, some optimization techniques such as Sparse Representation Coding, Coordinate descent, and Block coordinates descent have been employed on facial descriptors to obtain the best representative of those descriptors. The representative descriptors are further arranged in a spatial pyramid matching structure to extract more discriminant and distinctive feature vectors. To preserve them, the existing BioHashing technique has been modified and extended to some higher levels of security attacks and the modified BioHashing technique computes a cancelable feature vector by the combined effect of the facial feature vector and the assigned token correspond to each user. The elements of computed cancelable feature vector are in a numeric form that has been employed to perform both verifications as well as identification task in online while the original facial feature vectors are kept offline either in hard drive or disc. Then, to enhance more security levels and also to preserve the cancelable face features, an RSA based encryption-decryption algorithm has been introduced. The proposed system has been tested using four benchmark face databases: CASIA-FACE-v5, IITK, CVL, and FERET, and performance are obtained as correct recognition rate and equal error rate. The performance are compared to the state-of-the-art methods for the superiority of the proposed feature extraction technique and individual performance analysis has been performed at all the security levels of the proposed Cancelable FaceHashing Technique. These comparisons show the superiority of the proposed system

A Unified Framework of Deep Learning-Based Facial Expression Recognition System for Diversified Applications

This work proposes a facial expression recognition system for a diversified field of applications. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution images, can be introduced. Then, some convolutional neural network (CNN) architectures were proposed in the second component to analyze the texture patterns in the facial regions. To enhance the proposed CNN model’s performance, some advanced techniques, such data augmentation, progressive image resizing, transfer-learning, and fine-tuning of the parameters, were employed in the third component to extract more distinctive and discriminant features for the proposed facial expression recognition system. The performance of the proposed system, due to different CNN models, is fused to achieve better performance than the existing state-of-the-art methods and for this reason, extensive experimentation has been carried out using the Karolinska-directed emotional faces (KDEF), GENKI-4k, Cohn-Kanade (CK+), and Static Facial Expressions in the Wild (SFEW) benchmark databases. The performance has been compared with some existing methods concerning these databases, which shows that the proposed facial expression recognition system outperforms other competing methods

Impact of Deep Learning Approaches on Facial Expression Recognition in Healthcare Industries

Facial expression recognition system can provide quick assistance to the healthcare system and exceptional services to the patients. A facial expression recognition system has been proposed in this work. The implementation of the proposed approach has been divided into three components. In the first component, landmark points on the facial region has been detected. The detected face region is normalized to its fixed size and then down-sampled to its varying sizes producing multi-resolution images. Different CNN architectures have been proposed in the second component to analyze the texture patterns in the multi-resolution facial images. Data augmentation, progressive image resizing, transfer-learning, and fine-tune of parameters have been employed in the third component to extract more distinctive &amp; discriminant features and enhance the proposed CNN models' performance. Extensive experimentation has been carried out using Karolinska directed emotional faces (KDEF), Cohn-Kanade (CK+), and Static Facial Expressions in the Wild (SFEW) benchmark databases and the performance have been compared with some existing methods concerning these databases. The comparison shows that the proposed facial expression recognition system outperforms other competing methods

Enhanced biometric template protection schemes for securing face recognition in IoT environment

With the increasing use of biometrics in Internet of Things (IoT) based applications, it is essential to ensure that biometric-based authentication systems are secure. Biometric characteristics can be accessed by anyone, which poses a risk of unauthorized access to the system through spoofed biometric traits. Therefore, it is important to implement secure and efficient security schemes suitable for real-life applications, less computationally intensive, and invulnerable. This work presents a hybrid template protection scheme for secure face recognition in IoT-based environments, which integrates Cancelable Biometrics and Bio-Cryptography. Mainly, the proposed system involves two steps: face recognition and face biometric template protection. The face recognition includes face image preprocessing by the Tree Structure Part Model (TSPM), feature extraction by Ensemble Patch Statistics (EPS) technique, and user classification by multi-class linear support vector machine (SVM). The template protection scheme includes cancelable biometric generation by modified FaceHashing and a Sliding-XOR (called S-XOR) based novel Bio-Cryptographic technique. A user biometric-based key generation technique has been introduced for the employed Bio-Cryptography. Three benchmark facial databases, CVL, FEI, and FERET, have been used for the performance evaluation and security analysis. The proposed system achieves better accuracy for all the databases of 200-dimensional cancelable feature vectors computed from the 500-dimensional original feature vector. The modified FaceHashing and S-XOR method shows superiority over existing face recognition systems and template protection