Cloud computing is the rapidly growing model for providing resources to users over internet. Multimodal biometrics is an upcoming research area to explore for improving the security of cloud. In this work, a novel multimodal biometric fusion system using three different biometric modalities including face, ear, and gait, based on speed-up-robust-feature (SURF) descriptor along with genetic algorithm (GA) is anticipated. Artificial neural network (ANN) is utilised as a classifier for each biometric modality. Our novel approach has been effectively tested by means of dissimilar images analogous to subjects from three databases namely AMI Ear Database, Georgia Tech Face Database and CASIA Gait Database. Before going for the fusion, the SURF features are optimised using GA and cross validated using ANN. It is observed that, the amalgamation of face, ear and gait gives better performance in terms of accuracy, precision, recall and Fmeasure.
Cloud computing is the rapidly growing model for providing resources to users over internet. Multimodal biometrics is an upcoming research area to explore for improving the security of cloud. In this work, a novel multimodal biometric fusion system using three different biometric modalities including face, ear, and gait, based on speed-up-robust-feature (SURF) descriptor along with genetic algorithm (GA) is anticipated. Artificial neural network (ANN) is utilised as a classifier for each biometric modality. Our novel approach has been effectively tested by means of dissimilar images analogous to subjects from three databases namely AMI Ear Database, Georgia Tech Face Database and CASIA Gait Database. Before going for the fusion, the SURF features are optimised using GA and cross validated using ANN. It is observed that, the amalgamation of face, ear and gait gives better performance in terms of accuracy, precision, recall and Fmeasure.
This chapter explores the impact of distributed computing on data analytics and business insights. We first define distributed computing and its role in data analytics, highlighting the benefits of using distributed computing for large-scale data processing. We also discuss the importance of business insights in decision-making, providing examples of the impact of data analytics on business operations. We then examine how distributed computing supports data analytics, outlining the key features and advantages of several popular distributed computing platforms. In particular, we focus on the benefits of using distributed computing for large-scale data processing, real-time data analytics, and machine learning. Despite the many advantages of distributed computing for data analytics and business insights, there are also challenges and limitations to consider. We discuss scalability, latency, integration, and maintenance issues that can impact the effectiveness of distributed computing for data analytics and business insights. In conclusion, we summarize the benefits and challenges of using distributed computing for data analytics and business insights. We argue that the benefits of using distributed computing far outweigh the challenges, providing organizations with valuable insights into their operations and competitive edge in their respective markets. We expect to see continued growth and adoption of distributed computing technologies in the field of data analytics and business insights.
In 5G and beyond 5G wireless communication networks, the NOMA scheme is widely considered a major non-orthogonal access technique for improving system capacity and data rates. The main challenges in current NOMA systems are limited channel feedback and the difficulty of integrating it with advanced adaptive coding and modulation algorithms. This study analyses S-LSTM-based DL NOMA receivers in i.i.d. Nakagami-m fading channel circumstances as opposed to previously presented solutions. The LSTM has the advantage of responding dynamically to changing channel conditions. When compared to a typical NOMA system, a typical NOMA system has a 12% lower outage probability, a 39% increase in net throughput, and a maximum SER reduction of 48%. Complex modulated M-ary PSK and M-ary QAM data symbols are employed in D/L NOMA transmission. Classic receivers such as LS and MMSE are outperformed by the S-LSTM-based DL-NOMA receiver. The CP and non-linear clipping noise simulation curves compare the performance of the MMSE and LS receivers with that of the DL NOMA receiver in real-time propagation circumstances. The DL-based detector outperforms the MMSE for SNRs greater than 15 dB because the S-LSTM method is more robust than the clipping noise.
Ecogenomics is the scientific approach to understand the relationship between structural and functional aspects of genomes with biotic/abiotic environmental factors. The classification of ecology depends upon the overall complexity (behavioral and population ecology) of an organism (plant and animal ecology) and system under investigation (soil and forest ecology). The molecular techniques adopted by these various ecology branches result in a new field known as ecological genomics or ecogenomics that focuses on an organism's overall development during the evolutionary period. It is an interdisciplinary research field covering ecological science, microbiology, 176environmental and molecular biology, toxicology, physiology, chemistry, etc. The study related to the ecology of plants has a direct relationship with the adaptation mechanism because plants have no alternative to cope with the environment in which they grow. The world is facing biodiversity loss at an alarming rate, with a loss of 90% crop varieties in a century from the field. It is essential to select the genes in biological pathways responsible for an organism's stability in the ecological system. The challenge is to understand the basic phenomenon behind adaptation, migration, inbreeding mechanism of endangered or critically endangered species. in ecogenomics, we know that the genes are responsible for the effective management strategies from a conservation point of view. Therefore, in this chapter, we discuss various aspects of community shaping and visualization. The community's function and structure need to be studied due to the availability of plenty of molecular data. Further, the role of ecogenomics and multiomic approaches in conservation and management will also be emphasized.
Biometrics is a technique used to define, assess, and quantify a person's physical and behavioral property. In recent history, deep learning has shown impressive progress in several places, including computer vision and natural language processing for supervised learning. Since biometrics de
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