Recognition Techniques and System Classification

The voice is most primary mode of Communication among of human being. The communication among human computer interaction is called human computer interface. Voice potential of being important of interaction with computer .This paper gives an overview of major technological perspective and appreciation of the fundamental progress of recognition and also gives overview technique developed in each stage of recognition. This paper helps in choosing the technique along with their relative merits & demerits. A comparative study of different technique is done as per stages. This paper is concludes with the decision on feature direction for developing technique in human computer interface system using Hindi Language

Aspect Based Sentiment Analysis using Various Supervised Classification Techniques: An Overview

The Sentiment Analysis (SA) work is concerned with identifying aspect terms and categories and categorising emotions (positive, negatively, conflict, and neutral) in ratings and reviews. When it comes to subjectivity, it's typical to divide sentences into objective phrases that include accurate information and subjective statements that include express ideas, beliefs, and perspectives on a given topic. Various existing researchers have already done a lot of work in sentiment analysis with various methods, including aspect extraction. This paper proposed a systematic literature analysis of numerous sentiment analysis using supervised and unsupervised classification techniques. We investigate a few features extraction Natural language Processing (NLP) techniques used to identify aspects of machine learning for the detection of sentiment. An extensive experiment analysis, we discuss the findings of the study, challenges of the current and define the problem statement for the future directio

Quality Enhancement for Underwater Images using Various Image Processing Techniques: A Survey

Underwater images are essential to identify the activity of underwater objects. It played a vital role to explore and utilizing aquatic resources. The underwater images have features such as low contrast, different noises, and object imbalance due to lack of light intensity. CNN-based in-deep learning approaches have improved underwater low-resolution photos during the last decade. Nevertheless, still, those techniques have some problems, such as high MSE, PSNT and high SSIM error rate. They solve the problem using different experimental analyses; various methods are studied that effectively treat different underwater image distorted scenes and improve contrast and color deviation compared to other algorithms. In terms of the color richness of the resulting images and the execution time, there are still deficiencies with the latest algorithm. In future work, the structure of our algorithm will be further adjusted to shorten the execution time, and optimization of the color compensation method under different color deviations will also be the focus of future research. With the wide application of underwater vision in different scientific research fields, underwater image enhancement can play an increasingly significant role in the process of image processing in underwater research and underwater archaeology. Most of the target images of the current algorithms are shallow water images. When the artificial light source is added to deep water images, the raw images will face more diverse noises, and image enhancement will face more challenges. As a result, this study investigates the numerous existing systems used for quality enhancement of underwater mages using various image processing techniques. We find various gaps and challenges of current systems and build the enhancement of this research for future improvement. Aa a result of this overview is to define the future problem statement to enhance this research and overcome the challenges faced by previous researchers. On other hand also improve the accuracy in terms of reducing MSE and enhancing PSNR etc

DFQIoV: Design of a Dynamic Fan-Shaped-Clustering Model for QoS-aware Routing in IoV Networks

Internet of Vehicles (IoV) is a steadily growing field of research that deals with highly ad-hoc wireless networks. These networks require design of high-speed & high-efficiency routing models, that can be applied to dynamically changing network scenarios. Existing models that perform this task are highly complex and require larger delays for estimation of dynamic routes. While, models that have faster performance, do not consider comprehensive parameters, which limits their applicability when used for large-scale network scenarios. To overcome these limitations, this text proposes design of a novel dynamic fan-shaped clustering model for QoS-aware routing in IoV networks. The model initially collects network information sets including node positions, & energy levels, and combines them with their temporal packet delivery & throughput performance levels. These aggregated information sets are processed via a hybrid bioinspired fan shaped clustering model, that aims at optimization of routing performance via deployment of dynamic clustering process. The model performs destination-aware routing process which assists in reducing communication redundances. This is done via a combination of Elephant Herding Optimization (EHO) with Particle Swarm Optimization (PSO), which integrates continuous learning for router level operations. The integrated model is able to reduce communication delays by 5.9%, while improving energy efficiency by 8.3%, throughput by 4.5%, and packet delivery performance by 1.4% under different network scenarios. Due to which the proposed model is capable of deployment for a wide variety of dynamic network scenarios

Successor features based multi-agent RL for event-based decentralized MDPs

Decentralized MDPs (Dec-MDPs) provide a rigorous framework for collaborative multi-agent sequential decisionmaking under uncertainty. However, their computational complexity limits the practical impact. To address this, we focus on a class of Dec-MDPs consisting of independent collaborating agents that are tied together through a global reward function that depends upon their entire histories of states and actions to accomplish joint tasks. To overcome scalability barrier, our main contributions are: (a) We propose a new actor-critic based Reinforcement Learning (RL) approach for event-based Dec-MDPs using successor features (SF) which is a value function representation that decouples the dynamics of the environment from the rewards; (b) We then present Dec-ESR (Decentralized Event based Successor Representation) which generalizes learning for event-based Dec-MDPs using SF within an end-to-end deep RL framework; (c) We also show that Dec-ESR allows useful transfer of information on related but different tasks, hence bootstraps the learning for faster convergence on new tasks; (d) For validation purposes, we test our approach on a large multi-agent coverage problem which models schedule coordination of agents in a real urban subway network and achieves better quality solutions than previous best approaches

Segmentation and Descriptors for Pattern

Image segmentation is an essential preliminary step in most automatic pictorial pattern recognition. The purpose of representation and description is used to be the application of Pattern. In the application of image processing, we have to choose an approach and to do description, just like recognition of the image. Keywords: image processing, Patter

Homotopy perturbation method to space–time fractional solidification in a finite slab

AbstractA mathematical model describing the space and time fractional solidification of fluid initially at its freezing temperature contained in a finite slab under the constant wall temperature is presented. The approximate analytical solution of this problem is obtained by the homotopy perturbation method. The results thus obtained are compared with exact solution of integer order (β=1,α=2) and are good agreement. The problem has been studied in detail by considering different order time and space fractional derivatives. The temperature distribution and the moving interface position for different fractional order space and time derivatives are shown graphically. The model and the solution are the generalization of the previous works and include them as special cases

405 nm light exposure of osteoblasts and inactivation of bacterial isolates from arthroplasty patients : potential for new disinfection applications?

Infection rates after arthroplasty surgery are between 1-4 %, rising significantly after revision procedures. To reduce the associated costs of treating these infections, and the patients' post-operative discomfort and trauma, a new preventative method is required. High intensity narrow spectrum (HINS) 405 nm light has bactericidal effects on a wide range of medically important bacteria, and it reduced bacterial bioburden when used as an environmental disinfection method in a Medical Burns Unit. To prove its safety for use for environmental disinfection in orthopaedic theatres during surgery, cultured osteoblasts were exposed to HINS-light of intensities up to 15 mW/cm2 for 1 h (54 J/cm2). Intensities of up to 5 mW/cm2 for 1 h had no effect on cell morphology, activity of alkaline phosphatase, synthesis of collagen or osteocalcin expression, demonstrating that under these conditions this dose is the maximum safe exposure for osteoblasts; after exposure to 15 mW/cm2 all parameters of osteoblast function were significantly decreased. Viability (measured by protein content and Crystal Violet staining) of the osteoblasts was not influenced by exposure to 5 mW/cm2 for at least 2 h. At 5 mW/cm2 HINS-light is an effective bactericide. It killed 98.1 % of Staphylococcus aureus and 83.2 % Staphylococcus epidermis populations seeded on agar surfaces, and is active against both laboratory strains and clinical isolates from infected hip and knee arthroplasties. HINS-light could have potential for development as a method of disinfection to reduce transmission of bacteria during arthroplasty, with wider applications in diverse surgical procedures involving implantation of a medical device. With kind permission of full reproduction from eCM journal (www.ecmjournal.org). Founded by scientists for the benefit of Science rather than profit

AN OVERVIEW OF FOURIER TRANSFORM ON IMAGE PROCESSING

The recognition of an images are important in the digital image processing. In this paper we introduce the definition of Fourier Transform and it's properties through which the solution of the problem will be easier than expected and also describe that what is the roll of Fourier transform in image recognition