Convolutional Neural Network Model Based Human Wearable Smart Ring System: Agent Approach

The Internet of Things has a set of smart objects with smart connectivity that assists in monitoring real world environment during emergency situations. It could monitor the various applications of emergency situations such as road accidents, criminal acts including physical assaults, kidnap cases, and other threats to people’s way of  life. In this work, the proposed work is to afford real time services to users in emergency situations through Convolutional Neural Networks in terms of efficiency and reliable services. Finally, the proposed work has simulated with respect to the performance parameters of the proposed scheme like the probability of accuracy and processing time

Synergistic degradation of diazo dye Direct Red 5B by Portulaca grandiflora and Pseudomonas putida

Plants and bacterial consortium of Portulaca grandiflora and Pseudomonas putida showed complete decolorization of a sulfonated diazo dye Direct Red 5B within 72 h, while in vitro cultures of P. grandiflora and P. putida independently showed 92 and 81 % decolorization within 96 h, respectively. A significant induction in the activities of lignin peroxidase, tyrosinase, 2,6-dichlorophenol indophenol reductase and riboflavin reductase was observed in the roots of P. grandiflora during dye decolorization; whereas, the activities of laccase, veratryl alcohol oxidase and 2,6-dichlorophenol indophenol reductase were induced in the cells of P. putida. Plant and bacterial enzymes in the consortium gave an enhanced decolorization of Direct Red 5B synergistically. The metabolites formed after dye degradation analyzed by UV-Vis spectroscopy, Fourier transformed infrared spectroscopy and high performance liquid chromatography confirmed the biotransformation of Direct Red 5B. Differential fate of metabolism of Direct Red 5B by P. grandiflora, P. putida and their consortium were proposed with the help of gas chromatography-mass spectroscopy analysis. P. grandiflora metabolized the dye to give 1-(4-diazenylphenyl)-2-phenyldiazene, 7-(benzylamino) naphthalene-2-sulfonic acid, 7-aminonaphthalene-2-sulfonic acid and methylbenzene. P. putida gave 4-hydroxybenzenesulfonic acid and 4-hydroxynaphthalene-2-sulfonic acid and benzamide. Consortium showed the formation of benzenesulfonic acid, 4-diazenylphenol, 6-aminonaphthalen-1-ol, methylbenzene and naphthalen-1-ol. Consortium achieved an enhanced and efficient degradation of Direct Red 5B. Phytotoxicity study revealed the nontoxic nature of metabolites formed after parent dye degradation. Use of such combinatorial systems of plant and bacteria could prove to be an effective and efficient strategy for the removal of textile dyes from soil and waterways

Treatment of synthetic textile wastewater containing dye mixtures with microcosms

The aim was to assess the ability of microcosms (laboratory-scale shallow ponds) as a post polishing stage for the remediation of artificial textile wastewater comprising two commercial dyes (basic red 46 (BR46) and reactive blue 198 (RB198)) as a mixture. The objectives were to evaluate the impact of Lemna minor L. (common duckweed) on the water quality outflows; the elimination of dye mixtures, organic matter, and nutrients; and the impact of synthetic textile wastewater comprising dye mixtures on the L. minor plant growth. Three mixtures were prepared providing a total dye concentration of 10 mg/l. Findings showed that the planted simulated ponds possess a significant (p < 0.05) potential for improving the outflow characteristics and eliminate dyes, ammonium-nitrogen (NH4-N), and nitrate-nitrogen (NO3-N) in all mixtures compared with the corresponding unplanted ponds. The removal of mixed dyes in planted ponds was mainly due to phyto-transformation and adsorption of BR46 with complete aromatic amine mineralisation. For ponds containing 2 mg/l of RB198 and 8 mg/l of BR46, removals were around 53%, which was significantly higher than those for other mixtures: 5 mg/l of RB198 and 5 mg/l of BR46 and 8 mg/l of RB198 and 2 mg/l of BR46 achieved only 41 and 26% removals, respectively. Dye mixtures stopped the growth of L. minor, and the presence of artificial wastewater reduced their development

Convolutional Neural Network Model Based Human Wearable Smart Ring System: Agent Approach

The Internet of Things has a set of smart objects with smart connectivity that assists in monitoring real world environment during emergency situations. It could monitor the various applications of emergency situations such as road accidents, criminal acts including physical assaults, kidnap cases, and other threats to people’s way of  life. In this work, the proposed work is to afford real time services to users in emergency situations through Convolutional Neural Networks in terms of efficiency and reliable services. Finally, the proposed work has simulated with respect to the performance parameters of the proposed scheme like the probability of accuracy and processing time

SUPPORT VECTOR MACHINE BASED APPROACH FOR TRANSLATING VIDEO SCENERIES TO NATURAL LANGUAGE DESCRIPTIONS

Human uses communication language either by written, spoken or typed to describe visual the world around them. So, the study of text description for any video goes increasing. This paper represents a framework that gives output as a description for any video having a maximum size of 50 seconds by using natural language processing. The framework is divided into two sections called training and testing. The training section is used to train the video with its description like activities of objects present in that video. The trained data is stored into the database with its features of scenario of video. Another section is testing section. The testing section is used to test the video and retrieve the output as description of video. By using Natural language processing sentences are generated from objects and their activities present in the video

ANFIS Based Methodology for Sign Language Recognition and Translating to Number in Kannada Language

In the world of signing and gestures, lots of analysis work has been done over the past three decades. This has led to a gradual transition from isolated to continuous, and static to dynamic gesture recognition for operations on a restricted vocabulary. In gift state of affairs, human machine interactive systems facilitate communication between the deaf, and hearing impaired in universe things. So as to boost the accuracy of recognition, several researchers have deployed strategies like HMM, Artificial Neural Networks, and Kinect platform. Effective algorithms for segmentation, classification, pattern matching and recognition have evolved. The most purpose of this paper is to investigate these strategies and to effectively compare them, which can alter the reader to succeed in associate in nursing optimum resolution. This creates each, challenges and opportunities for signing recognition connected analysis. 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Sign Language Video Processing for Text Detection in Hindi Language

Sign language is a way of expressing yourself with your body language, where every bit of ones expressions, goals, or sentiments are conveyed by physical practices, for example, outward appearances, body stance, motions, eye movements, touch and the utilization of space. Non-verbal communication exists in both creatures and people, yet this article concentrates on elucidations of human non-verbal or sign language interpretation into Hindi textual expression. The proposed method of implementation utilizes the image processing methods and synthetic intelligence strategies to get the goal of sign video recognition. To carry out the proposed task implementation it uses image processing methods such as frame analysing based tracking, edge detection, wavelet transform, erosion, dilation, blur elimination, noise elimination, on training videos. It also uses elliptical Fourier descriptors called SIFT for shape feature extraction and most important part analysis for feature set optimization and reduction. For result analysis, this paper uses different category videos such as sign of weeks, months, relations etc. Database of extracted outcomes are compared with the video fed to the system as a input of the signer by a trained unclear inference system

Sign Language Video Processing for Text Detection in Hindi Language

Sign language is a way of expressing yourself with your body language, where every bit of ones expressions, goals, or sentiments are conveyed by physical practices, for example, outward appearances, body stance, motions, eye movements, touch and the utilization of space. Non-verbal communication exists in both creatures and people, yet this article concentrates on elucidations of human non-verbal or sign language interpretation into Hindi textual expression. The proposed method of implementation utilizes the image processing methods and synthetic intelligence strategies to get the goal of sign video recognition. To carry out the proposed task implementation it uses image processing methods such as frame analysing based tracking, edge detection, wavelet transform, erosion, dilation, blur elimination, noise elimination, on training videos. It also uses elliptical Fourier descriptors called SIFT for shape feature extraction and most important part analysis for feature set optimization and reduction. For result analysis, this paper uses different category videos such as sign of weeks, months, relations etc. Database of extracted outcomes are compared with the video fed to the system as a input of the signer by a trained unclear inference system

Euclidean Distance Based Classifier for Recognition and Generating Kannada Text Description from Live Sign Language Video

Sign language recognition has emerged in concert of the vital space of analysis in computer Vision. The problem long-faced by the researchers is that the instances of signs vary with each motion and look. Thus, during this paper a completely unique approach for recognizing varied alphabets of Kannada linguistic communication is projected wherever continuous video sequences of the signs are thought of. The system includes of three stages: Preprocessing stage, Feature Extraction and Classification. Preprocessing stage includes skin filtering, bar histogram matching. Eigen values and Eigen Vectors were thought of for feature extraction stage and at last Eigen value weighted Euclidean distance is employed to acknowledge the sign. It deals with vacant hands, so permitting the user to act with the system in natural manner. We have got thought of completely different alphabets within the video sequences and earned a hit rate of 95.25%