Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

In the present competitive environment, a farmer needs better education, business expertise and good knowledge of technologies and tools to be successful in agriculture. Farmers usually select crop for cultivation according to their traditional knowledge and past experience in farming, but a farmer’s predictions may go wrong due to natural disaster. Thus, decision-making tool need to be developed to help farmers to take decision on crop cultivation. In this paper, decision-making tool was developed for selecting the suitable crop that can be cultivated in a given agricultural land. In the present study, 26 input variables were identified and categorized into six broad heads of main variables such as soil, water, season, input, support and infrastructure. Each main variable has several sub-variables. The priority weights for the variables were determined using the dominance-based rough set approach. In order to convert sub-variable sequences to main variable sequences, evaluation scores of each main variable were calculated by applying the weights of sub-variables and by using simple additive method. Finally, the evaluation scores were applied to Johnson’s reduct algorithm and classification rules were generated. The developed tool predicts each site in the datasets into one of the three crops such as paddy, groundnut and sugarcane. In order to validate the performance of the tool, the same datasets were predicted again by agriculture experts. The results obtained from the tool showed 92% agreement with the results obtained from the experts. Thus, the tool is a feasible tool for cultivating the suitable crops in the agricultural sites. © 2017, The Natural Computing Applications Forum.

Department of Computer Applications

School of Information Technology and Engineering

Vellore Campus

Department of Software and Systems Engineering

Neural Computing and Applications

Decision-making tool for crop selection for agriculture development

In this paper, rough, fuzzy and soft set approaches have been integrated to develop a multi-class classification model to assist the farmers in taking decision on crop cultivation for a given agriculture land. The model is divided into three major sections, namely weight calculation of variables, conversion of continuous data to fuzzified values and classification rule generation. Dominance-based rough set approach is used for the calculation of relative weights of variables. Fuzzy proximity relation is applied to convert the continuous data into fuzzified values. Bijective soft set approach is used to generate classification rules for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi. The developed model has been tested with agriculture dataset which showed 92% accuracy for the validation dataset and proved to be confident and robust for agriculture development. Further, the performance of the proposed model is compared with three popular classifiers such as naïve Bayes, support vector machine and J48. The obtained experimental results showed high predictive performance, and the potential of the proposed model is compared with the other classifiers. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

Soft Computing

Hybrid rough fuzzy soft classifier based multi-class classification model for agriculture crop selection

A hybrid soft decision model has been developed in this paper to take decision on agriculture crop that can be cultivated in a given experimental land by integrating few soft computing techniques. The proposed model comprises of three parts, namely weight calculation, classification and prediction. Twenty-seven input criteria were categorized into seven broad criteria, namely soil (11 sub-criteria), water (2 sub-criteria), season (no sub-criterion), input (6 sub-criteria), support (2 sub-criteria), facilities (3 sub-criteria) and risk (2 sub-criteria). In the proposed model, relative weights of main criteria were calculated using Shannon’s Entropy method and relative weights of sub-criteria in each main criterion were calculated using rough set approach. As VIKOR method is effective in sorting the alternatives, it is used to determine the ranking index of main criteria in this study. A soft decision system was constructed from the results of rough set method, VIKOR method and Shannon’s Entropy method. Classification rules were generated for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi based on the soft decision system using bijective soft set approach. The developed model predicts each site in the validation dataset into one of the five crops. The performance of the proposed model has been sanity checked by agriculture experts. © 2016, The Natural Computing Applications Forum.

Multi-class classification using hybrid soft decision model for agriculture crop selection

Digital images are widely distributed today over the internet and through other mediums. There are powerful digital image processing tools which have made perfect image duplication a trivial procedure. Therefore, security, copyright protection, and integrity verification of digital video have become an urgent issue in the digital world, which can be achieved by means of a technique called digital watermarking. The issues of watermarking are to achieve imperceptibility, robustness, payload and security simultaneously. This paper presents a new Quaternion Curvelet Transform (QCT) based video watermarking technique for embedding a video on another video in a secure and optimized way. However, many of the existing techniques are unable to handle the problem of rotation, translation and scaling attacks on watermarked video. This study presents a novel embedding approach where in the quaternion transform followed by the traditional Curvelet transform is able to overcome the above disadvantages. In this paper, we first represent the color cover video frames in the quaternion form and subsequently generate Quaternion Curvelet Transform (QCT) coefficients for each such quaternion frames. Second, in order to withstand the desynchronization causes due to the geometrical attacks, the Harris corner detection algorithm is used to determine the invariant feature points on the QCT coefficients, which is followed by the calculation of energy for each block centered on those feature points. Third, in order to maintain the trade-off among the watermarking characteristics, the optimized location for embedding the watermark frames is determined using the cuckoo search optimization algorithm. Finally, to attain the authenticity, an authentication key generation procedure is employed using the owner’s biometric image and the watermarked frames. The experimental results prove that the proposed method is promising in terms of robustness, imperceptibility and security to most notable image processing attacks, geometrical attacks, and video processing attacks among the various conventional watermarking algorithms. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Department of Digital Communications

Multimedia Tools and Applications

RST invariant robust video watermarking algorithm using quaternion curvelet transform

VIT Unitversity

The European Physical Journal Plus

Chaos based video encryption using maps and Ikeda time delay system

Agriculture crop selection cannot be formulated from one criterion but from multiple criteria. A list of criteria for crop selection was identified through literature survey and agricultural experts. The identified criteria were grouped into seven main criteria namely, soil, water, season, input, support, facilities and threats. In this paper, Mahalanobis Taguchi system based tool was developed for identification of useful set of criteria which is a subset of the original criteria, for taking decision on crop selection in a given agriculture land. The combination of Mahalanobis distance and Taguchi method is used for identification of important criteria. Matlab software was used to develop the tool. After entering the values for each main criteria in the tool, it will process the value and identify the useful sub-criteria under each main criteria for selecting the suitable crop in a given agriculture land. Instead of considering all criteria, one can use these useful set of criteria under each main criteria for taking decision on crop selection in agriculture. © 2016, Indian Academy of Sciences.

Fulltext

Sādhanā

Mahalanobis Taguchi system based criteria selection tool for agriculture crops

Telemedicine is a rapidly developing application of clinical medicine where medical information is transferred through the internet and other networks for the purpose of consulting, and remote medical procedures or examinations. This paper presents a novel neural network inspired watermarking technique, to enhance the authentication of the transmitted sensitive medical images over telemedicine network. In order to examine the medical images, the multiple scan images of same type or different types can be considered as watermarks and the same are trained using Bidirectional Associative Memory (BAM) neural network. The resultant weight matrix is embedded in the less correlated Principal Component Analysis (PCA) components of the wavelet coefficients of the cover video frames using additive embedding type of watermark. At the receiver end, the same BAM neural network is used with the randomly generated target matrix and the extracted weight matrix as input. As a result of this, the input matrix will be obtained. From the input matrix, the physician can extract their own information or scan images using their private or secret key. The proposed watermarking technique is validated with the existing systems in terms of imperceptibility, robustness and watermark capacity using the metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Cross Correlation (NCC), Bit Correction Rate (BCR), Detection rate, Receiver Operating Characteristics (ROC) and payload. The performance of the proposed system is evaluated by introducing various notable image processing attacks, geometrical attacks and video processing attacks on watermarked video. The experimental results demonstrate that the proposed technique has good perceptual quality of 50.6292 dB in terms of PSNR value, robustness of about nearly 1.0000 in terms of NCC value and payload of 1000 watermarks. © 2015, Springer Science+Business Media New York.

A bi-directional associative memory based multiple image watermarking on cover video

India being one of the major producers of fish contributes 5.5 percent of global fish production and ranks second in the world after China. The production of aquaculture mainly depends on the quality of land selected for aqua farming. Neural Network algorithms have been applied to classify the aquaculture sites based on 6 input variables viz., water, soil, support, infrastructure, input and risk factor. An artificial neural network (ANN) consists of huge number of interconnected elements called neurons that work together to solve a specific problem. An Artificial Neural network can be used for classification, prediction, pattern recognition etc., through a learning process. In this paper, the models were constructed using three Neural Network algorithms viz., Back Propagation Network (BPN), Radial Basis Function (RBF) and Linear Vector Quantization (LVQ). The models classify each aquaculture site into 3 classes viz., suitable, moderate and unsuitable. From the results of the three models, it has been found that Radial Basis Function model not only gives accurate results but also time taken for training the dataset is less when compared with the other two Neural Network models. The results obtained from the neural network models were validated with the results of the fuzzy model. © 2016, Categorization of the EU Countries in the Context of Agricultural Production. All rights reserved.

Agris on-line Papers in Economics and Informatics

Aqua Site Classification Using Neural Network Models

The advancements in network technologies and processing of multimedia contents have provided the way for the distribution and sharing of multimedia contents through networks. This in turn has increased the demand for protecting the multimedia contents in terms of authentication, proof of ownership, copy control etc., which can be achieved by means of what is called digital watermarking. The challenges in watermarking techniques are how to achieve the imperceptibility, robustness and payload simultaneously. This paper presents a new bit plane sliced, scrambled color image watermark embedded on the color cover video using hybrid transforms such as Contourlet Transform (CT), Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) transformations with good imperceptibility, high robustness and at an information rate of (N − number of motion frames) / 24 images per second of the video, where N is the total number of frames in the video. In order to achieve a good level of imperceptibility, we perform the following: First, we slice the color watermark image into 24 slices using the bit plane slicing mechanism. Subsequently, the so called Arnold transformation key is used to scramble those slices, to achieve first-level of security. Thus, an authenticated receiver with an appropriate key alone can descramble the received slices. Second, we embed those scrambled slices on one of the DWT mid-frequency coefficients (LH band) of successive 1-level CT non-motion frames of color cover video. The non-motion frames are identified using the histogram difference based shot boundary detection algorithm. Third, in order to the provide second-level of security, we generate a random eigen vector from the color watermark image, using co-variance matrix and maximum eigen value and then embed it on another DWT mid-frequency coefficients (HL band). Thus, embedding only the slices (not an entire image) will improve the level of imperceptibility. The midfrequency embedding location can withstand against all low pass and hig pass filtering attacks; thereby it increases the level of robustness. Thus, the proposed system is suitable for authentication. Finally, as far as payload is concerned, we need only 24 non-motion frames for embedding our watermark on to the cover video. Hence the remaining frames can be utilized for embedding other color images. Our simulation results prove that the proposed system provides trustworthy performance against various notable image processing attacks, multiple attacks, geometrical attacks, and temporal attacks. © Springer Science+Business Media New York 2015.

A robust color video watermarking scheme based on hybrid embedding techniques

Decision making is the study of finding and selecting alternatives in order to obtain the best solution based on several criteria and taking into account the decision-maker’s expectations. The decisions are made in an environment where the collection of information, alternatives, preferences are available. Judging the alternatives characterized by multiple criteria is difficult in decision making. To overcome this, tools to help decision making with multiple criteria which is termed as Multi criteria decision making methods gained popularity. There are two levels in multi-criteria decision making which includes managerial level and engineering level. The level which defines the goals and selects the final optimal alternative is managerial level and the level which defines the alternatives and performs ranking of alternatives is engineering level. In this paper, several multi criteria decision making methods are reviewed and their applications are surveyed in detail. © RJPT All right reserved.

Research Journal of Pharmacy and Technology

A Brief Survey of Decision Making Methods and its Applications in Various Domains

Multilevel inverters are replacing conventional two level inverters due to reduced dv/dt stresses, common mode voltages, and lower harmonic distortion. They are finding applications in motor drives, active filters and power conditioning. Cascaded Multilevel inverter is the most used topology among Multilevel Inverters, due to reduced number of components at higher voltage levels. Constant frequency PWM is normally employed for cascaded multilevel inverter. However, to overcome EMI and higher harmonic distortion, a random modulation technique, adopting pseudorandom carrier modulation is employed. This improves the spread spectra effect; where in the signal generated with a particular bandwidth is deliberately spread in the frequency domain, resulting in a signal with wider bandwidth. The pseudorandom carrier involves random selection of two triangular carriers of same frequency but opposite in phase depending on the output states of pseudorandom generator. Pseudorandom carrier modulation is employed on various kinds of sub harmonic PWM techniques. Total Harmonic Distortion (THD) and Harmonic Spread Factor (HSF) are considered for evaluating the performance of these methods. HSF was found to be 5% less in pseudorandom carrier scheme compared to constant frequency scheme. Current THD was found to be less than 4% in all schemes. © 2014 IEEE.

Electronics

School of Electronics Engineering

Chennai Campus

Department of Physics

School of Advanced Sciences

2014 IEEE International Symposium on Circuits and Systems (ISCAS)

Pseudorandom carrier based subharmonic PWM for cascaded multilevel inverters

A novel dynamic pricing scheme for contributing peers in the VoD system

International Journal of Advanced Media and Communication

Video segmentation using Metropolis Hastings Algorithm for the VCR operations

Aquaculture development suitable location is not formulated from one criterion but from multiple criteria. A list of criteria was selected by reviewing the literature and consultation with aquaculture experts and then classified into five categories namely; water, soil, support, infrastructure and risk factor. Using these criteria a Mahalanobis Taguchi System (MTS) based tool was developed to facilitate the selection of prime set of criteria, which is a subset of the original criteria for suitable aquaculture site identification. Mahalanobis Distance (MD) was used for constructing a measurement scale for the aquaculture system. The principles of Taguchi method was used for screening the important criteria in the system. The information required to make an assessment was entered into the computer program. The computer program then processes the information and produces the prime set of criteria for each category. The software program is a valuable tool for the identification of prime set of criteria within each category. One can use these prime set of criteria within each category instead of all criteria for the identification of optimal location in shrimp aquaculture.