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This paper deals with leaf rot disease detection for betel vine (Piper betel L.) based on image processing algorithm. The measurement of plant features is a fundamental element of plant science research and related applications. The information related to plant features is especially useful for its applications in plant growth modeling, agricultural research and on farm production. Few methods have been applied in leaf rot disease detection for betel vine leaf (Piper Betel L.). Traditional direct measurement methods are generally simple and reliable, but they are time consuming, laborious and cumbersome. In contrast, the proposed vision-based methods are efficient in detecting and observing the exterior disease features. In the present investigation, image processing algorithms are developed to detect leaf rot disease by identifying the color featureof the rotted leaf area.Subsequently, the rotted area was segmented and area of rotted leaf portion was deduced from the observed plant feature data. The results showed a promising performance of this automatic vision-based system in practice with easy validation. This paper describes the steps to achieve an efficient and inexpensive system acceptable to the farmers and agricultural researchers as well for studying leaf rot disease in betel vine leaf.

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

Software testing involves verification and validation of the software to meet the requirements elucidated by customers in the earlier phases and to subsequently increase software reliability. Around half of the resources, such as manpower and CPU time are consumed and a major portion of the total cost of developing the software is incurred in testing phase, making it the most crucial and time-consuming phase of a software development lifecycle (SDLC). Also the fault detection process (FDP) and fault correction process (FCP) are the important processes in SDLC. A number of software reliability growth models (SRGM) have been proposed in the last four decades to capture the time lag between detected and corrected faults. But most of the models are discussed under static environment. The purpose of this paper is to allocate the resources in an optimal manner to minimize the cost during testing phase using FDP and FCP under dynamic environment. An elaborate optimization policy based on optimal control theory for resource allocation with the objective to minimize the cost is proposed. Further, genetic algorithm is applied to obtain the optimum value of detection and correction efforts which minimizes the cost. Numerical example is given in support of the above theoretical result. The experimental results help the project manager to identify the contribution of model parameters and their weight.

Many illegal copies of original digital videos are being made, as they can be replicated perfectly through the Internet. Thus, it is extremely necessary to protect the copyrights of the owner and prevent illegal copying. This paper presents a novel approach to digital video watermarking for copyright protection using two different algorithms, whereby successive estimation of a statistical measure was used to detect scene boundaries and watermark was embedded in the detected scenes with discrete wavelet transform. Haar wavelet was used for decomposition. For embedding, the approaches used were (i) the detailed subband (LH subband) and (ii) the approximate subband (LL subband) of the cover video. Imperceptibility, robustness, and channel capacity were measured using both algorithms. The system was tested for robustness in the presence of 15 different attacks of five different categories, and, under multiple attacks, ensured that a wide spectrum of attack analysis has been done. The performance metrics measured included mean square error, peak signal-to-noise ratio, structural similarity index, normalized correlation, and bit error rate. The experimental results demonstrated the better visual imperceptibility and improved performance in terms of normalized correlation and bit error rate with embedding using the LL subband. Comparative analysis with existing schemes proved the improved robustness, better imperceptibility, and reduced computational time of both the proposed schemes.

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