Though many novel image steganographic techniques have been proposed in the recent years, PVD modulus method produces high quality stego images, when compared with other methods. But a significant drawback of PVD modulus method is low capacity. The hiding capacity is extremely low in PVD modulus method, when compared with other methods like LSB, etc. The reason for the low hiding capacity is that, with PVD modulus method we divide the pixel blocks in to smooth blocks where, the pixel value difference is low and edge blocks where the pixel value difference is high. In this paper, we propose a technique, which is a combination of PVD modulus and LSB method. As per the experimental results hiding capacity increases enormously.
KeywordsSteganography, LSB Method, PVD Modulus Method.
People may quickly obtain information through a variety of channels including social media, blogs, websites, and other online resources. These platforms have made it possible for information to be shared more easily. As a result, the amount of time that individuals spend on various social networking applications has increased. This research predicts the effects of human exposure to social networks in the near future. In this work a competent model for predicting the ill-effects is provided, that is both accurate and efficient. This model represents a combination of the independent models that have been operating independently so far. Thus each of these models makes a forecast and ultimate selection is decided based on whether or not there exists a majority of convergence of results from the operation of various models. By employing the majority voting system, this strategy attempts to take the benefits of the predictions produced by all the models while also to reduce the inaccuracies generated by each model. Theoretically, this model should outperform the use of individual models in terms of performance. Important features are extracted from the datasets using the proposed model, and the extracted features are then classified using an ensemble model that consists of four popular machine learning models: support vector machines (SVMs), logistic regression (logistic regression), random forest (random forest classification), and neural networks (NN). We have analyzed our prediction performance with the existing methods for number of times by changing the train set and test set data. In all the cases our novel method has been predicting with 3% to 4% improved performance in accuracy, precision, F-1 score, and specificity. From the dataset it has been achievable to attain the highest training and testing accuracy from among the existing models.Povzetek: Optimizacija kakovosti napovedovanja slabih učinkov intenzivne izpostavljenosti človeka družbenim omrežjem z uporabo ansambelske metode.
Social Network (SN) is of avail for sharing information among individuals and communities for different purposes like sharing opinions, feelings, photos, videos and many others. Since the start of the COVID-19 epidemic and the ensuing limitations, the use of Apps on smart devices has exploded. In-line with how much time is spent on SN by a person, the manifestation of physical and mental problems are found in diverse patterns. In this review a comparative account is presented linking the time spent by individuals on social network and the patterns of the resultant health problems in course of time. Most of the earlier studies categorize the users in to various groups based on the time spent on social network. Then they describe the apparent problems that are faced, under two categories, due to the extensive time spent by the users on various social network applications. Finally, the review presents a comprehensive idea of the different analytical techniques used for finding problems faced with respect to the time spent and frequency of social network use. The results on the whole present a variegated picture as regards existence of correlation between intensity of usage and incidence of health problems.
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