All the works on writer's handwritten letters detection was based on the western languages and partly Chinese and Hindi, and there is a little study on Persian handwritten letters detection. Accordingly, in this paper a method is proposed to distinguish scanned Persian handwritten texts with image processing techniques. The proposed method assumes that the writer's handwritten are available in separate letters. The system trains with features extraction of these separate letters and then the trained system is used to detect individual handwritten among some indistinctive handwritten texts. The characteristics of our proposed method including highspeed of training in too much number of handwritten, is the content inattention and visual features considering. The results of procedures on 100 persons also admitted that the proposed method has a very good performance on Persian handwritten texts detection.

Although “a picture is worth a thousand words,” this may not be enough to get your post seen on social media. This study’s main objective was to determine the best ways to characterize a photo in terms of viral marketing and public appealing. We have to obtain this dataset for this reason from the social media site such as Instagram. A total of 1.4 million hashtags were used in the 570,000 photos that we crawled. Prior to training the text generation module to produce such popular hashtags, we had to determine the components and features of the photo. We trained a multilabel image classification module using a ResNet neural network model for the first section. In order to create hashtags pertaining to their popularity, we trained a cutting‐edge GPT‐2 language model for the second portion. This work differs from others in that, and it initially offered a cutting‐edge GPT‐2 model for hashtag generation using a combination of the multilabel image classification module. The popularity issues and ways to make an Instagram post popular are also highlighted in our essay. Social science and marketing research can both be conducted on this subject. Which content can be considered popular from the perspective of consumers can be researched in the social science setting. As a marketing strategy, end users can help by offering such well‐liked hashtags for social media accounts. This essay adds to the body of knowledge by demonstrating the two possible uses of popularity. Compared to the base model, our popular hashtag generating algorithm creates 11% more relevant, acceptable, and trending hashtags, according to the evaluation that was carried out.

Many different classification algorithms can be use in order to analyze, classify and predict data. Learning classifier system (LCS) which is known as a genetic base machine learning system, combines the machine learning with evolutionary computing and other heuristics to produce an adaptive system that learns to solve a particular problem. This paper uses the Michigan style LCS, in the context of bank customer satisfaction to classify customers into two different groups: unsatisfied/satisfied customers. Three different Rule Compaction strategies are used to compare the rule population’s accuracy and micro/macro population size. The result specifies features that mostly influence prediction.