Browsing by Author "Jony, Anik Hassan"

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A Digital Data Hiding Technique with Missing Puzzle and Seek Algorithm
(IEEE, 2020-11) Islam, Md. Ashiqul; Tabassum, Tasfia; Hossen, Md. Sagar; Hossain, Shahed; Hossain, Mosharof; Jony, Anik Hassan
presently a day’s data dissemination over the world become progressively simpler because of quick web and advancement of various kind of technology, for this explanation individuals become increasingly stressed about their information security. For this nowadays people use steganography to make the information secure by hiding and blending the data that make them hard to perceive by hackers. For concealing mystery data in content and pictures, there exists a huge assortment of Steganography methods some are more mind-boggling than others and every one of them has particular solid and feeble focuses. We are looking for the calculation to discover the missing puzzle word which otherwise called mystery calculation by using seek algorithm. For improving the security of mystery message, the message is mixed utilizing onetime cushion plot before being covered and Figure content is at that point hidden in the spread. This is the most efficient data hiding security system and probably its increases the data security all over the world and maintain our privacy.
Hotel Review Analysis for the Prediction of Business Using Deep Learning Approach
(IEEE, 2021-04-12) Hossen, Md.Sagar; Jony, Anik Hassan; Tabassum, Tasfia; Islam, Md. Tanvir; Rahman, Md Mahfujur; Khatun, Tania
Sentiment analysis is a widely used topic in Natural Language Processing that allows identifying the opinions or sentiments from a given text. Social media is the scope for the customers to share their opinion over the products or services as part of customer reviews. Dissect this review has become an important factor for business analysis since online business is exponentially growing in today's techno-friendly competitive market. A large number of algorithms have been found in recent articles. Among those deep learning is an important approach. In the proposed methodology, long short-term memory (LSTM) and Gated recurrent units (GRUs) have been used to train the hotel review data where the accuracy rate of identifying customer opinion is 86%, and 84% respectively. The dataset is also tested by using Naïve Bayes, Decision Tree, Random Forest, and SVM. For Naïve Bayes obtains an accuracy of 75%, for Decision Tree obtains an accuracy of 71%, for Random Forest the accuracy is 82% and for SVM our accuracy result is 71%. Deep learning is used to obtain better business performance and also get the review from customers and also to predict the sentiment about customer review. Our algorithm works properly and gives better accuracy.
Hotel Review Analysis for the Prediction of Business Using Deep Learning Approach
(International Conference on Artificial Intelligence and Smart Systems (ICAIS), IEEE, 2021-04-12) Hossen, Md. Sagar; Jony, Anik Hassan; Tabassum, Tasfia; Islam, Md. Tanvir; Rahman, Md Mahfujur; Khatun, Tania
Sentiment analysis is a widely used topic in Natural Language Processing that allows identifying the opinions or sentiments from a given text. Social media is the scope for the customers to share their opinion over the products or services as part of customer reviews. Dissect this review has become an important factor for business analysis since online business is exponentially growing in today's techno-friendly competitive market. A large number of algorithms have been found in recent articles. Among those deep learning is an important approach. In the proposed methodology, long short-term memory (LSTM) and Gated recurrent units (GRUs) have been used to train the hotel review data where the accuracy rate of identifying customer opinion is 86%, and 84% respectively. The dataset is also tested by using Naïve Bayes, Decision Tree, Random Forest, and SVM. For Naïve Bayes obtains an accuracy of 75%, for Decision Tree obtains an accuracy of 71%, for Random Forest the accuracy is 82% and for SVM our accuracy result is 71%. Deep learning is used to obtain better business performance and also get the review from customers and also to predict the sentiment about customer review. Our algorithm works properly and gives better accuracy.