A combined Feature extraction technique for cancer classification based on deep learning approach

The quality and efficiency of generating face-swap images have been markedly strengthened by deep learning. For instance, the face-swap manipulations by DeepFake are so real that it is tricky to distinguish authenticity through automatic or manual detection. To augment the efficiency of distinguishing face-swap images generated by DeepFake from real facial ones, a novel counterfeit feature extraction technique was developed based on deep learning and error level analysis (ELA). It is related to entropy and information theory such as cross-entropy loss function in the final softmax layer. The DeepFake algorithm is only able to generate limited resolutions. Therefore, this algorithm results in two different image compression ratios between the fake face area as the foreground and the original area as the background, which would leave distinctive counterfeit traces. Through the ELA method, we can detect whether there are different image compression ratios. Convolution neural network (CNN), one of the representative technologies of deep learning, can extract the counterfeit feature and detect whether images are fake. Experiments show that the training efficiency of the CNN model can be significantly improved by the ELA method. In addition, the proposed technique can accurately extract the counterfeit feature, and therefore achieves outperformance in simplicity and efficiency compared with direct detection methods. Specifically, without loss of accuracy, the amount of computation can be significantly reduced (where the required floating-point computing power is reduced by more than 90%).

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A Novel DWT and Deep Learning Based Feature Extraction Technique for Plant Disease Identification

10.1007/978-981-16-3346-1_29 ◽

2021 ◽

pp. 355-367

Author(s):

Kirti ◽

Navin Rajpal ◽

Jyotsna Yadav

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Plant Disease ◽

Extraction Technique ◽

Disease Identification ◽

Feature Extraction Technique

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Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Scientific Programming ◽

10.1155/2020/5812715 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Atif Khan ◽

Muhammad Adnan Gul ◽

M. Irfan Uddin ◽

Syed Atif Ali Shah ◽

Shafiq Ahmad ◽

...

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Language Processing ◽

Big Data Analytics ◽

Extraction Technique ◽

Learning Approach ◽

Machine Learning Approach ◽

Challenging Tasks ◽

Bayes Algorithm ◽

Feature Extraction Technique

Information is exploding on the web at exponential pace, so online movie review is becoming a substantial information resource for online users. However, users post millions of movie reviews on regular basis, and it is not possible for users to summarize the reviews. Movie review classification and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is demanded to summarize the vast amount of movie reviews, and it will allow the users to speedily distinguish the positive and negative aspects of a movie. This study has proposed an approach for movie review classification and summarization. For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents, and represent the review documents as a vector space model. Next, the Naïve Bayes algorithm is employed to classify the movie reviews (represented as a feature vector) into positive and negative reviews. For the task of movie review summarization, Word2vec feature extraction technique is used to extract features from classified movie review sentences, and then semantic clustering technique is used to cluster semantically related review sentences. Different text features are used to calculate the salience score of each review sentence in clusters. Finally, the top-ranked sentences are chosen based on highest salience scores to produce the extractive summary of movie reviews. Experimental results reveal that the proposed machine learning approach is superior than other state-of-the-art approaches.

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