Biomedical image analysis methods are gradually shifting towards computer-aided solutions from manual investigations to save time and improve the quality of the diagnosis. Deep learning-assisted biomedical image analysis is one of the major and active research areas. Several researchers are working in this domain because deep learning-assisted computer-aided diagnostic solutions are well known for their efficiency. In this chapter, a comprehensive overview of the deep learning-assisted biomedical image analysis methods is presented. This chapter can be helpful for the researchers to understand the recent developments and drawbacks of the present systems. The discussion is made from the perspective of the computer vision, pattern recognition, and artificial intelligence. This chapter can help to get future research directions to exploit the blessings of deep learning techniques for biomedical image analysis.

An Overview of Biomedical Image Analysis From the Deep Learning Perspective

Sentiment classification is to find the polarity of product or user reviews. Supervised machine learning algorithms is used for opinion mining such as Naive Bayes, K-nearest neighbor, Decision Trees, Maximum Entropy and Hidden Markov Model and Support Vector Machine. KNN is a simple algorithm, but a less efficient classification algorithm. In this paper, we propose an improved KNN algorithm. An optimized feature selection, genetic algorithm that incorporates the information gain for feature reduction and combined with bagging technique. The new method improves the accuracy of sentiment classification. Specifically, we compared two approaches with PCA feature reduction technique and traditional KNN for Sentiment Classification of movie reviews. The same approach has been applied to other machine learning algorithms such as Support Vector Machine and Naive Bayes. The proposed method is evaluated and experimental results using information gain, genetic algorithm with bagging technique indicate higher performance result with accuracy of 87.50% of the movie reviews and exhibits better performance in terms of Accuracy, Precision and Recall for Movie, Book, DVD, Electronics and Kitchen reviews.

Feature Selection based on Genetic Algorithm and Hybrid Model for Opinion Mining

Recent developments in sarcasm detection have been emerged as extremely successful tools in Social media opinion mining. With the advent of machine learning tools, accurate detection has been made possible. However, the social media data used to train the machine learning models is often ill suited due to the presence of highly imbalanced classes. In absence of any thorough study on the effect of imbalanced classes in sarcasm detection for social media opinion mining, the current article proposed synthetic minority oversampling based methods to mitigate the issue of imbalanced classes which can severely effect the classifier performance in social media sarcasm detection. In the current study, five different variants of synthetic minority oversampling technique have been used on two different datasets of varying sizes. The trustworthiness is judged by training and testing of six well known classifiers and measuring their performance in terms of test phase confusion matrix based performance measuring metrics. The experimental results indicated that SMOTE and BorderlineSMOTE – 1 are extremely successful in improving the classifier performance. A thorough analysis has been performed to better understand the effect of imbalanced classes in social media sarcasm detection.

Synthetic minority oversampling in addressing imbalanced sarcasm detection in social media

A numerical and analytical study is made of the macroscopic or homogenized viscoelastic response of suspensions of rigid inclusions in rubber under finite quasistatic deformations. The focus is on the prototypical case of random isotropic suspensions of equiaxed inclusions firmly embedded in an isotropic incompressible Gaussian rubber with constant viscosity. From a numerical point of view, a robust scheme is introduced to solve the governing initial–boundary-value problem based on a conforming Crouzeix–Raviart finite-element discretization of space and a high-order accurate explicit Runge–Kutta discretization of time, which are particularly well suited to deal with the challenges posed by finite deformations and the incompressibility constraint of the rubber. The scheme is deployed to generate sample solutions for the basic case of suspensions of spherical inclusions of the same (monodisperse) size under a variety of loading conditions. From a complementary point of view, analytical solutions are worked out in the limits: ( i ) of small deformations, ( i i ) of finite deformations that are applied either infinitesimally slowly or infinitely fast, and ( i i i ) when the rubber loses its ability to store elastic energy and reduces to a Newtonian fluid. Strikingly, in spite of the fact that the underlying rubber matrix has constant viscosity, the solutions reveal that the viscoelastic response of the suspensions exhibits an effective nonlinear viscosity of shear-thinning type. The solutions further indicate that the viscoelastic response of the suspensions features the same type of short-range-memory behavior — as opposed to the generally expected long-range-memory behavior — as that of the underlying rubber. Guided by the asymptotic analytical results and the numerical solutions, a simple yet accurate approximate analytical solution for the macroscopic viscoelastic response of the suspensions is proposed.

The nonlinear viscoelastic response of suspensions of rigid inclusions in rubber: I—Gaussian rubber with constant viscosity

— In this study, a new method has been eliciting for encoding 2D and 3D color images. The DNA strand construction was used as the basis for structuring the method. This method consisted of two main stages, the encryption and decryption stages. As each stage includes several operations to reach the desired goal. In the coding stage, a special table was prepared to show the mechanism of work. It starts with encoding the DNA bases into two binary orders, then two zeros are added to the string to finally consist of four binary bits whose size is parallel to the representation of a set of hexadecimal numbers represented in binary, where the XOR operation is then done between the two values to be the result is completely different from the original code. Then the binary values we obtained are converted to decimal values that are placed in an array with the same size as the image to be encoded. Finally, this last array was processed with the exponential function factor, so the final result is a 100% encoded image. In the decoding stage, another algorithm was built that reflects the work of what preceded it in the encryption stage, where the result was an exact copy of the original image. It is worth noting that standard images of different sizes were used as testing images. The performance evaluation of the method was calculated based on several factors: MSE, peak PSNR, and the time required to perform the encoding and decoding process. The method achieved good results when compared with the results of other methods in terms of quality and time.

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Encryption of Color Image Based on DNA Strand and Exponential Factor

Micro-Blogging platforms have become one of the popular medium which reflects opinion/sentiment of social events and entities. Machine learning based sentiment analyses have been proven to be successful in finding people’s opinion using redundantly available data. However, current study has pointed out that the data being used to train such machine learning models could be highly imbalanced. In the current study live tweets from Twitter have been used to systematically study the effect of class imbalance problem in sentiment analysis. Minority oversampling method is employed here to manage the imbalanced class problem. Two well-known classifiers Support Vector Machine and Multinomial Naive Bayes have been used for classifying tweets into positive or negative sentiment classes. Results have revealed that minority oversampling based methods can overcome the imbalanced class problem to a greater extent.

Imbalanced Twitter Sentiment Analysis using Minority Oversampling

Security of the digital data is one of the major concerns of the today’s world. There are several methods for digital data security that can be found in the literature. Biological sequences have some features that make it worthy for the digital data security processes. In this work, DNA encryption and its different approaches are discussed to give a brief overview on the data security methods based on DNA encryption. This work can be highly beneficial for future research on DNA encryption and can be applied on different domains.

Data Security Techniques Based on DNA Encryption

Biomedical image analysis is an integral part of the modern healthcare industry and has a huge impact on the modern world. Automated computer-aided systems are highly beneficial for fast, accurate and efficient diagnosis of the biomedical images. Remote healthcare systems allow doctors and patients to perform their jobs from separate geographic locations. Moreover, expert opinion about a patient can be obtained from a doctor who is in a different country or in some distant location within stipulated amount of time. Remote healthcare systems require digital biomedical images to be transferred over the network. But several security threats are associated with the transmission of the biomedical images. Privacy of the patients must be preserved by keeping the images safe from any unauthorized access. Moreover, the contents of the biomedical images must be preserved efficiently so that no one can tamper it. Data tampering can produce drastic results in many cases. In this work, a method for biomedical image security has been proposed. DNA encryption method is one of the emerging methods in the field of cryptography. A secure and lossless encryption method is developed in this work. Various numerical parameters are used to evaluate the performance of the proposed method which proves the effectiveness of the algorithm.

Kushankur Ghosh

Papers

Imbalanced Twitter Sentiment Analysis using Minority Oversampling

Synthetic minority oversampling in addressing imbalanced sarcasm detection in social media

Data Security Techniques Based on DNA Encryption

Biomedical Image Security Using Matrix Manipulation and DNA Encryption

The effective shear modulus of a random isotropic suspension of monodisperse liquid n-spheres: from the dilute limit to the percolation threshold.