Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet.

Common structures identified in the assembly of aromatic amino acids and their mixtures include the four-fold tube (a and b) and the zig-zag structure (c and d).

Sentiment analysis using stemmed Twitter data from various languages is an emerging research topic. In this paper, we address three data augmentation techniques namely Shift, Shuffle, and Hybrid to increase the size of the training data; and then we use three key types of deep learning (DL) models namely recurrent neural network (RNN), convolution neural network (CNN), and hierarchical attention network (HAN) to classify the stemmed Turkish Twitter data for sentiment analysis. The performance of these DL models has been compared with the existing traditional machine learning (TML) models. The performance of TML models has been affected negatively by the stemmed data, but the performance of DL models has been improved greatly with the utilization of the augmentation techniques. Based on the simulation, experimental, and statistical results analysis deeming identical datasets, it has been concluded that the TML models outperform the DL models with respect to both training-time (TTM) and runtime (RTM) complexities of the algorithms; but the DL models outperform the TML models with respect to the most important performance factors as well as the average performance rankings.

Phenylalanine, tyrosine, and tryptophan are aromatic amino acids, and they are of high interest in both health science and biotechnology. These amino acids form organized structures, like fibrils and nanotubes. Although these amino acids belong to the same family, they still differ from each other with respect to polarity, hydrophobicity as well as internal structures. In this work, we performed extensive molecular dynamics simulations to investigate the dynamics of the self-aggregations of these amino acids and studied the details of the formed structures. The amino acid monomers placed in water were simulated at a constant temperature. It has been observed that they compose nanostructures with similarities and differences.

Molecular self-assembly is a ubiquitous phenomenon in which individual atoms or molecules set up an ordered structure. It is of high interest for understanding the biology and a variety of diseases at the molecular level. In this work, we studied the self-assembly of tyrosine molecules via extensive molecular dynamics simulations. The formation of structures by self-assembly was systematically studied at various concentrations, from very low to very high. The temperature was kept constant, at which, in our former studies, we have already observed well-formed self-assembled structures. Depending on the concentration, the system displays a wide range of different structures, ranging from freely scattered monomers to very well formed four-fold structures. Different regimes of concentration dependence are observed. The results are proved by calculating the moments of inertia of the structures and the number of hydrogen bonds formed. Free energy landscapes calculated for the number of hydrogen bonds versus the number of contacts within a criterion provide insights into the structures observed.

Laser scanner has several bons when compared with video camera. It does not record real world videos except scanned points. As a result, processing of data becomes faster and easier. Over and above, it takes away the problem of private life conservation. This paper proposes a new and competent computer vision based approach for detecting and tracking targets (e.g., pedestrians and vehicles) from laser scanned datasets. Laser scanned data points from each scan have been deemed as a video frame. Blobs are extracted and then computer vision techniques (e.g., Kalman filter, Hungarian algorithms, and etc.) are applied to recognize and track the kind of targets. Scanned datasets, collected from two kinds of laser scanners, were used to conduct experiments. Full trajectories of pedestrians, vehicles, and noises were resulted in three dimensional spaces. Experimental results give evidence of the efficacy of our proposed framework.