A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. As a result, many innovative approaches have been proposed and validated on multiple benchmark datasets. This study offers an in-depth assessment of the latest developments in deep learning-based 3D object recognition. We discuss the most well-known 3D object recognition models, along with evaluations of their distinctive qualities.

Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-Based Recommendation in E-Commerce

Boosting the sales of e-commerce services is guaranteed once users find more items matching their interests in a short amount of time. Consequently, recommendation systems have become a crucial part of any successful e-commerce service. Although various recommendation techniques could be used in e-commerce, a considerable amount of attention has been drawn to session-based recommendation systems in recent years. This growing interest is due to security concerns over collecting personalized user behavior data, especially due to recent general data protection regulations. In this work, we present a comprehensive evaluation of the state-of-the-art deep learning approaches used in the session-based recommendation. In session-based recommendation, a recommendation system counts on the sequence of events made by a user within the same session to predict and endorse other items that are more likely to correlate with their preferences. Our extensive experiments investigate baseline techniques (e.g., nearest neighbors and pattern mining algorithms) and deep learning approaches (e.g., recurrent neural networks, graph neural networks, and attention-based networks). Our evaluations show that advanced neural-based models and session-based nearest neighbor algorithms outperform the baseline techniques in most scenarios. However, we found that these models suffer more in the case of long sessions when there exists drift in user interests, and when there are not enough data to correctly model different items during training. Our study suggests that using the hybrid models of different approaches combined with baseline algorithms could lead to substantial results in session-based recommendations based on dataset characteristics. We also discuss the drawbacks of current session-based recommendation algorithms and further open research directions in this field.

Ensemble Approach for Detection of Depression Using EEG Features

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.

Prediction of sgRNA Off-Target Activity in CRISPR/Cas9 Gene Editing Using Graph Convolution Network

CRISPR/Cas9 is a powerful genome-editing technology that has been widely applied in targeted gene repair and gene expression regulation. One of the main challenges for the CRISPR/Cas9 system is the occurrence of unexpected cleavage at some sites (off-targets) and predicting them is necessary due to its relevance in gene editing research. Very few deep learning models have been developed so far to predict the off-target propensity of single guide RNA (sgRNA) at specific DNA fragments by using artificial feature extract operations and machine learning techniques; however, this is a convoluted process that is difficult to understand and implement for researchers. In this research work, we introduce a novel graph-based approach to predict off-target efficacy of sgRNA in the CRISPR/Cas9 system that is easy to understand and replicate for researchers. This is achieved by creating a graph with sequences as nodes and by using a link prediction method to predict the presence of links between sgRNA and off-target inducing target DNA sequences. Features for the sequences are extracted from within the sequences. We used HEK293 and K562 t datasets in our experiments. GCN predicted the off-target gene knockouts (using link prediction) by predicting the links between sgRNA and off-target sequences with an auROC value of 0.987.