An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

A morphable model for the synthesis of 3D faces

EEG-based mild depressive detection using feature selection methods and classifiers

Investigation of different speech types and emotions for detecting depression using different classifiers

With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

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Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Skin Cancer is the most common (accounting for 40% of cancer cases globally) and potentially life-threatening type of cancers. It was diagnosed in about 5.6 million individuals last year. Automated classification of skin lesions through images has been a challenge throughout the years because of fine variability in their appearance. Deep Learning techniques exhibit potential in tackling fine-margined image-based analysis and manage to provide accurate results. The three modelling stages include data collection and augmentation, model architecture and finally prediction into 7 different types of skin cancer namely actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi and vascular lesions. A Convolutional Neural Network was fabricated (using TensorFlow) obtaining an accuracy of 81.24%. Further Transfer learning Approach was implemented in PyTorch, which yielded accuracies of 96.40%, 98.20%, 98.70% and 99.04% respectively for Wide Resnet101, Resnet50, Densenet121 and VGG19 with batch normalization, which are all trained end-to-end from images directly, to proliferate the scalability of these models and curtail initial diagnostic costs. The aim of this research paper is to render non-invasive skin cancer screening a common norm, making it simpler.

Skin Lesion Classification using Deep Learning and Image Processing

Mood disorders strikes millions each year, often with debilitating consequences. This psychological disorder is so common that it is sometimes referred to as the "common cold" of mental health, with nearly 10% of the population suffering from a depressive disorder at any given time. A major mood disorder significantly affects a person's family and personal relationships, work or school life, sleeping and eating habits, and general health. The use of Electroencephalogram (EEG) signals in the field of Brain Computer Interface (BCI) has obtained a lot of interest with diverse applications ranging from medicine to entertainment. In this paper, we are going to study nonlinear analysis of EEG signal for discriminating mood disordered patients and normal controls. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimensions and lyapunov exponent is considered. For classification of the two groups, k-nearest neighbor (KNN), linear discriminate analysis (LDA) and logistic regression (LR) as the classifiers are used. Machine learning techniques such as detrended fluctuation analysis (DFA), higuchi fractal, correlation dimensions and lyapunov exponent are mainly considered to distinguish between the normal subject and the normal one. Machine learning techniques are easy to implement and it gives best accuracy. It will be a complementary tool for psychiatrists to operate on the mood disordered patients.

Classifying Mood Disordered Patients and Normal Subjects Using Various Machine Learning Techniques

A self-organizing map (SOM) is a classical neural network method for dimensionality reduction. It comes under the unsupervised class. SOM is a neural network that is trained using unsupervised learning to produce a low-dimensional, discretized representation of the input space of the training samples, called a map. SOM uses a neighborhood function to preserve the topological properties of the input space. SOM operates in two modes: training and mapping. Using the input examples, training builds the map. It is also called as vector quantization. In this paper, we first survey related dimension reduction methods and then examine their capabilities for face recognition. In this work, different dimensionality reduction techniques such as Principal component analysis [PCA], independent component analysis [ICA] and self-organizing map [SOM] are selected and applied in order to reduce the loss of classification performance due to changes in facial expression. The experiments were conducted on ORL face database and the results show that SOM is a better technique.

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Dimensionality Reduction technique using Neural Networks – A Survey

Clinical depression has become a major factor causing suicides and is associated with high mortality rates making it the leading causes of death worldwide. Thus, early detection of depression is of primary importance as there has been a dramatic increase in the depressive symptoms and disorders in recent years. From a psychological point of view, one of the signs of a person being depressed is the way emotions are expressed in his/her speech. Depressive disorder affects the acoustic qualities of their speech; hence depression can be detected through an analysis of acoustical properties of speech. Features are extracted from the preprocessed speech signal and accordingly using non-linear classifiers speech is classified as depressed or controlled.

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A Survey: Pre-processing and Feature Extraction Techniques for Depression Analysis Using Speech Signal

We live in an era of information explosion where media for information communication and information sharing have proliferated due to the onset of a digital age. One such important source and medium of consolidated, comprehensive and reliable information, which has persisted through centuries, is news articles. News articles have been considered as a primary source of information, ranging from local to global scope, and encompassing a wide array of domains, by most of the human population. However, as the technology for dissemination of information attracts innovations, it becomes equally important that the consumption and utilization of this information becomes smart as well. If processed properly and treated in an intelligent manner, the information contained in news articles can reveal useful insights and hidden patterns that might prove to be of critical importance in certain decision making pertaining to the topics and domains in question. The paper puts forward a novel framework created by combining Natural Language Processing methodologies like Sentiment Analysis and Information Retrieval, which are performed on top of a Deep Learning based classification with aptly defined classes for generating useful insights from a news article dataset. The paper contains the details regarding the implementation of this system and also a presentation of results on a particular data set of news articles - articles on HIV-AIDS, which have been categorized into meaningful classes and have been processed using aforementioned NLP tasks. Through this paper, a curated dataset of 3,000 news articles related to HIV-AIDS has also been introduced, which will be open sourced so as to inspire further research and experimentation in this specific domain.

Shamla Mantri

Papers

Skin Lesion Classification using Deep Learning and Image Processing

Classifying Mood Disordered Patients and Normal Subjects Using Various Machine Learning Techniques

Dimensionality Reduction technique using Neural Networks – A Survey

A Survey: Pre-processing and Feature Extraction Techniques for Depression Analysis Using Speech Signal

DeepDiseaseInsight: A Deep Learning & NLP based Novel Framework for generating useful Insights from Disease News Articles