Different convolutional neural network (CNN) models have been tested for their application in histologic imaging analyses. However, these models are prone to overfitting due to their large parameter capacity, requiring more data and expensive computational resources for model training. Given these limitations, we developed and tested PlexusNet for histologic evaluation using a single GPU by a batch dimension of 16x512x512x3. We utilized 62 Hematoxylin and eosin stain (H&E) annotated histological images of radical prostatectomy cases from TCGA-PRAD and Stanford University, and 24 H&E whole-slide images with hepatocellular carcinoma from TCGA-LIHC diagnostic histology images. Base models were DenseNet, Inception V3, and MobileNet and compared with PlexusNet. The dice coefficient (DSC) was evaluated for each model. PlexusNet delivered comparable classification performance (DSC at patch level: 0.89) for H&E whole-slice images in distinguishing prostate cancer from normal tissues. The parameter capacity of PlexusNet is 9 times smaller than MobileNet or 58 times smaller than Inception V3, respectively. Similar findings were observed in distinguishing hepatocellular carcinoma from non-cancerous liver histologies (DSC at patch level: 0.85). As conclusion, PlexusNet represents a novel model architecture for histological image analysis that achieves classification performance comparable to the base models while providing orders-of-magnitude memory savings.

The concept of non-linearity in a Neural Network is introduced by an activation function which serves an integral role in the training and performance evaluation of the network. Over the years of theoretical research, many activation functions have been proposed, however, only a few are widely used in mostly all applications which include ReLU (Rectified Linear Unit), TanH (Tan Hyperbolic), Sigmoid, Leaky ReLU and Swish. In this work, a novel neural activation function called as Mish is proposed. The experiments show that Mish tends to work better than both ReLU and Swish along with other standard activation functions in many deep networks across challenging datasets. For instance, in Squeeze Excite Net- 18 for CIFAR 100 classification, the network with Mish had an increase in Top-1 test accuracy by 0.494% and 1.671% as compared to the same network with Swish and ReLU respectively. The similarity to Swish along with providing a boost in performance and its simplicity in implementation makes it easier for researchers and developers to use Mish in their Neural Network Models.

The application of deep learning to single-photon emission computed tomography (SPECT) imaging in Parkinson's disease shows effectively high diagnosis accuracy. However, difficulties in model interpretation were occurred due to the complexity of the deep learning model. Although several interpretation methods were created to show the attention map that contains important features of the input data, it is still uncertain whether these methods can be applied in PD diagnosis. Four different models of the deep learning approach based on 3-dimensional convolution neural network (3D-CNN) of well-established architectures have been trained with an accuracy up to 95-96% in classification performance. These four models have been used as the comparative study for well-known interpretation methods. Generally, radiologists interpret SPECT images by confirming the shape of the I123-Ioflupane uptake in the striatal nuclei. To evaluate the interpretation performance, the segmented striatal nuclei of SPECT images are chosen as the ground truth. Results suggest that guided backpropagation and SHAP which were developed recently, provided the best interpretation performance. Guided backpropagation has the best performance to generate the attention map that focuses on the location of striatal nuclei. On the other hand, SHAP surpasses other methods in suggesting the change of the striatal nucleus uptake shape from healthy to PD subjects. Results from both methods confirm that 3D-CNN focuses on the striatal nuclei in the same way as the radiologist, and both methods should be suggested to increase the credibility of the model.

Nevertheless, fairness in machine learning remains a problem. Machine learning algorithms have the risk of amplifying societal stereotypes by over associating protected attributes, e.g., race and gender, with the main prediction task [33]. Eventually they are capable of exhibiting discriminatory behaviors against certain subgroups. For example, a recruiting tool believes that men are more qualified and shows bias against women [26], facial recognition performs extremely poorly for darker skin females [5], recognition accuracy is very low for subgroup of people in pedestrian detection of self-driving cars [33]. The fairness problem might cause adverse impacts on individuals and society. It not only limits a person's opportunities which he/she is qualified, but also might further exacerbate social inequity. Among different machine learning models, the fairness problem of deep learning models has especially attracted attention from academia and industry recently. First, deep learning models have outperformed conventional machinePermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

Music genre classification is a widely researched topic in music information retrieval (MIR). Being able to automatically tag genres will benefit music streaming service providers such as JOOX, Apple Music, and Spotify for their content-based recommendation. However, most studies on music classification have been done on western songs which differ from Thai songs. Lukthung, a distinctive and long-established type of Thai music, is one of the most popular music genres in Thailand and has a specific group of listeners. In this paper, we develop neural networks to classify such Lukthung genre from others using both lyrics and audios. Words used in Lukthung songs are particularly poetical, and their musical styles are uniquely composed of traditional Thai instruments. We leverage these two main characteristics by building a lyrics model based on bag-of-words (BoW), and an audio model using a convolutional neural network (CNN) architecture. We then aggregate the intermediate features learned from both models to build a final classifier. Our results show that the proposed three models outperform all of the standard classifiers where the combined model yields the best $F_1$ score of 0.86, allowing Lukthung classification to be applicable to personalized recommendation for Thai audience.

In intelligent education systems, one key issue is to discover students' proficiency level on specific knowledge concepts, which called cognitive diagnosis. Existing approaches usually mine the student exercising process by manually designed function, which is usually linear and not sufficient to capture complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex interactions between student's and exercise's factor vectors. The interpretability of factor vectors is guaranteed with the monotonicity assumption borrowed from educational psychology. We provide NeuralCDM model as an implementation example of the framework. Further, we explore the text content for improving NeuralCDM to show the extendability of NeuralCD, and demonstrate the generality of NeuralCD by proving how it covers some traditional diagnostic models. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability.

AI has become the key driver for the adoption of edge computing. Originally, the edge computing layer was meant to deliver local compute, storage, and processing capabilities to IoT deployments. Sensitive data that cannot be sent to the cloud for processing and analysis is handled by the edge. It also reduces the latency involved in the roundtrip to the cloud. Most of the business logic that runs in the cloud is moving to the edge to provide low-latency, faster response time.

In this part, we're going to create a simple example of how we can use the neuron.js Throughout this series, we will try to describe how to use this code to build neural networks, AI bots, and potentially our own deep learning framework. There are many great resources on the fundamentals of neural networks, how they work, why they work, but there are few articles on how to actually build neural networks from a software development perspective. With this very "technical" series, I hope to address this; for a quick introduction, checkout this simple introduction to neural networks, Eric Elliot's article on neurons, or 3Blue1Brown's video series on neural networks. In this example we will be teaching a neural network created with the neuron.js

Today microcontrollers can be found in almost any technical device, from washing machines to blood pressure meters and wearables. Researchers at the Fraunhofer Institute for Microelectronic Circuits and Systems IMS have developed AIfES, an artificial intelligence (AI) concept for microcontrollers and sensors that contains a completely configurable artificial neural network. AIfES is a platform-independent machine learning library which can be used to realize self-learning microelectronics requiring no connection to a cloud or to high-performance computers. The sensor-related AI system recognizes handwriting and gestures, enabling for example gesture control of input when the library is running on a wearable. A wide variety of software solutions currently exist for machine learning, but as a rule they are only available for the PC and are based on the programming language Python.

The basis of applying deep learning to solve natural language processing tasks is to obtain high-quality distributed representations of words, i.e., word embeddings, from large amounts of text data. However, text itself usually contains incomplete and ambiguous information, which makes necessity to leverage extra knowledge to understand it.