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Enriched environmental conditions modify the gut microbiome composition and fecal markers of inflammation in parkinson’s disease

Recent findings suggest an implication of the gut microbiome in Parkinson’s disease (PD) patients. PD onset and progression has also been linked with various environmental factors such as physical activity, exposure to pesticides, head injury, nicotine, and dietary factors. In this study, we used a mouse model, overexpressing the complete human SNCA gene (SNCA-TG mice) modeling familial and

Artificial Intelligence

Predicting all star player in the national basketball association using random forest

National Basketball Association (NBA) All Star Game is a demonstration game played between the selected Western and Eastern conference players. The selection of players for the NBA All Star game purely depends on votes. The fans and coaches vote for the players and decide who is going to make the All Star roster. A player who continues to receive enough votes in following years will play more All

Artificial Intelligence

Combining lexical features and a supervised learning approach for arabic sentiment analysis

The importance of building sentiment analysis tools for Arabic social media has been recognized during the past couple of years, especially with the rapid increase in the number of Arabic social media users. One of the main difficulties in tackling this problem is that text within social media is mostly colloquial, with many dialects being used within social media platforms. In this paper, we

Artificial Intelligence
Software and Communications

A deep CNN-based framework for enhanced aerial imagery registration with applications to UAV geolocalization

In this paper we present a novel framework for geolocalizing Unmanned Aerial Vehicles (UAVs) using only their onboard camera. The framework exploits the abundance of satellite imagery, along with established computer vision and deep learning methods, to locate the UAV in a satellite imagery map. It utilizes the contextual information extracted from the scene to attain increased geolocalization

Artificial Intelligence
Software and Communications

Malicious VBScript detection algorithm based on data-mining techniques

Malware attacks are amongst the most common security threats. Not only malware incidents are rapidly increasing, but also the attack methodologies are getting more complicated. Moreover malware writers expand in using different platforms and languages. This raises the need for new detection methods which support more reliable, low resource consuming and fast solutions. In this paper, a new

Artificial Intelligence

Biochar affects community composition of nitrous oxide reducers in a field experiment

N2O is a major greenhouse gas and the majority of anthropogenic N2O emissions originate from agriculturally managed soils. Therefore, developing N2O mitigation strategies is a key challenge for the agricultural sector and biochar soil treatment is one reported option. Biochar's capacity to increase soil pH and to foster activity of specialized N2O reducers has been proposed as possible mechanisms

Healthcare

A Context Integrated Model for Multi-label Emotion Detection

This paper explores the impact of taking the environment within which a tweet is made, on the task of analyzing sentiment orientations of tweets produced by people in the same community. The paper proposes C-GRU (Context-aware Gated Recurrent Units), which extracts the contextual information (topics) from tweets and uses them as an extra layer to determine sentiments conveyed by the tweet. The

Artificial Intelligence

NU-Net: Deep residual wide field of view convolutional neural network for semantic segmentation

Semantic Segmentation of satellite images is one of the most challenging problems in computer vision as it requires a model capable of capturing both local and global information at each pixel. Current state of the art methods are based on Fully Convolutional Neural Networks (FCNN) with mostly two main components: an encoder which is a pretrained classification model that gradually reduces the

Artificial Intelligence

Correlation between protocol selection and packet drop attack severity in ad hoc networks

Mobile Ad hoc Network (MANET) are self-configuring, dynamic, networks that consist of nodes with various capabilities communicating through a wide spectrum of frequencies. Such flexibility in infrastructure and design comes with great risks in form of attacks on its nodes and the routing protocols that connect the network together. The aim of this paper is to explore the correlation between the

Artificial Intelligence

Decoding arm kinematics from EMG signals using Kalman filter

Myoelectric control of prosthetic arms provides a new hope for providing naturalistic movements to amputees. Extensive work has been made in recent years to use Electromyography (EMG) signals to enhance the operation of prosthetic arms. In this paper, we propose an EMG Kalman filter-based model, where we identify the relationship between the joint angles and recorded EMG signals. EMG signals were

Artificial Intelligence