itcsbanner.jpg

Filter by

Robust Background Template for Saliency Detection

In this paper, we propose an effective saliency detection method based on dense and sparse representation in-terms of an optimized background template. Firstly, the input image is divided into compact and uniform super-pixels. Then, the optimized background template is produced by introducing boundary conductivity measurement to improve the dense and sparse representation of the image's super

Artificial Intelligence

An E-health System for Encrypting Biosignals Using Triple-DES and Hash Function

This Electronic Health (E-Health) is a broad expression that enables the communication between healthcare professionals in handling patient information through the cloud. Exchanging medical data over the public cloud requires securing transferring for the data that's direct many researchers in proposing different secure schemes to enable users to handle data safely without hacking or alternating

Artificial Intelligence

An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data

Feature selection is critical in analyzing microarray data, which has many features (genes) or dimensions. However, with only a few samples the large search space and time consumed during their selection make selecting relevant and informative genes that improve classification performance a complex task. This paper proposed a hybrid model for gene selection known as (SVM-mRMRe), the proposed model

Artificial Intelligence

An Efficient Cancer Classification Model Using Microarray and High-Dimensional Data

Cancer can be considered as one of the leading causes of death widely. One of the most effective tools to be able to handle cancer diagnosis, prognosis, and treatment is by using expression profiling technique which is based on microarray gene. For each data point (sample), gene data expression usually receives tens of thousands of genes. As a result, this data is large-scale, high-dimensional

Artificial Intelligence

Multi projection fusion for real-time semantic segmentation of 3D LiDAR point clouds

Semantic segmentation of 3D point cloud data is essential for enhanced high-level perception in autonomous platforms. Furthermore, given the increasing deployment of LiDAR sensors onboard of cars and drones, a special emphasis is also placed on non-computationally intensive algorithms that operate on mobile GPUs. Previous efficient state-of-the-art methods relied on 2D spherical projection of

Artificial Intelligence
Software and Communications

Sentiment Analysis using Machine Learning and Deep Learning Models on Movies Reviews

The huge amount of data being generated and transferred each day on the Internet leads to an increase of the need to automate knowledge-extraction tasks. Sentiment analysis is an ongoing research field in knowledge extraction that faces many challenges. In this paper, different machine learning, neural networks, deep learning models were evaluated over the IMDB benchmark dataset for movies reviews

Artificial Intelligence

Corneal Biomechanics Assessment Using High Frequency Ultrasound B-Mode Imaging

Assessment of corneal biomechanics for pre- and post-refractive surgery is of great clinical importance. Corneal biomechanics affect vision quality of human eye. Many factors affect corneal biomechanics such as, age, corneal diseases and corneal refractive surgery. There is a need for non-invasive in-vivo measurement of corneal biomechanics due to corneal shape preserving as opposed to ex-vivo

Artificial Intelligence

Modeling Collaborative AI for Dynamic Systems of Blockchain-ed Autonomous Agents

Artificial Intelligence has been strongly evolving disrupting almost every research and application domain. One of the key attributes - and at the same time an enabler - of today's innovations is the massive connectivity resulted in the opportunity to exploit Artificial Intelligence across distributed network of self-contained smart agents those could range from software bots to complex devices

Artificial Intelligence

Hybrid feature selection model based on relief-based algorithms and regulizer algorithms for cancer classification

Cancer is a group of diseases that involve abnormal cell growth with the potential to spread to other parts of the body. Cancer microarray data usually include a small number of samples with a large number of gene expression levels as features. Gene expression or microarray is a technology that monitors the expression of the large number of genes in parallel that make it useful in cancer

Artificial Intelligence

A Hybrid Feature Selection Optimization Model for High Dimension Data Classification

Feature selection is an NP-hard combinatorial problem, in which the number of possible feature subsets increases exponentially with the number of features. In the case of large dimensionality, the goal of feature selection is to determine the smallest possible features considering the most informative subset. In this paper, we proposed a hybrid feature selection optimization model for Cancer

Artificial Intelligence