The real-time quantification of the effect of a wireless channel on the transmitting signal is crucial for the analysis and the intelligent design of wireless communication systems for various services. Recent mechanisms to model channel characteristics independent of coding, modulation, signal processing, etc., using deep learning neural networks are promising solutions. However, the current approaches are neither statistically accurate nor able to adapt to the changing environment. In this paper, we propose a new approach that combines a deep learning neural network with a mixture density network model to derive the conditional probability density function (PDF) of receiving power given a communication distance in general wireless communication systems. Furthermore, a deep transfer learning scheme is designed and implemented to allow the channel model to dynamically adapt to changes in communication environments. Extensive experiments on Nakagami fading channel model and Log-normal shadowing channel model with path loss and noise show that the new approach is more statistically accurate, faster, and more robust than the previous deep learning-based channel models.

Deep learning is a subset of machine learning that is revolutionizing the way we process and analyze data. It is a type of artificial neural network (ANN) that is designed to process large amounts of complex data and learn from that data to make predictions or decisions. Deep learning is based on the idea that computers can learn to perform tasks without being explicitly programmed to do so. Deep learning is a type of artificial intelligence that uses artificial neural networks to learn and make predictions or decisions based on the data it is trained on. A neural network is a mathematical model that is designed to simulate the structure and function of the human brain.

Software security is a critical issue that affects every aspect of our lives. From online banking to social media, we rely on software to protect our sensitive information and systems from unauthorized access and attacks. However, as technology advances, so do the methods of cybercriminals. They are constantly coming up with new and sophisticated ways to bypass traditional security measures. This is where AI and ML come in. These technologies can be used to enhance software security by providing more advanced and adaptive methods for intrusion detection and vulnerability management.

A team of researchers from the University of California, San Diego, and the University of Illinois has found that it is also possible to hide malware in deep learning neural networks and deliver it to an unsuspecting target, without it being detected by conventional anti-malware software. Not surprisingly, this new work is highlighting the need for better cybersecurity measures to counteract and protect users from the very real possibility of AI-assisted attacks, especially as individuals and businesses become increasingly reliant on AI in their daily activities. In a pre-print paper outlining EvilModel -- the team's ominously named method for embedding malware in deep learning neural networks -- the team discovered that it was possible to infect a deep learning model with malware, and have it fool anti-malware detectors, all without significantly affecting the model's performance. To achieve this, the team used an approach known as steganography, where pieces of data in a system are swapped out for other bits of data that might have a hidden message or function. To hide their sample piece of malware, the team first started by deconstructing the malware into smaller pieces so that each piece measured only 3 bytes -- an insignificant enough size to evade detection.

The algorithm, created by researchers at the University of Pittsburgh School of Medicine, was trained to predict outcomes for patients with traumatic brain injury (TBI). The algorithm was trained on a wide range of data including computed tomography (CT) scans, vital signs, blood tests, heart function, and coma severity estimates for the patient. The large and varied dataset lends itself well to a deep learning algorithm. The model was a fusion model that combined clinical input with a dataset of head CT scans predicting mortality and unfavorable outcomes. The researchers used transfer learning and curriculum learning applied to a convolutional neural network (CNN) in order to specialize the network to the CT scans.

The Bot Libre platform is not just a bot platform, but also a platform for artificial intelligence and deep learning. With Bot Libre you can create your own deep learning neural network for text classification, text generation, image recognition, audio and speech recognition, object detection, games, prediction, data analysis, and more. You may think that creating a "deep learning neural networks" sounds like a very complex thing to do, but with Bot Libre it is very simple, and requires no programming or data science experience. This article we walk you through the steps to create your own text generation network. First you need to decide what types of text you want your network to generate.

The neural networks in deep learning are generally considered to be similar to the neural networks we have in our brains, albeit a much simpler versions. This similarity helps comprehend the deep learning neural networks easily. I have always seen the similarity to be used only to comprehend the deep learning neural networks and not to understand our brain from what we learn about the deep learning neural networks. The points below do not have a logical deduction but mostly my observation and intuition. The lessons I learned about life from neural networks are not new.

The objective of this research was to evaluate the deep learning neural network in artificial intelligence (AI) technologies to rapidly classify seven different beef cuts (bone in rib eye steak, boneless rib eye steak, chuck steak, flank steak, New York strip, short rib, and tenderloin). Color images of beef samples were acquired from a laboratory-based computer vision system and collected from the Internet (Google Images) platforms. A total of 1,113 beef cut images were used as training, validation, and testing data subsets for this project. The model developed from the deep learning neural network algorithm was able to classify certain beef cuts (flank steak and tenderloin) up to 100% accuracy. Two pretrained convolution neutral network (CNN) models Visual Geometry Group (VGG16) and Inception ResNet V2 were used to train, validate, and test these models in classifying beef cut images. An image augmentation technique was incorporated in the convolution neutral network models for avoiding the overfitting problems, which demonstrated an improvement in the performance of the image classifier model. The VGG16 model outperformed the Inception ResNet V2 model. The VGG16 model coupled with data augmentation technique was able to achieve the highest accuracy of 98.6% on 116 test images, whereas Inception ResNet V2 accomplished a maximum accuracy of 95.7% on the same test images. Based on the performance metrics of both models, deep learning technology evidently showed a promising...

Population-based optimization algorithms, like evolutionary algorithms and swarm intelligence, often describe their dynamics in terms of the interplay between selective pressures and convergence. For example, strong selective pressures result in faster convergence and likely premature convergence. Weaker selective pressures may result in a slower convergence (greater computational cost) although perhaps locate a better or even global optima. An operator with a high selective pressure decreases diversity in the population more rapidly than operators with a low selective pressure, which may lead to premature convergence to suboptimal solutions. A high selective pressure limits the exploration abilities of the population.

The enormous and raging wave of change that has hit our world in the last decade, has got some of us thinking and others reveling in their glory. The internet and evolving technological practices have increased possibilities. Man and machine collaboration has got us introduced to automated virtual work and communication systems everywhere in the world. Deep Learning has given birth to several real-life applications that have lessened human control and involvement in several spheres of life. The immense popularity of the Deep Learning UseCases blog was enough encouragement to look at more such UseCases.