The introduction of Industry 4.0, to improve Internet of Things (IoT) standards, has sparked the creation of 5G, or highly sophisticated wireless networks. There are several barriers standing in the way of 5G green communication systems satisfying the expectations for faster networks, more user capacity, lower resource consumption, and cost‐effectiveness. 5G standards implementation would speed up data transmission and increase the reliability of connected devices for Industry 4.0 applications. The demand for intelligent healthcare systems has increased globally as a result of the introduction of the novel COVID‐19. Designing 5G communication systems presents research problems such as optimizing resource usage, managing mobility, ensuring cost‐efficiency, managing interference, and maximizing spectral efficiency. The fast advancement of artificial intelligence (AI) in several domains yields improved performance in contrast to traditional methods. Hence, including AI in 5G standards would enhance performance by catering to diverse end‐user applications. Initially, we provide an overview of concepts such as Industry 4.0, the 5G standard, and recent developments in the sphere of wireless communications in the future. The goal is to use 5G technology to look at current research problems. We present a new architecture for Industry 4.0 and 5G‐compliant smart healthcare systems. We develop and run the proposed model to investigate the current 5G methods using the Network Simulator (NS2). The results of the simulation show that 5G resource management and interference management approaches already in use face challenges including performance trade‐offs.
Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model is a novel convolutional neural network architecture that combines the capabilities of deep learning for feature extraction with the effectiveness o
... Show MoreA Multiple System Biometric System Based on ECG Data
There has been a growing interest in the use of chaotic techniques for enabling secure communication in recent years. This need has been motivated by the emergence of a number of wireless services which require the channel to provide low bit error rates (BER) along with information security. The aim of such activity is to steal or distort the information being conveyed. Optical Wireless Systems (basically Free Space Optic Systems, FSO) are no exception to this trend. Thus, there is an urgent necessity to design techniques that can secure privileged information against unauthorized eavesdroppers while simultaneously protecting information against channel-induced perturbations and errors. Conventional cryptographic techniques are not designed
... Show MoreOne of the most popular and legally recognized behavioral biometrics is the individual's signature, which is used for verification and identification in many different industries, including business, law, and finance. The purpose of the signature verification method is to distinguish genuine from forged signatures, a task complicated by cultural and personal variances. Analysis, comparison, and evaluation of handwriting features are performed in forensic handwriting analysis to establish whether or not the writing was produced by a known writer. In contrast to other languages, Arabic makes use of diacritics, ligatures, and overlaps that are unique to it. Due to the absence of dynamic information in the writing of Arabic signatures,
... Show MoreAZ Khalaf, M kassim Haidir, LK Jasim, Iraqi Journal of Science, 2012
<span lang="EN-US">The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in
... Show MoreI n this paper ,we 'viii consider the density questions associC;lted with the single hidden layer feed forward model. We proved that a FFNN with one hidden layer can uniformly approximate any continuous function in C(k)(where k is a compact set in R11 ) to any required accuracy.
However, if the set of basis function is dense then the ANN's can has al most one hidden layer. But if the set of basis function non-dense, then we need more hidden layers. Also, we have shown that there exist localized functions and that there is no t
... Show MoreThis paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback
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