Design space exploration of Convolutional Neural Networks based on Evolutionary Algorithms

This paper proposes a framework for design space exploration ofConvolutional Neural Networks (CNNs) using Genetic Algorithms(GAs). CNNs have many hyperparameters that need to be tunedcarefully in order to achieve favorable results when used for imageclassification tasks or similar vision applications. Genetic Algorithmsare adopted to efficiently traverse the huge search spaceof CNNs hyperparameters, and generate the best architecture thatfits the given task. Some of the hyperparameters that were testedinclude the number of convolutional and fully connected layers, thenumber of filters for each convolutional layer, and the number ofnodes in the fully connected layers. The proposed approach wastested using MNIST dataset for handwritten digit classification andresults obtained indicate that the proposed approach is able to generatea CNN architecture with validation accuracy up to 96.66% onaverage

Machine Learning Approaches to Predict Patient’s Length of Stay in Emergency Department

As the COVID-19 pandemic has afflicted the globe, health systems worldwide have also been significantly affected. This pandemic has impacted many sectors, including health in the Kingdom of Jordan. Crises that put heavy pressure on the health systems’ shoulders include the emergency departments (ED), the most demanded hospital resources during normal conditions, and critical during crises. However, managing the health systems efficiently and achieving the best planning and allocation of their EDs’ resources becomes crucial to improve their capabilities to accommodate the crisis’s impact. Knowing critical factors affecting the patient length of stay prediction is critical to reducing the risks of prolonged waiting and clustering inside EDs. That is, by focusing on these factors and analyzing the effect of each. This research aims to determine the critical factors that predict the outcome: the length of stay, i.e., the predictor variables. Therefore, patients’ length of stay in EDs across waiting time duration is categorized as (low, medium, and high) using supervised machine learning (ML) approaches. Unsupervised algorithms have been applied to classify the patient’s length of stay in local EDs in the Kingdom of Jordan. The Arab Medical Centre Hospital is selected as a case study to justify the performance of the proposed ML model. Data that spans a time interval of 22 months, covering the period before and after COVID-19, is used to train the proposed feedforward network. The proposed model is compared with other ML approaches to justify its superiority. Also, comparative and correlation analyses are conducted on the considered attributes (inputs) to help classify the LOS and the patient’s length of stay in the ED. The best algorithms to be used are the trees such as the decision stump, REB tree, and Random Forest and the multilayer perceptron (with batch sizes of 50 and 0.001 learning rate) for this specific problem. Results showed better performance in terms of accuracy and easiness of implementation

A Complex Matrix Private Key to Enhance the Security Level of Image Cryptography

Standard methods used in the encryption and decryption process are implemented to protect confidential data. These methods require many arithmetic and logical operations that negatively affect the performance of the encryption process. In addition, they use private keys of a specific length, in addition to the fixed length of the data block used in encryption, which may provide the possibility of penetration of these methods, thus decreasing the level of security. In this research paper, a new method of digital image cryptography is introduced. This method is based on using a color image as an image_key to generate a sophisticated matrix private key (MPK) that cannot be hacked. The proposed method uses an initial state to set the required parameters, with secret information needed to generate the private key. The data-block size is variable, and the complicity of the MPK depends on the number of selected rounds and the data-block size. The proposed method is appropriate for publication in Symmetry because it employs a symmetrical complex matrix key to encrypt and decrypt digital images. The proposed method is simple yet very efficient in terms of throughput and scalability. The experiments show that the proposed method meets the quality requirements and can speed up the encryption–decryption process compared with standard methods, including DES, 3DES, AES, and Blowfish

Building a Secure Image Cryptography System using Parallel Processing and Complicated Dynamic Length Private Key

A method of color images cryptography will be introduced, programmed, and tested. The proposed method is based on using a digital color image as an image key; this image is to be kept secret without transmission. The proposed method will provide a high level of images protection based on the complicated and complex private key used in cryptography, this key will be changed when replacing the image key, or changing the data block size, or changing the color channel. The proposed method will be compared with other standard methods of data cryptography, and it will be shown how this method will improve the efficiency of data cryptography by minimizing the encryption-decryption time, the obtained results will be compared with the standard method of data cryptography to show the speedup achieved by the proposed method. It will be shown how to execute the proposed method in parallel, 2, 4, and 8 threads will be used to execute the method and the associated speedup will be calculated. The proposed method will protect the data by providing a high level of security, this can be achieved by using a variable-length private key, the private key length and content will depend on the selected image key, selected color matrix, and the selected block size. The block size used in the proposed method will be variable and it will be shown that the proposed method will satisfy the quality requirements by providing good value for Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR

Protecting Digital Images Using Keys Enhanced by 2D Chaotic Logistic Maps

This research paper presents a novel digital color image encryption approach that ensures high-level security while remaining simple and efficient. The proposed method utilizes a composite key r and x of 128-bits to create a small in-dimension private key (a chaotic map), which is then resized to match the color matrix dimension. The proposed method is uncomplicated and can be applied to any image without any modification. Image quality, sensitivity analysis, security analysis, correlation analysis, quality analysis, speed analysis, and attack robustness analysis are conducted to prove the efficiency and security aspects of the proposed method. The speed analysis shows that the proposed method improves the performance of image cryptography by minimizing encryption–decryption time and maximizing the throughput of the process of color cryptography. The results demonstrate that the proposed method provides better throughput than existing methods. Overall, this research paper provides a new approach to digital color image encryption that is highly secure, efficient, and applicable to various images