Avoiding congestion for routing resources has become one of the most important placement objectives. In this paper, we present a machine-learning model for accurately and efficiently estimating congestion during FPGA placement. Compared with the state-of-the-art machine-learning congestion-estimation model, our results show a 25% improvement in prediction accuracy. This makes our model competitive with congestion estimates produced using a global router. However, our model runs, on average, 291x faster than the global router.

In recent years, the chaotic logistic map key (CLMK) has been widely used for image encryption due to its ability to generate random and unpredictable sequences of numbers. The CLMK algorithm uses the chaotic logistic map to generate a key stream, which is then used to encrypt the plaintext image. This study proposes an image encryption scheme based on CLMK, Collatz Conjecture, and indexing techniques; it uses a private key of a 15-digit size that is parsed in a novel way to generate a considerable keyspace reaches up to 297. The Collatz Conjecture generator is used to create variable length sequences that are used to generate the Chaotic parameters $r_{1}$ , $r_{2}$ , $x_{1}$ , and $x_{2}$ ; these parameters are used to create a 2D CLMK for encrypting each color channel and a 1D vector that is used for permeation, both confusion and diffusion are performed in four consecutive rounds to ensure the robustness of the proposed approach. The random sequences generated by the Collatz Conjecture were tested for randomness using the NIST benchmark randomness test suit, and they passed most tests. The proposed method is robust against all the well-known attacks, including differential attacks, white Gaussian noise attacks, Salt-and-pepper noise attacks, clipping attacks, occlusion attacks, and known plaintext attacks. Various experiments were conducted to evaluate the performance of the proposed method in terms of encryption time, throughput, and several image quality metrics. The results indicate that the proposed method is efficient regarding encryption time, and throughput enhancement can reach up to 3.34 times the state-of-the-art chaotic encryption methods.

Chronic Renal Failure (CRF) is a gradual loss of kidney's function over a period of time, ranging from months to years. Unlike other chronic diseases, CRF is not yet thoroughly explored in literature. In this paper, we propose a new clinical decision support system for diagnosing patients with CRF. Several data classification algorithms including Artificial Neural Networks (ANNs), Naïve Bayes and Decision Tree are developed and implemented to diagnose patients with CRF and determine the progression stage of the disease. A clinical dataset of 102 instances is collected from patients' records and used in this study. Performance of the developed CRF diagnosis system is assessed in terms of diagnosis accuracy, sensitivity, and specificity and is evaluated by specialist physicians. Furthermore, the open source Weka software is also used in this study for performance comparison and evaluation purposes. The obtained results showed that the developed decision tree algorithm is the most accurate CRF classifier (92.2%) when compared to all other algorithms/implementations involved in this study.

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.

This paper presents a new clinical decision support system for diagnosing patients with Chronic Renal Failure (CRF) which is not yet thoroughly explored in literature. This paper aims at improving performance of a previously reported CRF diagnosis system which was based on Artificial Neural Network (ANN), Decision Tree (DT) and Naïve Bayes (NB) classifying algorithms. This is achieved by utilizing more efficient data mining classifiers, Support Vector Machine (SVM) and Logistic Regression (LR), in order to: (i) diagnose patients with CRF and (ii) determine the rate at which the disease is progressing. A clinical dataset of more than 100 instances is used in this study. Performance of the developed decision support system is assessed in terms of diagnostic accuracy, sensitivity, specificity and decisions made by consultant specialist physicians. The open source Waikato Environment for Knowledge Analysis library is used in this study to build and evaluate performance of the developed data mining classifiers. The obtained results showed SVM to be the most accurate (93.14%) when compared to LR as well as other classifiers reported in the previous study. A complete system prototype has been developed and tested successfully with the aid of NHS collaborators to support both diagnosis and long-term management of the disease.

Due to the availability of several social media platforms and their use in sending text messages, it is necessary to provide an easy and safe way to protect messages from being hacked especially in the presence of intruders and data thieves, and taking into consideration that most of those messages are confidential and personal, it is necessary to provide an easy and safe way to protect messages from being hacked. In this research paper, a simple and easy method of message cryptography will be proposed. The method divides a message into blocks with fixed sizes. The block size ranges from 2 to 60. The method uses a secret color image to generate an array with a size equal to the number of resulted blocks. The array will then be used as a private key. Each element of the private key will be used to calculate the number of rotation digits for the associated block in order to apply block rotation left operation. The proposed method will be examined using the parameter’s Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), and throughput. The proposed method will be compared with other standard methods of message cryptography, such as Data Encryption Standard (DES), Triple-DES (3DES), Advanced Encryption Standard (AES), and Blow Fish (BF). Experimental results show that the proposed method is secured enough based on using secret image, block size, and calculated Rotation Left Digits (RLD) for each block.