Melanoma is the deadliest kind of skin cancer. However, it's hard to identify melanoma during its early to mid stages by visual examination. So, there is a call for an automated model which assists in early diagnosis of skin cancer. This paper introduces an enhanced automated computer-aided model for skin diagnosis using deep learning. The model integrates an enhanced segmentation phase for locating the infected lesion of the skin and a Convolution Neural Network (CNN) is designed as a feature extractor. A classifier model has been designed based on multiclass linear Support Vector Machine (SVM) trained with CNN features extracted from the digital skin images dataset. The experimental results show an outstanding performance in the terms of sensitivity, specificity and accuracy compared with others in literature. Index Termscomputer-aided model, convolutional neural network feature, deep learning, digital skin image, and support vector machine 

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DNA compression challenge has become a major task for many researchers as a result of exponential increase of produced DNA sequences in gene databases; in this research we attempt to solve the DNA compression challenge by developing a lossless compression algorithm. The proposed algorithm works in horizontal mode using a substitutional-statistical technique which is based on Auto Regression modeling (AR), the model parameters are determined using Particle Swarm Optimization (PSO). This algorithm is called Swarm Auto-Regression DNA Compression (SARDNAComp). SARDNAComp aims to reach higher compression ratio which make its application beneficial for both practical and functional aspects due to reduction of storage, retrieval, transmission costs and inferring structure and function of sequences from compression, SARDNAComp is tested on eleven benchmark DNA sequences and compared to current algorithms of DNA compression, the results showed that (SARDNAComp) outperform these algorithms.

Problem statement: Solving the state assignment problem means finding the optimum assignment for each state within a sequential digital circuit. These optimum assignments will result in decreasing the hardware realization cost and increasing the reliability of the digital circuit. Unfortunately, the state assignment problem belongs to the class of nondeterministic polynomial time problems (NP complete) which requires heavy computations. Different attempts have been made towards solving the problem with reasonable recourses. Approach: This study presented a methodology for solving the state assignment problem, the methodology conducted a neighborhood search while using a heuristic to determine the fitness of solution. To avoid being trapped at a local optimum solution, a metaheuristic (simulated annealing) was utilized for deciding whether a new solution should be accepted. A case study was included to demonstrate the proposed procedure efficiency. Results: The proposed approach finds the optimum assignment for the case study. Conclusion: In this study, we explored the usage of a stochastic search technique inspired by simulated annealing to solve the problem of the state assignment problem. This proved the efficiency of the methodology