In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

Due to the potential security problem about key management and distribution for the symmetric image encryption schemes, a novel asymmetric image encryption method is proposed in this paper, which is based on the elliptic curve ElGamal (EC-ElGamal) cryptography and chaotic theory. Specifically, the SHA-512 hash is first adopted to generate the initial values of a chaotic system, and a crossover permutation in terms of chaotic index sequence is used to scramble the plain-image. Furthermore, the generated scrambled image is embedded into the elliptic curve for the encrypted by EC-ElGamal which can not only improve the security but also can help solve the key management problems. Finally, the diffusion combined chaos game with DNA sequence is executed to get the cipher image. The experimental analysis and performance comparisons demonstrate that the proposed method has high security, good efficiency, and strong robustness against the chosen-plaintext attack which make it have potential applications for the image secure communications.

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An Image Encryption Method Based on Elliptic Curve Elgamal Encryption and Chaotic Systems

Abstract We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative model. Contrary to previous evaluation metrics for generative models, like Kernel Density Estimation, it only considers samples of the model which are close to training data records. The second attack specifically targets Variational Autoencoders, achieving high membership inference accuracy. Furthermore, previous work mostly considers membership inference adversaries who perform single record membership inference. We argue for considering regulatory actors who perform set membership inference to identify the use of specific datasets for training. The attacks are evaluated on two generative model architectures, Generative Adversarial Networks (GANs) and Variational Autoen-coders (VAEs), trained on standard image datasets. Our results show that the two attacks yield success rates superior to previous work on most data sets while at the same time having only very mild assumptions. We envision the two attacks in combination with the membership inference attack type formalization as especially useful. For example, to enforce data privacy standards and automatically assessing model quality in machine learning as a service setups. In practice, our work motivates the use of GANs since they prove less vulnerable against information leakage attacks while producing detailed samples.

https://sciendo.com/pdf/10.2478/popets-2019-0067

Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models

Quantum chaotic cuckoo search algorithm is proposed for the data clustering problem.The performance of the proposed approach was assessed on six well known datasets.The Chaos maps and Boundary handling strategy enhance the cuckoo search algorithm.The nonhomogeneous quantum update improves the global search ability.The significant superiority of the proposed algorithm over eight recent algorithms. This paper presents a new quantum chaotic cuckoo search algorithm (QCCS) for data clustering. Recent researches show the superiority of cuckoo search (CS) over traditional meta-heuristic algorithms for clustering problems. Unfortunately, all the cuckoos have identical search behaviours that may lead the algorithm to converge to local optima. Also, the convergence rate is sensitive to initial centroids seeds that are randomly generated.Therefore, the main contribution of this paper is to extend the CS capabilities using nonhomogeneous update inspired by the quantum theory in order to tackle the cuckoo search clustering problem in terms of global search ability. Also, the randomness at the beginning step is replaced by the chaotic map in order to make the search procedure more efficient and improve the convergence speed. In addition, an effective strategy is developed to well manage the boundaries.The experimental results on six famous real-life datasets show the significant superiority of the proposed QCCS over eight recent well known algorithms including, genetic quantum cuckoo search, hybrid cuckoo search and differential evolution, hybrid K-means and improved cuckoo search, standard cuckoo search, quantum particle swarm optimization, differential evolution, hybrid K-means chaotic particle swarm optimization and genetic algorithm for all benchmark datasets in terms of internal and external clustering quality.

A new quantum chaotic cuckoo search algorithm for data clustering

Pattern recognition is the task of choosing the pertinent and descriptive features that best describes the target concept during feature selection (FS). Choosing such descriptive features becomes a daunting task in large-volume datasets which have high dimensionality. In such cases, selecting the discriminative features with better classification accuracy is tedious. To overcome this issue, in recent times, many search heuristics have been used to select the best features from these large-volume datasets. In this work, a sine–cosine algorithm (SCA) with Elitism strategy and new best solution update mechanism is proposed to select best features/attributes to improve the classification accuracy. Improved version of SCA is named as improved sine–cosine algorithm (ISCA). Wrapper-based FS approach is used. ELM with radial basis function kernel is used as the learning algorithm. For experimentation, ISCA is tested with ten benchmark datasets. Experimental results have proved the efficiency of ISCA in achieving better classification performance along with less number of features. Both computational and time complexity has been handled by this algorithm in an expedite manner. The potency of this algorithm is proved by comparing its results with three well-known meta-heuristics such as GA, PSO and basic SCA. Finally, it can be seen that pattern classification using ISCA has been commendable in achieving better classification performance.

Sine---cosine algorithm for feature selection with elitism strategy and new updating mechanism

Automatic Handwritten Digits Recognition (HDR) is the process of interpreting handwritten digits by machines. There are several approaches for handwritten digits recognition. In this paper we have proposed an appearance feature-based approach which process data using Histogram of Oriented Gradients (HOG). HOG is a very efficient feature descriptor for handwritten digits which is stable on illumination variation because it is a gradient-based descriptor. Moreover, linear SVM has been employed as classifier which has better responses than polynomial, RBF and sigmoid kernels. We have analyzed our model on MNIST dataset and 97.25% accuracy rate has been achieved which is comparable with the state of the art. General Terms Image Processing, Computer Vision, Artificial Intelligence

/pdf/efficient-handwritten-digit-recognition-based-on-histogram-3gbasin2f9.pdf

Efficient Handwritten Digit Recognition based on Histogram of Oriented Gradients and SVM

Very recently evolutionary optimization algorithms use the Genetic Algorithm to improve the result of Optimization problems. Several processes of the Genetic Algorithm are based on 'Random', that is fundamental to evolutionary algorithms, but important defections in the Genetic Algorithm are local convergence and high tolerances in the results, they have happened for randomness reason. In this paper we have prepared pseudo random numbers by Lorenz chaotic system for operators of Genetic Algorithm to avoid local convergence. The experimental results show that the proposed method is much more efficient in comparison with the traditional Genetic Algorithm for solving optimization problems.

/pdf/chaotic-genetic-algorithm-based-on-lorenz-chaotic-system-for-4qad19hm5e.pdf

Chaotic Genetic Algorithm based on Lorenz Chaotic System for Optimization Problems

Nowadays, natural-based oxygenated fuels, especially biodiesel and ethanol, have been considered as substitutes for fossil fuels. Because of relatively lower energy content of oxygenated fuels, it is necessary to blend them with fossil ones. In this research, authors conducted an investigation on some BED blends to determine and compare their effects on equivalence ratio, exhaust oxygen fraction and water and oil temperature in a diesel engine. For this purpose, 18 different blendes of ethanol and biodiesel with net diesel fuel were tested in a MT4-244 engine 1 considering two engine speeds in full load condition. In almost all samples the equivalence ratio decreased with increasing of biodiesel and ethanol percents. Exhaust oxygen fraction in all of samples increased with increasing of biodiesel and ethanol percents, whereas the engine water and oil temperatures slightly reduced.

Effect of some BED blends on the equivalence ratio, exhaust oxygen fraction and water and oil temperature of a diesel engine

Nowadays many techniques are being used to increase the reliability of human identification systems. Iris is a part of human body that is desirable for biometric identification and has favorable factors. We have focused on the reality that iris is a fractal phenomenon in this paper. During the production of new fractals, some features will be extracted by Chaos Game mechanism. These features are useful and effective in iris identification. There are three steps for iris identification with fractal and Chaos Game Theory. The first step is making a new fractal. The second step includes extracting features during the first step. Finally, the iris identification based on extracted features is the third step. We have named this technique Iris Identification based-Fractal and Chaos Game Theory (Iris-IFCGT). This technique has some fractal properties like stability against zoom, removing part of the iris image, no sensitivity on rotation and so on as well as desirable speed which helps preventing time consuming process of pattern recognition.

Towards a fast method for iris identification with fractal and chaos game theory

Abstract Aim The aim of this study is to explore and describe the recovery experiences of nurses working in COVID‐19 wards. Background The global outbreak of coronavirus in 2020 has extracted job stress for nurses. Job stress has impacts on physical and mental health and performance, so recovery is essential to restore the lost energy resources. Method Semi‐structured interviews were conducted with 12 nurses in Isfahan city of Iran. Results The research results were extracted as 5 main themes, 14 subthemes and 54 items. The main themes are detachment from work, relaxation, non‐work activities, affiliation and meaning. The combination of these themes led to the clear statement that using recovery experiences is a major step toward relieving the nurses' COVID‐19‐related stress and their physical and mental resuscitation. Conclusion The use of recovery experiences, including detachment from work, relaxation, non‐work activities, affiliation and meaning by nurses, helps them cope with job stressors and regain their resources. Implications for Nursing Management It is the responsibility of health system policy makers, hospital managers and nurse managers to design and implement training programmes for nurses to use recovery experiences as stress management techniques in their profession.

Reza Ebrahimzadeh

Papers

Efficient Handwritten Digit Recognition based on Histogram of Oriented Gradients and SVM

Chaotic Genetic Algorithm based on Lorenz Chaotic System for Optimization Problems

Effect of some BED blends on the equivalence ratio, exhaust oxygen fraction and water and oil temperature of a diesel engine

Towards a fast method for iris identification with fractal and chaos game theory

Nurses' recovery experiences during the COVID‐19 pandemic in Isfahan, Iran: A qualitative study