The field of regenerative medicine is constantly advancing and aims to repair, regenerate, or substitute impaired or unhealthy tissues and organs using cutting-edge approaches such as stem cell-based therapies, gene therapy, and tissue engineering. Nevertheless, incorporating artificial intelligence (AI) technologies has opened new doors for research in this field. AI refers to the ability of machines to perform tasks that typically require human intelligence in ways such as learning the patterns in the data and applying that to the new data without being explicitly programmed. AI has the potential to improve and accelerate various aspects of regenerative medicine research and development, particularly, although not exclusively, when complex patterns are involved. This review paper provides an overview of AI in the context of regenerative medicine, discusses its potential applications with a focus on personalized medicine, and highlights the challenges and opportunities in this field.

The competition between malware creators and those who work on malware detection, led to emergence and development of multifarious techniques for both creation and detection. In recent years, metamorphic malwares have become a serious challenge for antivirus programmers. Signature and heuristic based techniques cannot offer plenary solutions for detection of metamorphic malwares; because such those malwares can reconstruct from generation to generation without destruction in their functions. It caused difficulty in detecting them. In this research, we introduce a new technique for detecting the unknown malwares based on the counting the assembly instructions. Statistics which are obtained from analysis of different variables of a specific malware can be utilized as a signature. Also, accuracy, efficiency and fast performance must be considered as important issues. So far, lack of some of these features is seen in almost all the suggested methods. But, in the proposed method, speed is not a challenging issue, since the extraction of statistics from assembly codes is a very fast process. Experiments on several malwares and harmless programs indicated the excellence of this method rather than previous studies.

This study offers a heuristic genetic algorithm based method for message hiding in a carrier image. This approach focuses on the "before embedding hiding techniques" by trying to find appropriate places in carrier image to embed the message with the least changes of bits. Due to it, segmentation is done in order to convert the LSBs and message strings to the sets of blocks for participation in genetic algorithm. After finding the right places, secret message blocks are embedded and a key file is created to make the message extraction available by providing the data addresses. Experimental results with the least changes in image histogram (by the policy of setting an appropriate amount for the length of block bits) show the efficiency of current method.

Shapes detection from noisy backgrounds is one of the hot topics in the field of image processing and pattern recognition. This paper investigates a new method which uses different filters like median and Laplacian filter for reducing the effect of noise in the image and applies the Canny edge detection algorithm. As the final step, a special form of Hough transform which is called Circular Hough Transform will be applied to detect the circular shapes in the image. To prove the efficiency of proposed method, results of implementation in real world use is presented. Results clearly show the good performance of it. This method is absolutely useful in the fields like: building extraction from aerial satellite images, digital filming, biometric security, astronomy, etc.

Malware is defined as any type of malicious code that is the potent to harm a computer or a network. Modern malwares are accompanied with mutation characteristics, namely polymorphism and metamorphism. They let malwares to generate enormous number of variants. Rising number of metamorphic malwares entails hardship in analyzing them for signature extraction and database updates. In spite of the broad use of signature-based methods in the security products, they are not able detect the new unseen morphs of malware, and it is stemmed from changing the structure of malware as well as the signature in each infection. In this paper, a novel method is proposed in which the proportion of opcodes is used for detecting the new morphs. Decision trees are utilized for classification and detection of malware variants based on the rate of opcode frequencies. Three metrics for evaluating the proposed method are speed, efficiency and accuracy. It was observed in the course of experiments that speed and time complexity will not be challenging factors; because of the fast nature of extracting the frequencies of opcodes from source assembly file. Empirical validation reveals that the proposed method outperforms the entire commercial antivirus programs with a high level of efficiency and accuracy.