In this article, the modeling of Automatic Link Establishment device or ALE using neural networks or artificial intelligence is introduced. In general, the performance of an ALE device can be modeled. The purpose of modeling is to design this device using new methods and implement it using existing tools and thus localize it. In this regard, in this article, using neural networks and MLP algorithm, it has been tried to model and implement the overall performance of the ALE device. At first, after examining telecommunication channels and nonlinear effects and noise on data transmission, a model for data transmission in telecommunication channels has been introduced and coded in the MATLAB software environment. Then the types of neural networks and their applications have been introduced and the best algorithm has been selected for modeling the ALE device. In the following, several models have been made and compared using MATLAB software tools and MLP algorithm coding. Finally, the proposed model, which is based on the MLP algorithm, can predict the right channel for each new output with the least error. After optimization, the proposed models can be implemented on FPGA and provide a way to build this device in the country.

Background and Aim: Despite the implementation of effective preventive and therapeutic programs, no significant success has been achieved in the reduction of tuberculosis. One of the reasons is the delay in diagnosis. Therefore, the creation of a diagnostic aid system can help to diagnose early Tuberculosis. The purpose of this research was to evaluate the role of the Naive Bayes algorithm as a tool for the diagnosis of pulmonary Tuberculosis. Materials and Methods: In this practical study, the study population included Patients with TB symptoms, the study sample is recorded data of 582 individuals with primary Tuberculosis symptoms in Tehran's Masih Daneshvari Hospital. The data of samples were investigated in two classes of pulmonary Tuberculosis and non-Tuberculosis. A Naive Bayes algorithm for screening pulmonary Tuberculosis using primary symptoms of patients has been used in Python software version 3. 7. Results: Accuracy, sensitivity and specificity after the implementation of the Naive Bayes algorithm for diagnosis of pulmonary Tuberculosis were %95. 89, %93. 59 and %98. 53, respectively, and the Area under curve was calculated %98. 91. Conclusion: The performance of a Naive Bayes model for diagnosis of pulmonary Tuberculosis is accurate. This system can be used to help the patient and manage illness in remote areas with limited access to laboratory resources and healthcare professional and cause to diagnose early Tuberculosis. It can also lead to timely and appropriate proceedings to control the transmission of TB to other people and to accelerate the recovery of the disease.

This introduction to this special issue discusses artificial intelligence (AI), commonly defined as “a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation. ” It summarizes seven articles published in this special issue that present a wide variety of perspectives on AI, authored by several of the world’s leading experts and specialists in AI. It concludes by offering a comprehensive outlook on the future of AI, drawing on minor, and macro-perspectives.

In the history of the law of torts, there is damages arising out of things. Some of these damages are because of things’ actions, such as buildings collapses, animals' behaviors, and cars accidents and so on. At the first, who have liability for the victim’ s injuries and damages are the proprietor and possessors; but in pursuit of technical developments of the era, we shouldn’ t ignore liability of producers and designers of these products. We all know that the technological progress of humankind is the cause of unbelievable innovations. One of this human incredible ingenuity is making of object acting similar to mankinds. For example, it can be a human-assisted robot, such as cleaning and cooking, or specializing in medicine, and the like. So there is a legal problem asking about legal situations of these creations. Now, the smart object may not be a common issue in the community, but events show that we will have to confront with difficult issues in the near future. So, today we need to think about its legal dimensions and solve the problems arising from the personality, ownership, contract and responsibility of these digital age objects. Moreover, studying the emerging institutions sheds light on current issues in the light of which we can illuminate the dim dimensions of many current and traditional problems and illustrate the ways we have gone wrong. The objects that are armed with a will, can have ownership on others objects and conclude a contract with persons and may harm others, either voluntarily or by the will of people realized in the object. For example, active home-based artificial intelligence can provide harm to guests or surgery or treatment robots can harm patients, or a driverless car may cause harms to pedestrian and create financial losses with accidents. If the basis of liability is fault, the owner of the objects would be liable as much as it was at fault. A master is someone who has effective supervision or guidance on an object at the time of the crash, such as a car driver. In Iranian law, the responsibility of the owner is based on fault, whether living, inanimate, movable or immovable property. In addition to the owner and possessor, sometimes the creator and designer of the object is responsible for the caused damage. With all of the foregoing, we may still be considering new and separate AI. But first, we need to know that AI has three features: it understands the world, analyzes the information received, and then acts on it. Machines are planned elsewhere and do not have means to act in the sense of freedom of the human will; now, is it right to hold them responsible? Today, it appears that there is less of a means to perceive, think and act independently. In fact, most machines are not independent and cannot think and decide. The object may be autonomous in performing certain tasks, so people associated with the object, such as the artificial intelligence manager or the author of the program and its software, will be responsible for compensating for the resulting damage. Apart from that, we may have a personality for artificial intelligence and recognize him or her as a right-doer; in this case, in addition to being a person's representative and subject to responsibility, the robot may be itself responsible for its actions. In this situation, the robot knows the true meaning of 'act' and truly is realized. The machine has many different types of autonomy and power. Some tools are human instrument and others have the power to act. Among the latter category, some may be in control of their affairs, and some are still involved in the determinacy of algorithms. Artificial intelligence is the criterion of selfresponsibility, that is, the robot is able to manage its financial affairs. In that case, civil liability can be borne. Otherwise, the person is responsible for its actions and if it is damaged by a third party, the person responsible may be the owner or the developer or the creator. Given the fact that there is still theoretically independent of the robots, it is necessary in the present circumstances to consider it a tool and hold others responsible for its actions. However, the legislator is supposed to prescribe rules on the personality and responsibility of artificial intelligence, and this is impossible unless the unknown dimensions of the presence of these electronic persons in society have been explored by lawyers.

As one of the important goals of advanced technologies, the application of artificial intelligence (AI) in aging has managed to significantly improve the healthcare for older adults. This technology has been successful in precise data analysis, prognosis of diseases, providing individualized health recommendations, facilitating medication management, monitoring exercises, and even implementing social interactions for older adults. Thanks to great achievements, ranging from promoting safety to presenting tools for domestic support and medical care, AI has turned into an attractive and effective technology in aging. Nevertheless, using AI in aging, some specific issues such as privacy protection, ethics in the employment of data, and appropriate management of information should be taken to account. Hence, paying attention to the ethical and humanitarian aspects carries immense weight. In general terms, the development of AI in the field of aging, demonstrates the noticeable capacity of technology to improve older adults׳ lives. Given the rise in elderly population, these innovations will lead to the provision of better facilities in the service of older adults’ health and welfare. Keywords: Artificial intelligence, Ageing.