No abstract

In this study, the vulnerability tests were carried out through the techniques of penetration, crack and impersonation the wireless network connections in which the thing in the industry 4.0 was communicating. The MAC addresses of all the devices by the penetration technique has been seen in the network identified. The device is dropped from the network using de-auth attacks with MAC address. As a result of this, the operation of Industrial 4.0 systems is interrupted or stopped. It is possible to predict the standard passwords of the distributors with WPS feature turned on by means of cracking techniques and to penetration the wireless network or to disable the network. In the end, it can be seen that the existing data can be deleted or collected by adding a new device to the system with the fake technique of the wireless network. The results showed that WEP, WPA, and WPA2 security were not sufficiently used in the wireless network. As a result of the penetration methodology, it was determined that the use of wireless networks would not be a solution in the connections where data security was high in Industry 4.0.

Aim: Today, data banks contain unpredictable data. Together with the advances in data science, large data offer the potential to better understand the causes of diseases. This potential results from the processing, analysis or modeling of machine learning algorithms. Various data sets stored in different institutions are not always shared directly due to privacy and legal concerns. This problem limits the full use of large data in health research. Federated learning is aimed at developing artificial intelligence systems based on both high accuracy and data privacy. Materials and Methods: In this study, a federated learning approach was proposed in order to access any data and develop machine learning applications without sharing personal information within the scope of data privacy. Firstly, the structure of the Federated learner has been studied. It was then determined how federated learning should be used in machine learning models in different health applications. Results: In federated learning, the model is trained on local computers and its updates are transferred to a central server. The updated model is then transferred to local models. In this way, the central model is trained without seeing the data. Conclusion: It is necessary to make machine learning models in which confidentiality is applied with data obtained from health. For this, federated learning must be integrated into traditional machine learning applications. Thus, high performance is envisaged to be achieved with big data where data confidentiality is adopted.

Nowadays, the main features of Industry 4.0 are interpreted to the ability of machines to communicate with each other and with a system, increasing the production efficiency and development of the decision-making mechanisms of robots. In these cases, new analytical algorithms of Industry 4.0 are needed. By using deep learning technologies, various industrial challenging problems in Industry 4.0 can be solved. Deep learning provides algorithms that can give better results on datasets owing to hidden layers. In this chapter, deep learning methods used in Industry 4.0 are examined and explained. In addition, data sets, metrics, methods, and tools used in the previous studies are explained. This study can lead to artificial intelligence studies with high potential to accelerate the implementation of Industry 4.0. Therefore, the authors believe that it will be very useful for researchers and practitioners who want to do research on this topic.

Time is a phenomenon interlinked with an act because an act must occur at a specific time. There are three foundational times in all languages. These are past, present, and future. The time of occurrence for a specific action is indicated in Arabic by the verb because it expresses the time of action. Verbs in Arabic get separated into three forms: the perfect, the imperfect, and the imperative, a version derived from the imperfect. The basis of the time system in Arabic is composed of these three forms. The perfect indicates the past, the imperfect indicates both the present and the future, and the imperative indicates the future time. These times expressed by verbs are morphological times which the verbs provide independent of any context. Time can also be expressed with forms other than verbs. These forms are the active participle, the passive participle, the verbal noun, and the infinitive. Time expressions of these forms occur when used within sentences. This is called syntactical time. Syntactical time can only be understood by looking at the whole sentence. Because the factor that expresses the tense, here, is the fluency of the sentence and the context of it, rather than the forms used. When associated with syntactical time, the perfect can refer to present tense and future tense. The same goes for the imperfect as well, it may refer to the past tense. The time indicated by the verbs and the other forms -which act as verbs- can be inferred with the knowledge of their either linguistic or situational context. Prepositions especially provide for these tense changes that occur in sentences. For example, the imperfect verb used with لَـمْ and لَمَّا indicates the past tense, and the perfect verb used with the conditional preposition إِنْ refers to the future tense. The imperfect verb; except for لَـمْ and لَمَّا, when it comes as meczum or as mansup, refers to past tense, and refers to future tense when it gets used as merfu. Therefore, to understand the tense of a sentence in Arabic one must recognize the prepositions in it. Apart from prepositions, which provide linguistic context, the expression of time can also be determined by the situation at the moment of utterance. The situational context, which we call hâlî karine, plays a major role in determining the tense expressed by verbs and nouns which get used instead of verbs. In the first part of our study, The Forms of the Time in Arabic, Their Places in Use and Comparison with the Times in Turkish, occurrences of the morphological and the syntactical times are observed along with their usage areas. In the second part, Arabic counterparts of Turkish Forms of time are given and compared. By doing so, it is aimed to make learning and teaching the forms of time, in Turkish and Arabic Languages, easier. Time is a phenomenon interlinked with an act because an act must occur at a specific time. There are three foundational times in all languages. These are past, present, and future. The time of occurrence for a specific action is indicated in Arabic by the verb because it expresses the time of action. Verbs in Arabic get separated into three forms: the perfect, the imperfect, and the imperative, a version derived from the imperfect. The basis of the time system in Arabic is composed of these three forms. The perfect indicates the past, the imperfect indicates both the present and the future, and the imperative indicates the future time. These times expressed by verbs are morphological times which the verbs provide independent of any context. Time can also be expressed with forms other than verbs. These forms are the active participle, the passive participle, the verbal noun, and the infinitive. Time expressions of these forms occur when used within sentences. This is called syntactical time. Syntactical time can only be understood by looking at the whole sentence. Because the factor that expresses the tense, here, is the fluency of the sentence and the context of it, rather than the forms used. When associated with syntactical time, the perfect can refer to present tense and future tense. The same goes for the imperfect as well, it may refer to the past tense. The time indicated by the verbs and the other forms -which act as verbs- can be inferred with the knowledge of their either linguistic or situational context. Prepositions especially provide for these tense changes that occur in sentences. For example, the imperfect verb used with لَـمْ and لَمَّا indicates the past tense, and the perfect verb used with the conditional preposition إِنْ refers to the future tense. The imperfect verb; except for لَـمْ and لَمَّا, when it comes as meczum or as mansup, refers to past tense, and refers to future tense when it gets used as merfu. Therefore, to understand the tense of a sentence in Arabic one must recognize the prepositions in it. Apart from prepositions, which provide linguistic context, the expression of time can also be determined by the situation at the moment of utterance. The situational context, which we call hâlî karine, plays a major role in determining the tense expressed by verbs and nouns which get used instead of verbs. In the first part of our study, The Forms of the Time in Arabic, Their Places in Use and Comparison with the Times in Turkish, occurrences of the morphological and the syntactical times are observed along with their usage areas. In the second part, Arabic counterparts of Turkish Forms of time are given and compared. By doing so, it is aimed to make learning and teaching the forms of time, in Turkish and Arabic Languages, easier.