نشریه سیستم های پردازشی و ارتباطی چند رسانه ای هوشمند
سال سوم شماره 3 (پیاپی 9، پاییز 1401)

Intraosseoues Dental implants are widely used in the field of oral restoration. Endosseous dental implants have been well accepted for replacing missing teeth in today’s dental practice. The success of dental implant therapy is essentially based on the process of osseointegration. These success rates depends on the mechanical,structural and surfaces properties in contact with the jaw bone and timely identification of osseointegration process . In zirconia implants,the combined surface modification of femtosecond laser and bioceramic coatings is important to accelerateosteointegration.The major challenge for contemporary dental implantologists is to provide oral rehabilitation to patients with healthy bone conditions asking for rapid loading protocols or to patients with quantitatively or qualitatively compromised bone. These charging conditions require advances in implant surface design.The elucidation of bone healing physiology has driven investigators to engineer implant surfaces that closely mimic natural bone characteristics.The type of surface modification has a great effect on uniting the implant with the jawbone. But still many problems such as osseointegration defect and infection adjacent to the implant lead to their failure in clinical applications This paper provides a comprehensive overview of surface modifications that beneficially alter the topography, hydrophilicity, and outer coating of dental implants in order to enhance osseointegration in healthy as well as in compromised bone. It is important to use effective surface modification methods and apply appropriate coatings in order to create successful osseointegration and preventing bacteria from adhesion. Byreducing periimplantitis and accelerating osseointegration, it will lead to improved repair and clinical success. The review gives an insight of the various surface modifications and designs that can be successfully applied on dental implants so that a greater level of osseointegration can be achieved. Major advancements have been made in order to develop implants with innovative surface topography and design. These modifications have greatly influenced the rate and degree of osseointegration. Therefore, the purpose of this review article was study new methods of surface modification for detecting of accelerating osteointegration by sensory nodes that located in wireless body area network, reducing the periimplantitis and aims to comprehensively discuss currently available implant surface modifications commonly used in implantology in terms of their impact on osseointegration and biofilm formation,which is critical for clinicians to choose the most suitable materials to improve the survival of implantation and Follow-up of patients at any time without the use of X-rays with sensory nodes that located in body area wireless network..

IntroductionIn this article, the performance of adaptive traffic signal control systems based on an artificial intelligence technique, namely Firefly Algorithm, for traffic control at urban intersections has been investigated. In order to check the proposed algorithm, the required data were first collected from two intersections in Yazd city. These intersections were then simulated using AIMSUN traffic simulator software and calibrated and validated under existing conditions. In the next step, the adaptive control system based on the proposed Firefly algorithm was developed and then used in simulated intersections and its performance was compared with pre-time control system in terms of intersection traffic capacity and vehicle queue length at the entry approaches. The t-test was used for a more scientific investigation. For this reason SPSS software was used as one of the most widely used statistical software to perform this test.MethodIn order to concurrently optimize vehicle departure volume or throughputat and queue length at the entry approaches of each intersection, the Firefly algorithm was used to to develop a multi-objective adaptive traffic signal control logic and appropriately distribute the effective green time in each cycle between the entry approaches. ResultsThe simulation results indicated a lower average queue length and higher throughput when the proposed adaptive model was compared with pre-time model at both intersections. The t-test results showed that the adaptive traffic signal control method has resulted in a significant lower average queue length than the pre-time time control at one of the intersections with p-value equal to 0.002. However, this improvement was not statistically significant for the other intersection. Moreover, the t-test results on the average flow departure volume measure for both intersections indicated a significant improvement with p-value equal to 0.000. when the proposed adaptive method was compared to the pre- time method.DiscussionAccording to the results, the proposed adaptive model showed better overall performance in the scope of this research than the pre-time control method. The results indicate that the performance of adpative signal controls could be enhanced when artificial intelligence techniques such as Firefly algorithm are used in the control logic and a multi-objective optimization approach is used.

Software development can be considered an activity that uses various technological advances and requires high knowledge. For this reason, every software development project contains elements of uncertainty known as project risk. The success of a software development project is highly dependent on the amount of risk associated with each project activity. Therefore, as a project manager, awareness of risks is not enough. In order to achieve a successful outcome, a project manager must be able to identify, then evaluate, prioritize, and ultimately manage all major risks.Risk management is a process to identify, eliminate and predict possible risks. In other words, this process is related to all the activities that are done to reduce the uncertainty associated with specific tasks or events. Risk management focuses on identifying risks and dealing with them appropriately. Projects have individual or general risks. Some of these risks are related to a specific activity and others to the project. Usually, risks are first identified and associated with project activities. Determining how people behave to achieve strategic activity goals is to identify risks. One of the methods of improvement is to take help from new algorithms and use machine learning techniques in the process of identifying or predicting possible risks.The use of different algorithms and techniques to identify software risks has always attracted the attention of experts. In this study, the grasshopper optimization algorithm has been used to improve classification accuracy and increase accuracy as well as reduce specificity. In this method, by combining the proposed algorithm with the support vector machine algorithm, it is used to obtain better and more acceptable results in data with large dimensions in order to reduce the dimension and also select the feature, which has attracted the attention of many researchers.The grasshopper optimization algorithm has not been used in the risk prediction system of software projects, the obtained results have shown that this proves the applicability and strength of the grasshopper optimization algorithm for solving problems with unknown and real search spaces.The purpose of this study is to predict the risks of software projects with the help of the grasshopper optimization algorithm. In this method, feature selection and reduction is done by the grasshopper optimization algorithm, and vector machine classification methods are used to classify risk and features.

Science and technology are growing rapidly and complex networks have become a substantial necessity to our daily life in a way that the separation of people from complex networks built on fundamental needs of human life is almost impossible. In this research, we presented a model for multi-layer dynamic social networks to discover influential groups based on a combination of developing frog-leaping algorithm and C-means clustering. We collected the data in the first step. Then, we conducted data cleansing and normalization in order to identify influential individuals and groups using the optimal data by forming a decision matrix. Hence, using the matrix, we conducted identification and clustering (based on phase clustering) and also determined the importance of each group. In order to discover influential individuals and groups in social networks, frog-leaping algorithm was used to improve identification of influence parameters, which lead to improvement in nodes importance. In measurement and simulation of clustering section, the proposed method was contrasted against K-means method and its equilibrium value in cluster selection resulted 5. The proposed method presented a more genuine improvement in comparison to the other compared methods. However, measuring precision indicator for the proposed method had 3.3 improvement in comparison to similar methods and recorded 3.8 improvement in comparison to M-ALCD basic method.

In this research, we examine the Investigate the relationship between product evaluation And consumer acceptance of the intention to repurchase In the e-commerce environment (Case Study: digikala Web site). The method of this research is applied in terms of target type and descriptive-analytical method in a survey method. The data collection tool was a researcher-made questionnaire. The statistical population in this study is the users of the site of digikala. To determine the sample size, the unlimited Cochran's formula was used, which was distributed and collected based on the calculation of 384 questionnaires. Lisrel software was used to analyze the data. The results showed that perceived risk had a negative and significant effect on online trust and intention to buy again. Online trust has a positive and meaningful effect on perceived usefulness and the intention to re-purchase and the perceived usefulness of re-purchasing intention, the reputation of the website on perceived quality, perceived value and online trust.The results showed that perceived risk had a negative and significant effect on online trust and intention to buy again. Online trust has a positive and meaningful effect on perceived usefulness and the intention to re-purchase and the perceived usefulness of re-purchasing intention, the reputation of the website on perceived quality, perceived value and online trust.The results showed that perceived risk had a negative and significant effect on online trust and intention to buy again. Online trust has a positive and meaningful effect on perceived usefulness and the intention to re-purchase and the perceived usefulness of re-purchasing intention, the reputation of the website on perceived quality, perceived value and online trust.

Academic success of students is one of the important goals in educational environments. In recent years, with the rapid proliferation of information technology in the field of education has led to a new model called "electronic learning". Nowadays, the application of this technology in education has made the education process go beyond time, geographical and political limitations. Providing effective training is an example benefit of e-learning. Considering an appropriate teaching method improves the performance of learners in the educational environment. In traditional education, there is a direct interaction between the learner and the teacher, which makes it possible for the teacher to adapt the teaching method to the learner's conditions. In electronic education, the absence of this direct interaction demands methods for the personalization of education. In this paper, using the data mining approach, the knowledge level of the learners is determined. This knowledge can be observed and collected by the system. Intelligent educational systems use a model of the learner that represents the level of literacy and skill of the learner in a specific field, and use it to analyze the learner's inputs to the system during educational interactions.

In this paper, LP-MLTSVM semi-supervised learning method is used to improve the learning quality and the satisfaction of learners in e-learning. The LP-MLTSVM algorithm is a semi-supervised algorithm developed based on the support vector machine algorithm. The proposed model creates a multi-class classification. In the proposed model, the data of the electronic education center of Semin have been used. This center has about five thousand members, in which e-learning courses are conducted. In this method, all the training data are used in the construction of the model, with the difference that there is no need to label all the samples. In the proposed model, only 20% of the samples are labeled by an expert. 75% of the labeled data were used for training and 25% of them were used for testing the models.

To evaluate the effectiveness of using the proposed system in the held course, the criteria of "academic success" and "academic satisfaction" have been used. In each of the groups, at the end of the course, a comprehensive test of 40 questions was presented to the learners for evaluation. The purpose of this test is to measure the academic success of the students in the mentioned groups. This work was done in order to check the effect of the proposed groups in the system. To explore the effect of these groups, the one-way analysis of variance was used and the amount of difference between the results of the groups was checked. In order to check the learners' satisfaction with the methods used in the first, second and third groups, at the end of each session, a questionnaire containing four questions was presented to the learners. These questions have 5 options, option 1 indicates lack of satisfaction and option 5 indicates complete satisfaction with the course. The results show the success of the proposed method.

In the proposed method, the characteristics of the learners were investigated and suitable characteristics were created in order to predict the class variable which was the knowledge level of the learners. Then the research data was collected and solved using the proposed method. The results of the proposed method and the existing methods were compared with each other, and according to the evaluation criteria, the obtained results show that the proposed method is better. Finally, in order to evaluate, the presented model was used in a virtual course. The results of the course show the success and academic satisfaction of the students of the course with the proposed model.