Artificial Intelligence(AI) application in Library Systems in Iran: A taxonomy study

With introducing and developing AI logic, this science as a branch of computer science could impact and improve all sciences which used computer systems. LIS also could get benefit from AI in many areas. This paper survey applications of AI in library and information science and introduce the potential of library system to apply AI techniques. Intelligent systems have contributed for many librarian purposes like cataloging, indexing, information retrieval, reference, and other purposes. We applied Exploratory Factor Analysis (EFA) as a primer method for identification of the most applicable AI techniques categories in LIS. ESs are the most usable intelligent system in LIS which mimic librarian expert’s behaviors to support decision and management. AI also can utilize in many areas such as speech recognition, machine translation and librarian robots. In this study four criteria for the application of AI in the library systems in Iran was considered and it is determined in three area included public services, technical services, and management services. Then, degree of development these services was studied using taxonomy method. The results showed that most developed Recommender Systems (RM) in library systems in Iran and Natural Language Processing (NLP) is the most undeveloped criterion

A judgment-based model for usability evaluating of interactive systems using fuzzy Multi Factors Evaluation (MFE)

The study aimed to propose a judgment-based evaluation model for usability evaluating of interactive systems. Human judgment is associated with uncertainty and gray information. We used the fuzzy technique for integration, summarization, and distance calculation of quality value judgment. The proposed model is an integrated fuzzy Multi Factors Evaluation (MFE) model based on experts’ judgments in HCI, ISPD, and AMLMs. We provided a Fuzzy Inference System (FIS) for scoring usability evaluation metrics in different interactive systems. A multi-model interactive system is implemented for experimental testing of the model. The achieved results from the proposed model and experimental tests are compared using statistical correlation tests. The results show the ability of the proposed model for usability evaluation of interactive systems without the need for conducting empirical tests. It is concluded that applying a dataset in a neuro-FIS and training system cause to produce more than a hundred effective rules. The findings indicate that the proposed model can be applied for interactive system evaluation, informative evaluation, and complex empirical tests. Future studies may improve the FIS with the integration of artificial neural networks

Designing a model to measure information intelligence based on the Indices and measures

Measuring information intelligence is especially important in information societies. The present study aimed to identify indices and measures effective and design a model for measuring information intelligence. The research used a mixed method to achieve objectives. Data collection tools included the study of scientific literature and interviews. The research population included related published literature related to scientific theories, information science experts, and psychology experts. Based on the research findings, 14 indices and 97 measures were identified as effective for measuring information intelligence in two dimensions of management and information retrieval. After weighing and prioritizing the indices, finally, a model is presented for measuring information intelligence. From the findings, it is concluded that in the information society, officials should first determine their information strategies and then address the strategic goals of the information society. One of these strategic goals can be to increase the level of information intelligence of individuals, organizations, and society. To do this, effective indices must be identified for measuring and strategies to increase the level of information intelligence of the target community. It is suggested that this be one of the priorities of an information society

Unveiling the impact of managerial traits on investor decision prediction

Investment decisions are influenced by various factors, including personal characteristics and managerial issues. In this research, we aimed to investigate the impact of managerial traits on investment decisions by using adaptive neuro-fuzzy inference system (ANFIS) to develop a personalized investment recommendation system. We collected data from potential investors through a survey, which included questions on investment-types, investment habits, and managerial traits. The survey data were used to create an ANFIS model, which is a hybrid model that combines the strengths of both artificial neural networks and fuzzy logic systems. The ANFIS model was trained using 1542 survey data pairs, and the model's performance was evaluated using a validation set. The results of the ANFIS model showed that the model had a minimal training root mean square error of 0.837341. The ANFIS model was able to effectively capture the relationship between managerial traits and investment decisions and was able to make personalized investment recommendations based on the input data. The results of this research provide valuable insights into the impact of managerial traits on investment decisions and demonstrate the potential of ANFIS in developing personalized investment recommendation systems. In conclusion, this research aimed to investigate the impact of managerial traits on investment decisions using ANFIS. The results of this study demonstrate the potential of ANFIS to personalize investment recommendations based on the input data. This research can be used as a foundation for future research in the field of investment recommendations and can be helpful to investors to take their decision-making

The Competitive Situation of the Cheminformatics Industry Based on Porter’s Model in Iran

The purpose of this study was to analyze the competitive situation of the cheminformatics industry using Porter’s competitive model and to determine the priority and weight of each competitive force in this industry. In addition to qualitative analysis of data collected from library surveys and the Delphi method, multicriteria decision-making techniques (MCDM) were used to determine the rank and weight of forces (criteria). A preference judgment questionnaire was used to collect data. This researcher-made questionnaire was sent to cheminformatics specialists in Iran. Using the process of hierarchical analysis (AHP), Porter's competitive forces in this industry were investigated. The criteria, subcriteria, alternatives, and relation between them were drawn using the analytical decision tree model. Then, the priority and weight of each force were calculated. Then, the effect of each force on each other was investigated. The results showed that the decision-making priorities of cheminformatics industry managers in the competitive market concerning the management of competitive forces of the Porter model are as follows: (1) competitive rivalry condition between current competitors, (2) the threat of the entry of alternative products (the threat of substitutes), (3) the threat of new entrants (potential competitors), (4) the bargaining power of customers, and (5) the bargaining power of suppliers. We concluded that due to the prevailing economic conditions, companies active in the field of cheminformatics in the present study, to ensure profitability, should prioritize the competitive situation between competitors and consider this priority in strategic planning. Finally, we recommend that the present study be repeated in other countries and companies active in this industry

An integrated model for evaluation of big data challenges and analytical methods in recommender systems

The study aimed to present an integrated model for evaluation of big data (BD) challenges and analytical methods in recommender systems (RSs). The proposed model used fuzzy multi-criteria decision making (MCDM) which is a human judgment-based method for weighting of RSs’ properties. Human judgment is associated with uncertainty and gray information. We used fuzzy techniques to integrate, summarize, and calculate quality value judgment distances. Then, two fuzzy inference systems (FIS) are implemented for scoring BD challenges and data analytical methods in diferent RSs. In experimental testing of the proposed model, A correlation coefcient (CC) analysis is conducted to test the relationship between a BD challenge evaluation for a collaborative fltering-based RS and the results of fuzzy inference systems. The result shows the ability of the proposed model to evaluate the BD properties in RSs. Future studies may improve FIS by providing rules for evaluating BD tools

Towards Standard Information Privacy, Innovations of the new General Data Protection Regulation

Protection of personal data in recent decades became more crucial affecting by emergence of the new technologies especially computer, internet, information and communications technology. However, Europeans felt this necessity at time and provided for up-to-date and supportive laws. The General Data Protection Regulation (GDPR) is the latest legislation in EU to protect personal data of individuals based on the recent technological advancements. However, its’ domestic and international output still is debatable. This doctrinal legal study by using descriptive methods, aimed to evaluate the GDPR through analyzing and interpreting its’ provisions by especial focus on its’ innovations. The results show that the GDPR is much developed in comparison with previous personal data protection documents and will be a referred reference for the rest of the world in the near future

A systematic review of modalities in computer-based interventions (CBIs) for language comprehension and decoding skills of children with autism spectrum disorder

This paper presents a systematic review of the literature on the modalities used in computer-based interventions (CBIs)and the impact of using these interventions in the learning, generalization, and maintenance of language comprehension and decoding skill for children with autism spectrum disorder (ASI), ending with appraisal of the certainty of evidence. Despite the importance of both skills in the reading comprehension and overall learning a limited number of studies have been found. These include seven studies in language comprehension and seven studies on decoding. The shortlisted studies were analysed and a very limited number of modalities were found to have been used; text, graphics, audio, video, and mouse movements are used in all the studies and are termed basic modalities. Statistical analysis was also conducted on three parameters: (1) outcome of the study; (2) generalization; and (3) maintenence. The analysis showed that CBIs were effective in facilitating these children's learning; there was a significant improvement in the performance of children from the baseline to during and the post-intervention period. The analysis of generalization has revealed positive results are also noted from the analysis of maintenance, which indicate that the children retained information following the withdrawal of intervention. The combination of teachers's instruction and CBI has provided better results than using either of them separately. This study has discovered 23 potential modalities and 2 potential CBIs including serious games and virtual learning environments that can be explored for language comprehension and decoding skills