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

Impact of Library and Information Science Master\u27s degree (MLIS) on the Graduates: A case study

Objective: The study aimed to examine the effect of MLIS degree on graduates in Iran from different dimensions. The study examined the effects of MLIS on scientific progress, the development of subject expertise, employment, individual characteristics, skills and capabilities, and scientific activities of Iran\u27s graduates. Method: The study was a descriptive-survey and researcher-made questionnaire is used for data collection. The population included all graduates MLIS in Iran that their information was available in, Iranian Library and Information Studies Alumni Database . 212 persons are selected randomly out of 547. Data has been analyzed by SPSS software on both levels of descriptive statistics (frequency tables and graphs of relevant data) and inferential statistics (including single-sample t test to examine the hypothesis, ANOVA test to check differences between variables, Tukey test to compare paired variables, and Pearson correlation test to assess the relationship between two variables). Results: The overall effect average of degree of MLIS graduates was equal to 3/25. The findings showed that the average effect of MLIS degree associated with each studied factors on the graduates in the country were: Scientific progress (3/13), development of subject expertise (3/27), employment (3/27), individual characteristics (2/75), skills and capabilities (3/48), scientific activities (3/57). Discussion: The effect of MLIS degree on Iranian graduates was more than moderate. Generally, it can be concluded that MLIS courses at universities in the country, can increase the value of a master\u27s degree of graduates at an acceptable level, but is not perfect; it seems that the authorities should increase their efforts to promote the value of a master\u27s degree in graduates

A Novel Combined Investment Recommender System Using Adaptive Neuro-Fuzzy Inference System [védés előtt]

Investment recommendation systems (IRSs) are critical tools used by potential investors to make informed decisions about investment options. However, existing systems have limitations in terms of accuracy and efficiency, leading to a need for more effective and efficient recommendation systems. This dissertation proposes the use of an adaptive neuro-fuzzy inference system (ANFIS) to develop a combined IRS that can provide accurate and efficient investment recommendations for potential investors. The main research question for this study is "How can an ANFIS be utilized to propose an effective and efficient investment recommendation system?" The specific sub-goals of the study are: 1) to categorize and cluster potential investors based on available data to make accurate investment recommendations, 2) to offer customized investment-type services using adaptive neural-fuzzy inference solutions for different categories of potential investors, and 3) to propose a combined recommender system to provide appropriate investment type recommendations for all categorized and clustered potential investors. The dissertation is structured into five chapters. Chapter I provides an overview of the research question and objectives, and Chapter II presents a theoretical framework and literature review, covering existing research on ANFIS in investment recommendation systems. Chapter III explains the methodology used to develop the combined IRS using ANFIS, including data collection, categorization and clustering of potential investors, development of the combined ANFIS model, and evaluation of the proposed system. Chapter IV presents the experimental results and analysis, highlighting the effectiveness of the model in providing appropriate investment-type recommendations for categorized and clustered potential investors. This chapter describes seven experiments that focused on investment recommender systems. Each experiment proposed a unique system that utilized various features of potential investors and their investment type experiences, in addition to employing fuzzy neural inference and the K-Means technique to generate personalized investment recommendations. The first experiment proposed a demographic ANFIS that utilized customer feedback and fuzzy neural inference to generate personalized investment recommendations. The second experiment proposed an automatic recommender system that worked with four key decision factors (KDFs) of potential investors: system value, environmental awareness, high return expectation, and low return expectation. The third experiment used potential investors' financial management traits and investment type for the recommendation. The model was based on an ANFIS, and feedback from knowledge experts and investors was used to improve the system. The fourth experiment used potential investors' experiences data to predict investment outcomes, and the system's performance was evaluated by comparing its recommendations with actual investment outcomes. The fifth experiment proposed an ANFIS-based investment recommendation system based on customers' financial situations, risk tolerance, and investment goals. The sixth experiment investigated the impact of personal characteristics such as age, income, and education level, as well as managerial issues, on investment decisions. The seventh experiment combined and clustered data from the six previous ANFIS systems to provide accurate investment recommendations. The system utilized clustering techniques to group customers with similar financial situations and investment goals, thereby enhancing the personalization of the recommendations. Overall, these experiments propose a novel approach to developing an effective and efficient investment recommendation system using ANFIS. The proposed system has the potential to significantly improve the accuracy and efficiency of investment recommendations, thereby enhancing the decision-making process for potential investors. A comparison of the results with other existing methods and a discussion of the limitations and challenges faced during the development of the system are also included in this chapter. Finally, Chapter V provides a comprehensive discussion of the research findings and their implications, including suggestions for future research. Overall, this dissertation proposes a novel approach to developing an effective and efficient investment recommendation system using ANFIS. The proposed system has the potential to significantly improve the accuracy and efficiency of investment recommendations, thereby enhancing the decision-making process for potential investors

Benefits and Challenges of Pervasive and Mobile Computing in Healthcare”

This letter has been written to express my views on the increasing use of pervasive and mobile computing in health information systems. Pervasive and mobile computing has become increasingly important in the field of healthcare over the past decade. The use of mobile devices, wireless communication, and cloud computing has revolutionized the way healthcare is delivered, making it more efficient and accessible to patients as well as healthcare providers.Pervasive computing refers to the integration of computing technology into daily life. This includes the use of smart devices, such as smartphones, tablets, and wearable technology, to connect individuals to the Internet and to other devices. Mobile computing, on the other hand, refers specifically to the use of mobile devices to access information and communication. Both pervasive and mobile computing have the potential to transform the way healthcare is delivered, making it more patient-centered, personalized, and accessible.One of the most significant applications of pervasive and mobile computing in healthcare is the use of mobile health apps like MHealth. These apps can be used by patients to monitor their health, track their medication, and communicate with healthcare providers. For example, a patient with diabetes can use an app to track their blood sugar levels and send the data to their doctor in real time, allowing for quick adjustments to treatment plans. These apps could also be used to provide patients with education and support, helping them to manage their condition more effectively Another important application of pervasive and mobile computing in healthcare is the use of telemedicine. Telemedicine allows healthcare providers to communicate with patients remotely, using video conferencing and other technology tools. This can be particularly beneficial for patients who live in rural areas or who have mobility issues, as it allows them to access healthcare services without having to travel long distances. Telemedicine can also be used to provide patients with access to specialists who may not be available locally.In addition to healthcare apps and telemedicine, pervasive and mobile computing can be used to improve the efficiency of healthcare delivery. For example, electronic health records (EHRs) can be accessed from mobile devices, allowing healthcare providers to access patient information anywhere. This can be particularly useful in emergency situations, where quick access to patient data can be critical.Despite the benefits of pervasive and mobile computing in healthcare, there are challenges to their implementation. One of the biggest challenges is ensuring the security and privacy of patient data. Healthcare data is highly sensitive, and the use of mobile devices and cloud computing can increase the risk of data breaches. It seems indispensable that healthcare providers take steps to ensure the security of patient data, including the use of encryption and other security measures.Another challenge is ensuring that patients have access to the technology they need to benefit from pervasive and mobile computing. While smartphones and other mobile devices are becoming increasingly widespread, not all patients may have access to these technologies. Healthcare providers may need to provide patients with devices or other support to ensure that they are able to use related apps and other technology tools.In conclusion, the use of pervasive and mobile computing in healthcare has the potential to revolutionize the way healthcare is delivered, making it more patient-centered, personalized, and accessible. From healthcare apps to telemedicine, these technologies can improve the efficiency of healthcare delivery while providing patients with better access to care. However, there are also challenges to their implementation, including the need to ensure the security and privacy of patient data and to provide patients with access to the necessary technology. By addressing these challenges, healthcare providers can harness the power of pervasive and mobile computing to improve the health and well-being of their patients

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

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