درخت حوزه‌های تخصصی

This paper examines the effectiveness of implementing artificial intelligence (AI) in the Google Ads advertising service. The study analyzes the advantages and disadvantages of AI integration, focusing on attribution models and end-to-end analytics. The findings show that traditional metrics, such as CTR, CPC, and ROI, used to evaluate advertising campaign performance, exhibit significant statistical errors when AI tools are applied, with errors reaching up to 35%, exceeding typical business margins. A comparative analysis in the construction industry highlights discrepancies of 10% to 35% between traditional and AI-driven models. The study concludes that universal AI algorithms often fail to account for industry-specific dynamics, leading to inaccurate evaluations. The practical significance of this research lies in proposing an alternative approach that combines traditional evaluation methods with AI-based tools, offering a more reliable framework for assessing campaign effectiveness

Purpose: The main purpose of this research is to assess the feasibility of using VSAT satellites in the information services of university libraries.Method: The research method is a survey using the TOPSIS model, which indicates that the optimal method of providing the Internet is the method that has the greatest distance from negative factors and the least distance from positive factors. The opinions of user organizations, i.e. academic libraries and information centers, have been examined to clarify the necessity of using this method as well as its characteristics and advantages compared to other ways of providing the Internet.Findings: The findings show that VSAT satellite internet can have better conditions for providing services compared to other services such as ADSL, optical fiber, Wi-Fi and Wi-MAX. Also, their assessment determined that VSAT satellite internet is currently the best way to provide internet based on the criteria of service, support, cost, trust and quality, and ranks second in criteria such as security, confidentiality and service. In conclusion, the priority of solutions to provide library internet using TOPSIS analysis is: VSAT service; Optical fiber; ADSL service; Wi-Max services and wireless services.Conclusion: The results indicate that the VSAT satellite network, with advantages in the use of Internet services by libraries, plays an important role in improving the quality of these services.

Purpose: In the present research   the relationship between the dimensions of mental image, brand awareness and brand recognition in attracting customers, has been investigated and analyzed with   marketing of electronic services (the case study of Iran Postbank) especially taken into account. E-marketing has greatly facilitated the banking operation.Method: This research is applied in terms of purpose and descriptive-survey and correlational as regards the nature of data collection. The statistical population of the research includes managers and senior supervisors of Iran Postbank. The sample size of the research is 168 people, and simple sampling method was used, and 117 people were selected for the research. A questionnaire was used to collect data. The validity of the questionnaire was confirmed through content validity and its reliability using Cronbach's alpha coefficient. To analyze the data, structural equation modeling was used with the help of PLS ​​software.Findings: The results of this research showed that the variables of mental image, brand awareness and brand recognition have a significant and positive impact on the marketing of electronic services, also the marketing of services has a positive and significant impact on customer attraction and the marketing of electronic banking services will increase customer attraction.Conclusion: Given the increasing competition among Iranian banks and the challenge of attracting new customers and keeping current customers, as discussed in this research, brand awareness is one of the most important factors affecting customer attraction in the bank.

This paper sought to examine the impact of perceived Social Media Marketing Activities (SMMAs) on customer purchase intention via brand awareness in an online context. An online questionnaire was used to collect data from 188 samples. The data were analyzed using the structural equation modeling approach, and the research hypotheses were examined using SEM. The study measured SMMAs through personalization, customer community, and live video. The results revealed that SMMAs were insignificant towards brand awareness and purchase intention. The result also stated that brand awareness does not mediate the relationship between SMMA and purchase intention. However, brand awareness was found to affect purchase intention positively. The current study introduces the stimulus–organism–response model as a theoretical support to examine SMMAs of e-commerce to customers' purchase intention via brand awareness.

This paper aims to conceptualize the success factors of a digital transformation (DT) strategy and analyze its impact on a company's economic performance. We explore the concepts that affect the field of DT definition and the key drivers that lead to successful DT. Through these key drivers considered as success factors, we propose a research framework linking these drivers to the DT strategy and then corporate economic performance in emerging markets. To test the research model empirically and provide a contextualized interpretation of the results, we adopted a sequential explanatory design. Initially, we performed a quantitative study through a survey among companies listed on the Casablanca Stock Exchange in Morocco. We then analyzed the collected data using the structural equation method. Next, to explain the results, we conducted a qualitative analysis through interviews with semi-structured questions. The findings show that in an emerging economy context such as Morocco, placing the customer at the core of the DT strategy, aligning the organization with the DT strategy, adopting a value system imbued with DT values, and establishing an operational roadmap to drive the change can enhance the company’s digital transformation. These drivers contribute to 59.5% of the implementation of the DT strategy. Driving a DT strategy has a significant impact on companies' economic performance, contributing to 21.5% of their commercial and financial outcomes. This study highlights that the maintenance of a "phygital" business model, which mixes digital and physical business models, and the lack of human resources involvement in the DT process are specific to the emerging market context studied.

This paper provides a systematic review of the literature on big data capabilities, business model innovation, firm performance, and environmental uncertainty. It aims to establish a foundation for theoretical modeling, research proposition refinement, and the overall research framework by meticulously examining the theoretical backgrounds of existing studies and identifying research gaps. An initial search yielded 1,360 articles, which were filtered to remove duplicates and irrelevant studies, resulting in 475 articles for final analysis. These articles were classified into three main categories: the relationship between big data capabilities and business model innovation, the impact of business model innovation on firm performance, and the integrated relationship involving environmental uncertainty. Additionally, it examines the mediating role of business model innovation on firm performance as well as the moderating effect of environmental uncertainty on these relationships. Finally, the paper formulates research hypotheses and discusses identified research gaps, establishing a solid groundwork for methodological discussions in future research and contributing to the advancement of knowledge in the field.

This paper gives a performance analysis of multiple vote classifiers based on meta-classification methods for estimating the risk of diabetes. The study's dataset includes a number of biological and clinical risk variables that can result in the development of diabetes. In the analysis, classifiers like Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks were used. In the study, each classifier was trained and evaluated separately, and the outcomes were compared to those attained using meta-classification methods. Some of the meta-classifiers used in the analysis included Majority Voting, Weighted Majority Voting, and Stacking. The effectiveness of each classifier was evaluated using a number of measures, including accuracy, precision, recall, F1-score, and Area under the Curve (AUC). The results show that meta-classification techniques often outperform solo classifiers in terms of prediction precision. Random Forest and Gradient Boosting, two different classifiers, had the highest accuracy, while Logistic Regression performed the worst. The best performing meta-classifier was stacking, which achieved an accuracy of 84.25%. Weighted Majority Voting came in second (83.86%) and Majority Voting came in third (82.95%).

Cardiovascular-Diseases (CVD) are a principal cause of death worldwide. According to the World-Health-Organization (WHO), cardiovascular illnesses kill 20 million people annually. Predictions of heart-disease can save lives or take them, depending on how precise they are. The virus has rendered conventional methods of disease anticipation ineffective. Therefore, a unified system for accurate illness prediction is required. The study of disease diagnosis and identification has reached new heights thanks to artificial intelligence. With the right kind of training and testing, deep learning has quickly become one of the most cutting-edge, reliable, and sustaining technologies in the field of medicine. Using the University of California Irvine (UCI) machine-learning (ML) heart disease dataset, we propose a Convolutional-Neural-Network (CNN) for early disease prediction. There are 14 primary characteristics of the dataset that are being analyzed here. Accuracy and confusion matrix are utilized to verify several encouraging outcomes. Irrelevant features in the dataset are eliminated utilizing Isolation Forest, and the data is also standardized to enhance accuracy. Accuracy of 98% was achieved by employing a deep learning technique.

Purpose: Economic and social status play a prominent role in many human activities and their  function is accentuated in the theory of the knowledge gap. According to the idea, the knowledge of the people with higher socio-economic status increases compared to those with lower socio-economic status. The purpose of this study was to determine the role of socio-economic status (based on knowledge gap theory) in the information-seeking behavior of fellow members of staff at  Qom University.Method: The study was an applied research in terms of purpose and in terms of strategy and data collection was correlational. The population consisted of 761 university employees. Based on Cochran’s formula the sample of the study included 255 employees. A researcher-made questionnaire was used to collect data. Spearman and X2 statistical tests were applied to analyze data.Findings: People who have a higher socio-economic status (with higher employment, income and education levels) are more motivated to search and obtain information, and there is a significant relationship between the components of individuals' socio-economic status and the type of the used information resources. Socio-economic status affects the criteria for evaluating information resources, and people with higher rate use various evaluation criteria while assessing the information. People with socio-economic status use various and different channels to obtain information, thus, there is a positive and significant relationship between the use of search engines and meta-search engines, internal and external databases, conference papers, library RSS, specialized social networks, consultation with librarians and technical blogs, and their socio-economic status.Conclusion: The social and economic status explains and predicts the information-seeking behavior of the staff and the results confirmed the theory of knowledge gap. Prediction of the facilities required for searching and seeking information in organizations and making them accessible to all human resources can help provide fair access to information for the better part of society and reduce the knowledge gap.

This study aims to develop an adoption model tailored for service-oriented organizations and then evaluate its effectiveness within the specific context of Tehran Municipality, Iran's foremost service-oriented institution. Utilizing a mixed-method research approach integrating qualitative and quantitative methodologies, this study delineated the dimensions, categories, and indicators pertinent to the adoption of Electronic Human Resource Management (EHRM) systems in service-oriented organizations. Qualitative methodologies were employed to identify and develop the adoption model, which was subsequently evaluated within Tehran Municipality using a quantitative approach. In the qualitative segment of this study, in-depth interviews were conducted using a snowball sampling technique until theoretical saturation was achieved. For the quantitative phase, a sample of 310 experts affiliated with Tehran Municipality's EHRM system was surveyed. Structural equation modeling and Smart PLS 4.0 software were employed for data analysis. Ultimately, this research extracted five dimensions, 14 categories, and 94 indicators for the proposed adoption model. Notably, experts accorded the highest priority to the technological dimension in the adoption model, with specific emphasis on “adaptive architecture, security and privacy of employees, trialability and reliability, organizational citizenship behavior, organizational dynamic capabilities, digital Leadership Policy and Actions, cloud computing, etc…”, as pivotal factors in EHRM adoption. The organizational dimension assumed the second-highest priority, while the individual dimension was assigned a third-place ranking. Micro and macro-environmental factors followed in subsequent priority order.

Leveraging supplementary technology such as Blockchain has the potential to alter the stakeholders involved in a system. Paying attention to stakeholders is one of the main pillars of developing a system. Evidence has shown that Blockchain can solve existing challenges and add new capabilities. These actions will change the stakeholders of PHR. If a value is different for everyone, at the first stage, stakeholders should be identified, and that is our goal in this study. The research adhered to the guidelines outlined in the PRISMA statement. To this end, the study utilized databases including MEDLINE, ScienceDirect, and Google Scholar for English language articles, while the "iranjournals.nlai.ir" database was accessed for Persian language articles. Finally, 35 articles were chosen from searching databases, and six extra articles were selected from reviewing the final articles' references. Stakeholders were categorized into 15 groups. The patient (individual) was identified as the most frequent stakeholder (41 times), and infrastructure providers and the token exchange market were mentioned once each. The usage type is categorized into four groups: direct user interaction, data user, impact user, and financial beneficiaries, comprising six, eight, four, and four stakeholders, respectively. Patients (individuals) use the four groups, and health care providers, policymakers, hospitals, and the government each use two groups. Intelligent contracts are neglected in PHR, which can significantly impact the motivation and creation of incentives for using different stakeholders. The grouping presented here can be used in the preparation of the business model of PHR based on Blockchain. Data has the most usage for stakeholders and strengthens and supports investments in technologies such as Blockchain as an infrastructure for creating data markets, new business models, and creating value.

This article identifies the factors for managing the behavioral resilience of a firm in the face of exogenous shocks in the economy. Its main hypothesis is that each enterprise has certain resilience competencies that need to be strengthened and developed in the future. The paper identifies 17 key competencies that determine the behavioral resilience of an enterprise. Using the method of factor analysis, a model of behavioral resilience is built, which is used to support management decision-making.  The factor model of behavioral resilience SELF&IRR includes 7 competencies: S – Speed of response to processes and events; E – Endurance; L – Leadership; F – Flexibility; I – Innovation, ideas, ingenuity; R – Responsibility; R – Resource capabilities. This model can be used to determine the level of behavioral resilience, based on which a decision is made on the choice of the enterprise's strategy. Depending on the level of behavioral resilience, the management staff decides on the choice of a certain strategy (systemic transformation; structural transformation; local changes in the firm's competencies; adaptation of competencies to changes), which is aimed at strengthening the firm's viability and development. The successful execution of a chosen strategy enhances the firm's capacity to withstand current and future threats while actively seeking or purposefully creating new opportunities for development.

Predicting the development of cancer has always been a serious challenge for scientists and medical professionals. The prompt identification and prognosis of a disease is greatly aided by early-stage detection. Researchers have proposed a number of different strategies for early cancer detection. The purpose of this research is to use meta-learning techniques and several different kinds of convolutional-neural-networks(CNN) to create a model that can accurately and quickly categorize breast cancer(BC). There are many different kinds of breast lesions represented in the Breast Ultrasound Images (BUSI) dataset. It is essential for the early diagnosis and treatment of BC to determine if these tumors are benign or malignant. Several cutting-edge methods were included in this study to create the proposed model. These methods included meta-learning ensemble methodology, transfer-learning, and data-augmentation. With the help of meta-learning, the model will be able to swiftly learn from novel data sets. The feature extraction capability of the model can be improved with the help of pre-trained models through a process called transfer learning. In order to have a larger and more varied dataset, we will use data augmentation techniques to produce new training images. The classification accuracy of the model can be enhanced by using meta-ensemble learning techniques to aggregate the results of several CNNs. Ensemble-learning(EL) will be utilized to aggregate the results of various CNN, and a meta-learning strategy will be applied to optimize the learning process. The evaluation results further demonstrate the model's efficacy and precision. Finally, the suggested model's accuracy, precision, recall, and F1-score will be contrasted to those of conventional methods and other current systems.

Cancer is an abnormal cell growth that occurs uncontrollably within the human body and has the potential to spread to other organs. One of the primary causes of mortality and morbidity for people is cancer, particularly lung cancer. Lung cancer is one of the non-communicable diseases (NCDs), causing 71% of all deaths globally, and is the second most common cancer diagnosed worldwide. The effectiveness of treatment and the survival rate of cancer patients can be significantly increased by early and exact cancer detection. An important factor in specifying the type of cancer is the histopathological diagnosis. In this study, we present a Simple Convolutional Neural Network (CNN) and EfficientNetB3 architecture that is both straightforward and efficient for accurately classifying lung cancer from medical images. EfficientnetB3 emerged as the best-performing classifier, acquiring a trustworthy level of precision, recall, and F1 score, with a remarkable accuracy of 100%, and superior performance demonstrates EfficientnetB3’s better capacity for an accurate lung cancer detection system. Nonetheless, the accuracy ratings of 85% obtained by Simple CNN also demonstrated useful categorization. CNN models had significantly lower accuracy scores than the EfficientnetB3 model, but these determinations indicate how acceptable the classifiers are for lung cancer detection. The novelty of our research is that less work is done on histopathological images. However, the accuracy of the previous work is not very high. In this research, our model outperformed the previous result. The results are advantageous for developing systems that effectively detect lung cancer and provide crucial information about the classifier’s efficiency.

The study evaluated the opportunities and challenges of e-learning for university students and investigated their experiences. A sequential exploratory mixed-methods approach (quantitative and qualitative) was used. In the quantitative phase, a survey was conducted to explore students' competencies in coping with e-learning attributes, involving 237 university students (46.9% male, 53.1% female). Descriptive and analytical tests were used to analyze the data. The results indicated the mean scores of students' perspectives on the opportunities and challenges of e-learning in university were 4.05 ± 0.49 out of 5. In the qualitative phase, data were collected through semi-structured interviews. To provide a richer context and better understanding and interpretation of the quantitative findings, the current research employed qualitative research methodologies, including focus group discussions with ten interviewees—five academic staff members and five students. Combining both student and academic staff perspectives provides a more comprehensive understanding of the research topic. Students and staff may have different viewpoints, experiences, and needs related to the subject matter. The qualitative analysis identified five significant themes: communication defects, technical challenges, personal-level challenges, curricular-level issues, and social challenges. The study's findings may be utilized to design better policies and strategies to enhance e-learning and address its issues among both instructors and students. Finally, the study provides implications for relevant stakeholders

Green information technology is in the spotlight for organizations, helping them save money by using information technology (IT) to achieve the highest efficiency and thus reduce environmental impacts. One of the ways that can help organizations planning for deploying green IT is to evaluate green information technology maturity (GITM). Previous studies have referred to various criteria for green IT evaluation, most of which are qualitative criteria that are difficult to measure and evaluate in ambiguous conditions. The main objective of this study is to identify crucial criteria that affect the GITM level and to design a fuzzy inference system to assess the GITM level in any organization. While using a Mamdani Inference system, inputs can be verbal expressions or crisp values, and the output shows the level of maturity of green information technology. Since green IT knowledge is not modeled in previous studies, modeling it in the current study is a valuable step for organizations confused about various factors they should consider for going green. The main system criteria are the conditions of the data center, office environment, work practice, procurement, and corporate citizenship. Due to the generality of the model used for the knowledge base system development, organizations can use this system for the green IT maturity level determination. The presented inference system helps organizations understand their status of being IT green and plan for the following steps to accomplish their desired maturity level. The proposed inference system has been tested, validated, and used to determine the maturity level of Tehran municipality.

Purpose: This research aimed to identify and evaluate the risks of content production in the governmental digital libraries of Tehran.Method: In terms of essence, this research is synthetic (library studies, qualitative and quantitative), and regarding purpose, it is an applied one.  In the first part, using the studies of the research literature, a set of indicators related to the risks of content production in the governmental libraries of Tehran was obtained. The second part of this research involved a fuzzy Delphi approach that was conducted at two stages among 20 experts. In this stage, a researcher-made questionnaire based on the indicators obtained from the first stage was used. The third part of the research was a survey-analysis conducted with a quantitative method using a questionnaire made out based on the results of qualitative stage with three criteria: the probability of occurrence, effect intensity, and the inability of organization to respond and was distributed among 100 managers and experts of governmental digital libraries in Tehran.Findings: In this research, the risks of content production were identified and fell into nine major categories (human force, environmental factors, infrastructure, protection and maintenance,  creators`  technical rights, integration, content evaluation of sources and authors and information security).Conclusion: The results of this evaluation show that digital libraries are not exactly at their best level when it comes to their responses to the risk of human resources, authors' rights, and integration risks.