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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.

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.

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.

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.

Purpose: Due to the importance of creating competitive advantages, the present study was conducted with a view to validating the pattern of digital marketing capabilities affecting the development of new Abadan petrochemical products. The present research is applied in terms of purpose and has been done with a survey method.Method: The type of research is quantitative. The data collection tool was a questionnaire with 50 questions. Confirmatory factor analysis was used for the validation of the questionnaire as well as Cronbach's alpha coefficient.Findings: Findings showed that the value of confirmatory factor analysis (t-value) for all 5 paths of the model is greater than 1.96 and the significance of the test is less than 0.05, so with a 95% confidence level causal factors affect the main category (marketing capabilities for new product development) by 0.705; The main category (marketing capabilities for new product development) has an impact on strategies of 0.379; Intervening factors affect strategies by 0.129; Underlying factors affect strategies by 0.457; Finally, strategies have an impact on outcomes of 0.849Conclusion: The results show that the innovation, customer orientation, marketing technologies improvement, research and development capabilities and communication capabilities are confirmed. Also they emphasized as causal dimensions and the basis of digital marketing. Finally, the board diversity is confirmed as the underlying dimensions and platform of digital marketing.

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

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.

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

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.

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.

Purpose: Start-up businesses have attracted considerable attention regarding the new approach in the modern economy The present study was conducted to investigate the factors affecting the improvement of customers’ online shopping experience in e-commerce start-ups in the healthcare sector.Method: This is an applied research conducted as a descriptive-survey. The target population includes all customers who used the electronic start-up services in the healthcare sector; out of which a sample of 384 individuals was selected using the Morgan table. A self-administered questionnaire was developed to collect the data which were analyzed using partial least squares and Smart-PLS software.Findings: All research hypotheses were confirmed, and it was proved that factors such as customer respect, customer enjoyment, importance of time, information security, convenience, perceived experience, valuable experience, and perceived image experience have a positive effect on improving customers’ online shopping experience in e-commerce start-ups.Conclusion: Applying the proposed model helps e-start-ups increase their performance   by eliminating their shortcomings and boosting their strengths.

Purpose: The purpose of this research was to provide a model for Validating and Presenting Brand-Customer Interaction Model in Digital Platform in the Cosmetics Industry.Method: This research was applied in its purpose and utilized a mixed approach (qualitative and quantitative). In this regard, this study was conducted with the aim of presenting and validating the brand-customer interaction model in Instagram Platform. The present study is a descriptive survey in terms of its practical-developmental purpose and data collection method. The statistical population in the qualitative section includes marketing professors and cosmetics industry managers, 20 of whom were selected by purposive sampling. The statistical population in the quantitative section also includes customers of cosmetics and health products, 384 participants were selected using the convenience sampling. The data collection tools were a semi-structured interview and a researcher-made questionnaire. First, thematic analysis method was used to analyze the expert interviews. Next, the identified pattern was validated using partial least squares method. Thematic analysis and partial least squares were done with MaxQDA software and Smart PLS software, respectively.Findings: The criterion to achieve data saturation has been to achieve repetition in extracting codes. 235 codes were identified in the open coding stage. Finally, three overarching themes, eight organizing themes, and 49 basic themes were obtained through axial coding. Based on the structural equation model, the proposed model was fitted and confirmed.Conclusion: Based on the results, effective marketing and digital content marketing are the basic elements of the model, which increase brand recognition and brand identity among customers by increasing interaction with customers. Brand recognition and identity contribute to positive word-of-mouth marketing, which in turn affects brand positioning on Instagram. Finally, in this way, it is possible to create a constructive and interactive brand-customer relationship.

Purpose: This study extends the current understanding of customer engagement by examining the impact of relationship bonding and relationship quality on customer engagement in value creation for online taxis.Method: A quantitative research design was used to validate the hypotheses proposed in this research. Customer engagement has become an important metric for measuring the quality of relationships between brands and their customers. Despite this, there has been limited research into how relationship bonds affect the effectiveness of building such a relationship in online taxi services. A survey was conducted using the convenience method among 600 users of online transportation services in the city of Urmia, Iran to test the theoretical model. Structural equation modelling in software Amos 23 were used in this study to test the research hypotheses. Findings: Findings showed that relationship bonding (financial, social, and structural) positively affects the quality of online relationships. Moreover, it was found that the quality of online relationships had a positive impact on all four aspects of customer engagement value (lifetime value, influence value, customer knowledge value, and customer referral value).Conclusion: In order to demonstrate the promotion of customer engagement through relationship bonds and online relationship quality, the study adds new data to the literature on online taxis services. Online taxi services are able to offer open innovation structures to help them   increase customer engagement, gather innovative ideas and integrate them into their operations. In this regard, in order to enrich ideas, customers who have successfully come up with new ideas should be rewarded.

Purpose: The purpose of this study was to prioritize the factors affecting consumer-buying behavior in online social media marketing.Method: The analysis and validation of indicators were conducted using the mixed method of structural equation modeling and partial least squares. Additionally, the IPMA matrix was utilized to determine the importance and type of performance of each factor. The research data were influenced by consumer information, predominantly consisting of women with a master's degree aged between 36 and 50 years. The study employed a non-probability sampling method common for online surveys, and 466 individuals were examined using Cohen's effect size formula.Findings: The research findings indicated that consumer engagement was the most important factor, and ease of use exhibited the highest level of performance in the overall social media model. Furthermore, consumer engagement was identified as the most important factor, while electronic word-of-mouth (EWOM) demonstrated the highest performance level on Instagram. Conversely, EWOM was deemed the most important factor, with consumer engagement displaying the highest performance level on Telegram. These results can be utilized by marketers to influence consumer purchasing behavior and craft online marketing strategies accordingly.Conclusion: Companies utilizing online social media platforms are advised to enhance consumer engagement and performance by implementing short-term techniques and effective strategies, such as incorporating online chat functionalities in the user environment. Instagram, being the most widely used social media platform and an online shopping hub for consumers, showcased acceptable performance in terms of consumer engagement. Hence, companies need to take measures to elevate performance levels to match their importance. In the case of Telegram, EWOM emerged as the most crucial factor among others, with a commendable performance rating. Businesses can bolster their Customer Relationship Management (CRM) units and enhance WOM initiatives to the fullest extent possible.

In terms of storage and consumption, blockchain technology is poised to transform the way we manage healthcare data. The primary goal is to empower individuals to take charge of their health records, allowing them to become independent of the institutions or organizations they use. Elec-tronic Health Records (EHRs) can be tracked in a novel and unique way through blockchain tech-nology and smart contracts. This technology can give patients more control over their data. Health practitioners and institutions, such as hospitals, may be granted access to patient data controlled by other organizations. This research highlights how blockchain technology can be used to manage EHRs while improving operational efficiency through process simplification and transparency. Additionally, the study proposes an architecture for managing and sharing healthcare data across enterprises. The suggested approach could significantly reduce the time required to transfer patient data among various health organizations while lowering overall costs.