Abstract
This study investigates the impact of innovation and digitalization on the quality of higher education at Tashkent State Technical University (TSTU) in Uzbekistan. Using a mixed-methods approach, data were collected through a well-structured questionnaire from 300 participants, including students, faculty members, and administrative staff. The results demonstrate a strong positive perception among respondents regarding the influence of digital technologies on educational quality, with 83% acknowledging their positive impact. In addition, 72% reported having access to beneficial digital resources, while 68% perceived TSTU as an innovative institution. The study concludes that digital technologies play a significant role in enhancing the quality of higher education at TSTU, promoting a dynamic learning environment and improved student engagement.
1 Introduction
Innovation and digitalization have been important drivers of change in higher education worldwide, and Uzbekistan is no exception. With a growing demand for high-quality education and the increasing importance of technology, higher education institutions in Uzbekistan are under pressure to embrace innovation and digitalization to improve the quality of education. However, the impact of these changes on the quality of education in Uzbekistan is not yet fully understood. Therefore, this study aims to investigate the impact of innovation and digitalization on the quality of higher education in Uzbekistan [1].
The importance of this study lies in the fact that it will provide insights into the effectiveness of innovation and digitalization in improving the quality of higher education in Uzbekistan. The findings of this study can be useful for policymakers, university administrators, and educators who are interested in adopting or improving innovation and digital technologies in higher education. In addition, this study will contribute to the research gap on the influence of innovation and digitalization on the quality of higher education in Uzbekistan.
According to Sharma and Taneja [2], innovation and digitalization have the potential to improve the quality of higher education by increasing access to education, enhancing learning outcomes, and providing new opportunities for innovation in teaching and learning. However, the effectiveness of these changes in the Uzbekistan context is not yet clear. In addition, a study by Mijid and Turaev [3] found that there are challenges in implementing digital technologies in higher education in Uzbekistan, including inadequate infrastructure, a lack of funding, and limited faculty and student readiness.
Moreover, digital transformation and innovation have become critical components of higher education in Uzbekistan, especially after the COVID-19 pandemic [4,5]. According to the UNESCO Institute for Statistics, only 13% of the Uzbekistan population had access to the Internet in 2010. However, this number had risen to 47% by 2020, indicating the country’s growing reliance on digital technologies [6]. The Ministry of Higher and Secondary Specialized Education of Uzbekistan has recognized the importance of digitalization in education and has implemented several initiatives to promote e-learning and digital transformation in higher education institutions [7].
Despite these efforts, there are still challenges in implementing digital technologies in higher education in Uzbekistan. For instance, a survey conducted by the Uzbekistan Research and Education Network found that only 22% of respondents believed that their universities were well prepared for online education. In addition, a study by Olimova and Arifdjanova [8] found that university students in Uzbekistan face challenges with online learning, including poor internet connectivity and a lack of necessary equipment.
Recent research has highlighted the increasing importance of digital technologies in higher education, with scholars arguing that these tools can enhance the quality of education and provide students with new opportunities for learning (Balbaa et al. [9]). At the same time, some studies have questioned the effectiveness of digital technologies in improving educational outcomes and raised concerns about potential negative effects, such as decreased engagement and increased distraction [10]. Given these divergent perspectives, there is a need for further research to explore the impact of innovation and digitalization on the quality of higher education in specific contexts, such as universities in Uzbekistan.
In recent years, the impact of innovation and digitalization on the quality of higher education has garnered increasing attention. This study focuses on selected universities in Uzbekistan, with a primary emphasis on Tashkent State Technical University (TSTU), to investigate the transformative potential of digital technologies in the realm of education. The main contributions of this research are as follows:
Identification of Key Challenges: We delve into the existing challenges faced by higher education institutions in Uzbekistan concerning access to quality educational resources, student engagement, and overall learning experiences.
Innovative Algorithms and Approaches: To address the identified challenges, we propose novel algorithms and approaches that leverage digital technologies to enhance teaching and learning processes.
Enhanced Learning Outcomes: Through the implementation of these algorithms and approaches, we explore their impact on students’ learning outcomes, knowledge acquisition, and skill development.
Empirical Survey and Data Analysis: To substantiate our findings, we conducted an extensive survey among students, faculty members, and administrative staff at TSTU [11]. The collected data were then rigorously analyzed to derive meaningful insights.
Implications for Higher Education: By showcasing the potential benefits of digitalization and innovation in higher education, our study contributes to the ongoing discourse on transforming educational practices in Uzbekistan.
Recommendations for Policy and Practice: Based on our research findings, we provide practical recommendations and guidelines for policymakers and educators to effectively integrate digital technologies and foster a culture of innovation in higher education institutions.
By investigating the impact of innovation and digitalization in the context of higher education, this research aims to shed light on the potential pathways for educational institutions in Uzbekistan to adapt and thrive in the ever-evolving landscape of digital learning.
This study aims to address these challenges and investigate the influence of digitalization and innovation on the quality of higher education in Uzbekistan. By examining the current state of innovation and digitalization in higher education and its impact on quality, this study will provide evidence-based insights for policymakers, university administrators, and educators to make informed decisions about adopting or improving innovation and digital technologies in higher education in Uzbekistan.
The remainder of this article is structured as follows: in Section 2, we review the relevant literature on innovation and digitalization in higher education, providing the theoretical foundation for our research. Section 3 details the research methodology, including the survey design, data collection process, and data analysis techniques employed. The findings of our empirical study are presented in Section 4, followed by a comprehensive discussion of the results in Section 5. Section 6 highlights the practical implications of our research and offers recommendations for educational institutions and policymakers. Finally, in Section 7, we conclude the article by summarizing the main insights, outlining the study’s limitations, and suggesting potential avenues for future research.
2 Literature review
The use of innovation and digitalization in higher education has become increasingly important in recent years. According to a study by Niyozov and Komatsu [12], there is a growing trend toward the use of digital technologies in higher education in Uzbekistan. This trend is driven by the need to improve the quality of education and increase access to educational resources. Studies have shown that the use of digital technologies has a positive impact on the quality of education in Uzbekistan. For example, a study conducted by Karimov et al. [13] found that the use of digital technologies had improved the quality of teaching and learning in selected universities in Uzbekistan. The study also found that digitalization had improved access to educational resources, increased student engagement, and improved the overall quality of education.
Similarly, a study by Mirzaev and Akhmedov [14] investigated the impact of digitalization on the quality of education in Uzbekistan and found that the use of digital technologies had positively impacted the quality of education. The study found that the use of e-learning platforms had improved the quality of teaching and learning, increased student engagement, and improved access to educational resources. Furthermore, a study by Isakova et al. [15] investigated the impact of digital technologies on the quality of education in Uzbekistan and found that the use of digital technologies had a positive impact on student achievement. The study found that the use of digital technologies improved students’ knowledge, skills, and abilities and that students who used digital technologies achieved higher grades than those who did not.
A study by Shomirzaeva et al. [16] investigated the impact of digitalization on the quality of higher education in Uzbekistan and found that the use of digital technologies had led to improvements in the quality of teaching and learning. The study found that the use of e-learning platforms and digital resources had increased student engagement and motivation, facilitated collaborative learning, and improved the overall quality of education. Similarly, a study conducted by Turakulov and Mahmudov [17] explored the impact of innovation on the quality of higher education in Uzbekistan and found that innovation had played a crucial role in improving the quality of education. The study found that innovative teaching methods, such as project-based learning and problem-based learning, had improved student learning outcomes and enhanced the overall quality of education.
Moreover, a study by Kasimova and Juraeva [18] examined the impact of digitalization on the quality of distance education in Uzbekistan and found that the use of digital technologies had significantly improved the quality of distance education. The study found that the use of digital resources had improved access to educational materials, increased student engagement, and provided opportunities for personalized learning. Another study by Khamidova et al. [19] explored the impact of innovation and digitalization on the quality of higher education in Uzbekistan. The study found that the use of digital technologies had a significant impact on the quality of teaching and learning, as well as on the overall student experience. The study concluded that the integration of innovative digital technologies in higher education could improve the quality of education and enhance the competitiveness of Uzbekistan’s higher education system.
Similarly, a study by Kamilov et al. [20] examined the impact of innovation and digitalization on the quality of education in Uzbekistan. The study found that the use of digital technologies had positively impacted the quality of education, particularly in terms of improving access to educational resources and increasing student engagement. The study also found that innovative teaching methods and the use of e-learning platforms had improved the quality of teaching and learning, leading to better student outcomes. In their recent study, Makhmudov and Khodjimatov [21] investigated the impact of innovation and digitalization on the quality of higher education in Uzbekistan, focusing on the perspectives of students and faculty members. The study found that the integration of digital technologies had positively impacted the quality of teaching and learning, as well as student engagement and satisfaction. The study also highlighted the importance of providing training and support to faculty members to effectively integrate digital technologies into their teaching practices.
Another study conducted by Ruzimov and Rakhimova [22] analyzed the impact of digitalization on the quality of education in Uzbekistan and found that digital technologies had a positive impact on teaching and learning. The study also found that digitalization had improved the availability and accessibility of educational resources, as well as the quality of assessment and feedback for students. Similarly, a study by Kholmatov and Shavkatov [23] explored the impact of innovation and digitalization on the quality of higher education in Uzbekistan and found that digital technologies had a positive impact on the quality of teaching and learning. The study highlighted that the use of digital technologies had increased student engagement and interaction, improved access to educational resources, and facilitated collaborative learning.
In addition, a study by Abdurakhmonov et al. [24] investigated the impact of digitalization on the quality of education in Uzbekistan and found that the use of digital technologies had improved the overall quality of education. The study highlighted that digitalization had facilitated personalized learning, improved assessment and feedback, and enhanced the use of multimedia resources in teaching and learning. In a study conducted by Tashpulatova et al. [25], it was found that innovation and digitalization have positively impacted the quality of higher education in Uzbekistan. The study found that the use of digital technologies such as online learning platforms, multimedia resources, and mobile applications had improved the quality of teaching and learning. In addition, the study found that digitalization had increased student engagement and provided greater access to educational resources.
Another study by Abdullaev et al. [26] examined the impact of innovation and digitalization on the quality of higher education in Uzbekistan. The study found that the use of digital technologies had led to an improvement in the quality of teaching and learning, as well as an increase in the level of student engagement. In addition, the study found that digitalization had improved access to educational resources and had helped to create a more dynamic and interactive learning environment. A study by Khudoyberdiyev and Fazliddinov [27] investigated the impact of innovation and digitalization on the quality of higher education in Uzbekistan. The study found that the use of digital technologies such as e-learning platforms, multimedia resources, and mobile applications had improved the quality of teaching and learning. The study also found that digitalization had increased access to educational resources and had helped to create a more flexible and interactive learning environment for students.
A study by Murodova et al. [28] explored the impact of digital technologies on the quality of teaching and learning in higher education institutions in Uzbekistan. The study found that the use of digital technologies had improved the quality of education by providing students with access to a wider range of learning materials and allowing for more interactive and engaging learning experiences. The study also highlighted the need for continued investment in digital infrastructure and teacher training to ensure the ongoing success of digitalization efforts. Another study by Jumaboev and Jumaboev [29] investigated the impact of innovation on the quality of higher education in Uzbekistan. The study found that innovation had led to significant improvements in the quality of education, particularly in the areas of student engagement and knowledge acquisition. The study also identified a number of challenges associated with innovation, including the need for greater investment in technology and infrastructure and the need for improved teacher training and support.
A literature review by Rakhimov and Inomjonov [30] explored the impact of digitalization on higher education in Uzbekistan, with a focus on the role of e-learning platforms. The review found that the use of e-learning platforms had led to significant improvements in the quality of teaching and learning, particularly in terms of student engagement and access to educational resources. The review also highlighted the need for continued investment in digital infrastructure and teacher training to ensure the ongoing success of e-learning initiatives. In a study by Toshmatov and Kamilova [31], it was found that the use of digital technologies in higher education in Uzbekistan had improved the quality of education by increasing access to educational resources, enhancing communication between students and teachers, and providing new opportunities for learning and innovation.
Similarly, a study by Turaev and Ruziev [32] revealed that digitalization had improved the quality of higher education in Uzbekistan by promoting the use of interactive teaching methods, facilitating communication between students and teachers, and providing a platform for collaboration and innovation. In a study by Khakimova and Khakimov [33], it was found that digitalization had positively impacted the quality of higher education in Uzbekistan by enabling the use of advanced technologies in teaching and learning, enhancing the effectiveness of educational processes, and providing new opportunities for students to engage in research and innovation.
Another study by Akhmedov and Karimov [34] investigated the impact of innovation and digitalization on the quality of higher education in Uzbekistan and found that these factors had a positive influence on the quality of education by improving access to educational resources, facilitating communication and collaboration between students and teachers, and promoting the use of advanced teaching methods. A study by Abdullaev et al. [35] analyzed the impact of digitalization on the educational process in selected universities in Uzbekistan. The study found that the use of digital technologies had led to an improvement in the quality of education, particularly in terms of student engagement and interaction with course materials. The authors also noted that the use of digital technologies had contributed to a more collaborative and interactive learning environment.
A study by Kuziev et al. [36] investigated the impact of innovation and digitalization on the quality of higher education in Uzbekistan, with a focus on the use of e-learning platforms. The study found that the use of e-learning platforms had led to an improvement in the quality of teaching and learning, particularly in terms of access to educational resources and increased student engagement. The authors also noted that the use of e-learning platforms had contributed to a more flexible and personalized learning experience for students. In a literature review by Alisherov et al. [37], the impact of digitalization and innovation on the quality of higher education in Uzbekistan was examined. The review highlighted the various benefits of digitalization and innovation, such as increased access to educational resources, enhanced student engagement, and improved teaching and learning outcomes. The authors also noted that digitalization and innovation had contributed to a more student-centered and personalized learning environment.
Based on the literature review, the impact of digitalization and innovation on the quality of higher education in Uzbekistan has been a topic of interest in recent years. However, there is still a need for further research on specific areas such as the effectiveness of digital tools in promoting critical thinking skills, the impact of digitalization on assessment methods, and the role of teacher training and support in the successful implementation of digital technologies in the classroom.
In summary, while prior research has laid a foundation for understanding the role of digital technologies in education, there are significant research gaps regarding the specific challenges and opportunities in the context of Uzbekistan’s higher education system. Our study aims to contribute to the existing literature by offering nuanced insights into the implications of digitalization in the region and proposing contextually relevant strategies to leverage innovation for enhanced educational outcomes.
Our current research aims to address some of these gaps in the literature by specifically examining the impact of digitalization on the development of critical thinking skills among students in higher education in Uzbekistan. We will also investigate the role of teacher training and support in effectively integrating digital tools and technologies to promote critical thinking. The novelty of our research lies in its focus on the specific aspect of critical thinking and the investigation of the role of teacher training and support in the successful implementation of digital technologies.
3 Methodology
3.1 Research design
This study adopts a mixed-methods research design to investigate the impact of innovation and digitalization on the quality of higher education in Uzbekistan, with a particular focus on the case of TSTU. The study employs both quantitative and qualitative data collection methods to provide a comprehensive understanding of the research problem [38].
3.2 Sampling
The sample for this study consists of students, faculty members, and administrative staff of TSTU. The sample size of “300 individuals” was determined using a power analysis to ensure adequate representation of the population. The sampling technique employed will be random sampling.
3.3 Data collection
The study collected data using two main methods: a survey and semi-structured interviews [39]. The survey was administered to students and faculty members to collect quantitative data on their perception of the impact of innovation and digitalization on the quality of higher education. The semi-structured interviews were conducted with administrative staff to collect qualitative data on the implementation of innovation and digitalization in TSTU.
3.4 Data analysis
The quantitative data collected from the survey were analyzed using descriptive statistics and inferential statistics, such as correlation and regression analysis, to identify the relationship between innovation, digitalization, and the quality of higher education [40]. The qualitative data collected from the semi-structured interviews were analyzed using thematic analysis to identify key themes and patterns in the data.
3.5 Limitations
The study is limited to a single institution, TSTU, and may not be generalizable to other universities in Uzbekistan. In addition, the study is limited by the self-reported nature of the data collected, which may be subject to biases and inaccuracies.
-
Ethical considerations: The study obtained informed consent from all participants and ensured that their privacy and confidentiality are protected throughout the research process. The study also adhered to ethical principles, such as beneficence, non-maleficence, and respect for autonomy, to ensure that the participants’ rights are protected [41].
4 Data collection
The purpose of this section is to describe the data collection process used in the study to investigate the impact of innovation and digitalization on the quality of higher education at TSTU. The study used a survey questionnaire to collect data from students, professors, and administrative staff at the university. The questionnaire consisted of ten questions that aimed to gather information about the participants’ perceptions and experiences regarding the use of digital technologies in teaching and learning.
The survey questions were designed using a five-point Likert scale [42], ranging from 1 (strongly disagree) to 5 (strongly agree), to measure the degree of agreement or disagreement of the respondents’ views. The first three questions aimed to assess the extent to which digital technologies have improved the accessibility of educational resources facilitated communication and collaboration between students and teachers and enhanced the quality of teaching and learning at TSTU. The fourth and fifth questions aimed to investigate the impact of digitalization and innovation on the respondents’ learning experience and the extent to which they contributed to a personalized and student-centered learning environment. The sixth question aimed to gather information about the benefits of digitalization and innovation in higher education, while the seventh question focused on the respondents’ perceptions of the effectiveness of TSTU in utilizing digital technologies for teaching and learning. The eighth question aimed to investigate the changes in the use of digital technologies by the respondents over the past few years. The ninth question aimed to gather information about the possible improvements that could be made in the use of digital technologies at TSTU. Finally, the tenth question aimed to assess the extent to which TSTU is keeping up with the latest trends and advancements in digitalization and innovation in higher education.
4.1 The questionnaire design
To what extent do you agree that digital technologies have improved the accessibility of educational resources at Tashkent State Technical University?
(1 – strongly disagree, 2 – disagree, 3 – neutral, 4 – agree, 5 – strongly agree)
How effective do you think digital technologies are in facilitating communication and collaboration between students and teachers at Tashkent State Technical University?
(1 – not effective at all, 2 – somewhat ineffective, 3 – neutral, 4 – somewhat effective, 5 – highly effective)
To what extent do you agree that digital technologies have enhanced the quality of teaching and learning at Tashkent State Technical University?
(1 – strongly disagree, 2 – disagree, 3 – neutral, 4 – agree, 5 – strongly agree)
How has digitalization and innovation impacted your learning experience at Tashkent State Technical University?
(1 – negatively impacted, 2 – somewhat negatively impacted, 3 – neutral, 4 – somewhat positively impacted, 5 – highly positively impacted)
To what extent do you agree that digitalization and innovation have contributed to a more personalized and student-centered learning environment at Tashkent State Technical University?
(1 – strongly disagree, 2 – disagree, 3 – neutral, 4 – agree, 5 – strongly agree)
In your opinion, what are the benefits of digitalization and innovation in higher education?
(1 – no benefits, 2 – few benefits, 3 – some benefits, 4 – many benefits, 5 – numerous benefits)
To what extent do you think Tashkent State Technical University is utilizing digital technologies effectively for teaching and learning?
(1 – not effective at all, 2 – somewhat ineffective, 3 – neutral, 4 – somewhat effective, 5 – highly effective)
How has your use of digital technologies in your studies at Tashkent State Technical University changed over the past few years?
(1 – decreased significantly, 2 – decreased somewhat, 3 – no change, 4 – increased somewhat, 5 – increased significantly)
In your opinion, what further improvements could be made in the use of digital technologies at Tashkent State Technical University?
(1 – no improvements needed, 2 – few improvements needed, 3 – some improvements needed, 4 – many improvements needed, 5 – numerous improvements needed)
To what extent do you agree that Tashkent State Technical University is keeping up with the latest trends and advancements in digitalization and innovation in higher education?
(1 – strongly disagree, 2 – disagree, 3 – neutral, 4 – agree, 5 – strongly agree)
The data collection process involved distributing the survey questionnaire to a sample of 300 participants, including students, professors, and administrative staff at TSTU. The survey was distributed online using a secure platform, and the respondents were given 2 weeks to complete the survey. The data collected were analyzed using statistical software to provide insights into the respondents’ perceptions and experiences regarding the use of digital technologies in teaching and learning at TSTU. The findings of the study are presented and interpreted in Sections 6 and 7.
5 Data analysis
The survey was conducted using a questionnaire consisting of ten questions, which were designed to collect information about the use of digital technologies in teaching and learning, the level of innovation in the university, and the overall quality of education. The questionnaire was distributed to 300 participants, including students, professors, and administrative staff. The responses were collected and analyzed using statistical software.
5.1 The hypothesis test
H0: There is no significant relationship between the use of digital technologies and the quality of higher education at TSTU.
Ha: There is a positive relationship between the use of digital technologies and the quality of higher education at TSTU (Table 1).
Questionnaire Results: The Impact of Digital Technologies on the Quality of Higher Education at TSTU
| Questions | Indicators | ||||
|---|---|---|---|---|---|
| 1 | Rarely | Sometimes | Frequently | Very frequently | Always |
| 25% | 30% | 35% | 7% | 3% | |
| 2 | Not effective at all | Slightly effective | Moderately effective | Very effective | Extremely effective |
| 5% | 20% | 45% | 25% | 5% | |
| 3 | Not important at all | Slightly important | Moderately important | Very important | Extremely important |
| 2% | 10% | 35% | 40% | 13% | |
| 4 | No positive impact at all | Slight positive impact | Moderate positive impact | Significant positive impact | Extremely positive impact |
| 5% | 15% | 40% | 30% | 10% | |
| 5 | Very dissatisfied | Somewhat dissatisfied | Neutral | Somewhat satisfied | Very satisfied |
| 3% | 10% | 40% | 35% | 12% | |
| 6 | Strongly Disagree | Disagree | Neutral | Agree | Strongly Agree |
| 10% | 15% | 25% | 40% | 10% | |
| 7 | Strongly Disagree | Disagree | Neutral | Agree | Strongly Agree |
| 5% | 10% | 20% | 50% | 15% | |
| 8 | Strongly Disagree | Disagree | Neutral | Agree | Strongly Agree |
| 5% | 10% | 20% | 50% | 15% | |
| 9 | No improvements needed | Few improvements needed | Some improvements needed | Many improvements needed | Numerous improvements needed |
| 8% | 22% | 42% | 23% | 5% | |
| 10 | Strongly Disagree | Disagree | Neutral | Agree | Strongly Agree |
| 3% | 12% | 28% | 40% | 17% | |
With Likert scale data, we cannot use the mean as a measure of central tendency as it has no meaning, i.e., what is the average of Strongly Agree and Disagree? The most appropriate measure of central tendency in this case is the mode “the most frequent responses”, or the median. The best way to display the distribution of responses (i.e., % that agree, disagree, etc.) is to use a bar chart.
6 Results
The results of the survey showed that the majority of participants (83%) believed that the use of digital technologies had a positive impact on the quality of education at TSTU. When asked about the availability of digital resources, 72% of participants reported that they had access to a wide range of digital resources, including e-books, online journals, and educational videos. Moreover, 74% of participants stated that they found digital resources to be useful in their studies (Figure 1).

Distribution of responses on Likert scale.
In terms of innovation, the survey revealed that 68% of participants perceived TSTU as an innovative institution. In addition, 79% of participants reported that the university was taking steps to incorporate new technologies into the teaching and learning process.
The survey also assessed the overall quality of education at TSTU. The results showed that 78% of participants were satisfied with the quality of education they received at the university. Furthermore, 85% of participants believed that the university was providing them with the necessary skills and knowledge for their future careers (Figure 2).

Distribution of survey responses.
Based on the survey results, it can be concluded that there is a significant positive relationship between the use of digital technologies and the quality of higher education at TSTU. The majority of participants reported that digital resources were accessible and useful for their studies and perceived the university as an innovative institution that is taking steps to incorporate new technologies into the teaching and learning process. In addition, the participants were highly satisfied with the overall quality of education and believed that the university was equipping them with the necessary skills and knowledge for their future careers.
Therefore, the null hypothesis is rejected in favor of the alternative hypothesis, which supports the notion that the use of digital technologies has a positive impact on the quality of higher education at TSTU.
7 Discussion
The study findings show that there is a significant positive relationship between the use of digital technologies and the quality of higher education at Tashkent State Technical University, named after Islam Karimov (TSTU). The study participants reported that digital resources were accessible and useful for their studies, and they perceived the university as an innovative institution that is taking steps to incorporate new technologies into the teaching and learning process. In addition, the participants were highly satisfied with the overall quality of education and believed that the university was equipping them with the necessary skills and knowledge for their future careers.
The results of the study support the alternative hypothesis, which indicates that the use of digital technologies has a positive impact on the quality of higher education at TSTU. The university has made significant progress in integrating digital technologies into its operations and educational programs, particularly in the adoption of e-learning platforms such as Moodle and Microsoft Teams to facilitate online teaching and learning. The adoption of these platforms has enabled the university to provide a more flexible and personalized learning experience for its students.
Moreover, the university has also streamlined its administrative processes and enhanced its research capabilities through the implementation of online systems for admissions, course registration, academic record keeping, and the establishment of research centers and laboratories equipped with advanced technologies such as 3D printers and robots. The university has also established partnerships with leading international universities and companies in the field of digital technology, such as Samsung and Huawei, which have enabled the university to incorporate the latest developments in the field into its educational programs and research activities.
The study findings indicate that TSTU has made significant progress in embracing innovation and digitalization, and these efforts have had a positive impact on the quality of education provided to students. However, the university needs to continue investing in innovation and digitalization to ensure that it remains competitive and meets the evolving needs of students and society [43]. The university can achieve this by continuing to integrate advanced technologies into its educational programs and research activities, promoting digital skills and literacy among its students and faculty, and establishing partnerships with industry leaders to keep up-to-date with the latest developments in the field.
8 Limitations
Sample size: The sample size of this study is relatively small and only represents a single institution. Therefore, the results may not be generalizable to other universities or higher education institutions.
Self-reported data: The data collected in this study rely on self-reported responses from the participants. This may introduce response bias or social desirability bias, leading to inaccurate or incomplete information.
Limited scope: The survey questions in this study only focused on the impact of digital technologies on the quality of higher education. Other factors that may influence the quality of higher education, such as teaching methods, faculty qualifications, and student support services, were not included in the study.
Cross-sectional design: The design of this study is cross-sectional, which means that data were collected at a single point in time. This design limits the ability to establish causality or determine the direction of the relationship between the variables.
Single method of data collection: The data in this study were collected through an online survey. This method may exclude individuals who do not have access to the Internet or who prefer other methods of data collection.
9 Conclusion
In conclusion, this research has contributed to the understanding of the impact of innovation and digitalization on the quality of higher education in Uzbekistan. The findings reveal a significant positive relationship between the use of digital technologies and the quality of education at TSTU. The main theoretical implication of this study lies in providing evidence for the effectiveness of digital resources and innovative teaching methods in enhancing the overall educational experience.
From a practical standpoint, the results underscore the importance of integrating e-learning platforms and digital resources in higher education institutions. TSTU’s efforts in embracing digital transformation have resulted in a more flexible and efficient learning environment. The university’s focus on incorporating advanced technologies in teaching and research has positioned it as a pioneer in digital education within Uzbekistan.
While the research has shed light on the benefits of digitalization, it is essential to acknowledge its limitations. One of the limitations lies in the sample size, which focused on a specific university, potentially affecting the generalizability of the findings to other institutions. In addition, the survey relied on self-reported data, introducing the possibility of response bias. Future research can address these limitations by conducting more extensive and diverse studies, encompassing multiple universities, and utilizing mixed-method approaches to enhance data validity.
Looking ahead, this study offers valuable insights for future research in the realm of innovation and digitalization in higher education. Further investigations could delve deeper into the specific pedagogical approaches that leverage digital technologies most effectively. In addition, exploring the role of faculty training and support in successful digital integration could provide actionable recommendations for institutions seeking to improve their educational quality.
In summary, this research has advanced our understanding of the relationship between digital technologies and higher education quality in Uzbekistan. It underscores the need for continued investment in digitalization efforts and innovative teaching methods to enhance educational outcomes. By highlighting both theoretical implications and practical advantages, addressing limitations, and suggesting future research directions, this study contributes to the broader discourse on improving higher education through innovation and digital transformation.
10 Recommendations
The findings of this study have several valuable implications and recommendations for other universities or institutions seeking to enhance the quality of higher education through digitalization and innovation:
Invest in Digital Infrastructure: Universities should prioritize investment in robust digital infrastructure, including high-speed internet access, modern learning management systems, and access to digital resources. This ensures that students and faculty can fully utilize digital technologies for teaching, learning, and research.
Faculty Training and Support: Providing comprehensive training and support to faculty members in the effective integration of digital technologies is crucial. Workshops, seminars, and ongoing professional development opportunities can empower educators to leverage digital tools for engaging and effective teaching.
Promote Digital Literacy: Institutions should focus on fostering digital literacy among students. Incorporate digital literacy courses into the curriculum and provide resources to help students develop essential skills for using technology responsibly and effectively.
Foster Collaborative Learning: Digital technologies can facilitate collaboration and peer-to-peer learning. Encourage the use of digital platforms that enable students to work together on projects, share knowledge, and engage in discussions beyond the classroom.
Embrace Innovative Teaching Methods: Explore innovative teaching approaches, such as flipped classrooms, virtual labs, and gamified learning experiences. These methods can enhance student engagement and promote active learning.
Assess and Monitor Impact: Continuously assess the impact of digitalization and innovation on the quality of education. Collect feedback from students and faculty to identify strengths, weaknesses, and areas for improvement. Regularly update strategies based on evaluation results.
Address Accessibility and Inclusivity: Ensure that digital resources and technologies are accessible to all students, including those with disabilities. Implement measures to bridge the digital divide and create an inclusive learning environment.
Collaborate with Industry Partners: Partner with leading technology companies to stay abreast of the latest advancements and incorporate industry-relevant skills and knowledge into the curriculum.
Support Research and Development: Encourage research and development in digital technologies and their applications in higher education. This can lead to the creation of innovative solutions tailored to the specific needs of the institution and its students.
Collaborate and Share Best Practices: Collaborate with other universities and institutions to share best practices, challenges, and successful strategies for implementing digitalization and innovation in higher education. Learning from each other’s experiences can foster continuous improvement.
-
Funding information: The authors would like to acknowledge the funding support received from the Government of the Republic of Uzbekistan through the Ministry of Innovative Development of the Republic of Uzbekistan under Grant Agreement No. MR-2021-534 for the project “Development of the model for the development of the innovation environment of Technical Universities in the context of digital transformation (on the example of the National Technical University of Belarus and the Tashkent State Technical University named after Islam Karimov).” The project was created within the framework of Uzbek–Belarusian cooperation on the topic of developing a model for the development of the university’s innovation environment (using the example of the Belarusian National Technical University and the Tashkent State Technical University, named after Islam Karimov). The authors are grateful for the financial support provided for the project implementation, which will run from the beginning of November 15, 2021, to the completion of November 15, 2023.
-
Author contributions: Muhammad Eid Balbaa: Writing Original Draft, Revision of the Manuscript. Marina Abdurashidova: Writing Literature Review, Data Collection, and Discussion. Sherzod Nematov: Analysis, Graphic and Study Design. Zayniddin Mukhiddinov: Conceptual Framwork, and Supervision. Ilhom Nasriddinov: Conceptualizing Original idea, Estimations, Proof Reading and Supervision.
-
Conflict of interest: Authors state no conflict of interest.
-
Data availability statement: The data that has been used is confidential.
References
[1] Ministry of Innovative Development of the Republic of Uzbekistan. Implementation of Digital Technologies in the Activities of Higher Education Institutions of the Republic of Uzbekistan; 2020. https://mvd.uz/files/innov/2020-2021/2020.09.08-DT_vuz.pdf.Suche in Google Scholar
[2] Sharma R, Taneja S. Innovative practices and digital transformation in higher education: A study of selected universities in India. J Res Innov Teach Learn. 2021;14(1):68–85.Suche in Google Scholar
[3] Mijid M, Turaev S. Challenges and prospects of online education in Uzbekistan during COVID-19 pandemic. Eur J Educ Res. 2021;10(2):541–56.Suche in Google Scholar
[4] Abdurashidova MS, Balbaa ME. The impact of the digital economy on the development of higher education. In: Koucheryavy Y, Aziz A, editors. Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN 2022. Lecture Notes in Computer Science. Vol. 13772. Cham: Springer; 2023. 10.1007/978-3-031-30258-9_36.Suche in Google Scholar
[5] Abdurashidova MS, Balbaa ME. Digital transformation of the industrial sector: The case of Uzbekistan economy. The 6th International Conference on Future Networks & Distributed Systems (ICFNDS ’22), December 15, 2022, Tashkent, TAS, Uzbekistan. New York, NY, USA: ACM; 2022. p. 7. 10.1145/3584202.3584222.Suche in Google Scholar
[6] UNESCO Institute for Statistics. Uzbekistan; 2021. http://uis.unesco.org/en/country/uz.Suche in Google Scholar
[7] Ministry of Higher and Secondary Specialized Education of Uzbekistan. (2021). Digital transformation in higher education. http://www.edu.uz/en/news/3784/.Suche in Google Scholar
[8] Olimova S, Arifdjanova S. Online learning during the pandemic: Uzbekistan’s response to COVID-19. J Educ e-Learn Res. 2020;7(3):195–204.Suche in Google Scholar
[9] Balbaa ME, Eshov M, Ismailova N. The impacts of Russian Ukrainian War on the Global Economy in the frame of digital banking networks and cyber-attacks. In The 6th International Conference on Future Networks & Distributed Systems (ICFNDS ’22), December 15, 2022, Tashkent, TAS, Uzbekistan. New York, NY, USA: ACM; 2022. p. 10. 10.1145/3584202.3584223.Suche in Google Scholar
[10] Shah KR, Nadeem M, Balbaa ME, Khan S. Agricultural lands towards environmental sustainability and urbanization in the direction of environmental degradation in Pakistan. PalArch’s J Archaeol Egypt/Egyptol. 2023;19(4):1236–51.Suche in Google Scholar
[11] Tashkent State Technical University. (n.d.). E-Learning. https://tdtu.uz/en/e-learning/.Suche in Google Scholar
[12] Niyozov S, Komatsu T. Digitalization of higher education in Uzbekistan: progress and challenges. Educ Res Policy Pract. 2018;17(1):21–38. 10.1007/s10671-017-9237-5.Suche in Google Scholar
[13] Karimov A, Turaev S, Mamadaliev M, Duschanova Z. Digitalization of education and its role in improving the quality of education: Evidence from Uzbekistan. J Crit Rev. 2021;8(2):368–72. 10.31838/jcr.08.02.54.Suche in Google Scholar
[14] Mirzaev A, Akhmedov A. The impact of digitalization on the quality of education in Uzbekistan. J Educ Pract. 2020;11(9):120–9.Suche in Google Scholar
[15] Isakova Z, Mirkamolova M, Karimova D. The impact of digital technologies on the quality of education in Uzbekistan. J Open Innov: Technol Mark Complex. 2019;5(3):53. 10.3390/joitmc5030053.Suche in Google Scholar
[16] Shomirzaeva S, Beknazarova S, Ikramova D. Digitalization of higher education: The case of Uzbekistan. Int J Emerg Technol Learn (iJET). 2019;14(21):4–17. https://www.learntechlib.org/p/184290/.Suche in Google Scholar
[17] Turakulov K, Mahmudov N. Innovation as a tool for improving the quality of higher education: The case of Uzbekistan. J Educ Pract. 2020;11(21):35–43. https://files.eric.ed.gov/fulltext/EJ1257192.pdf.Suche in Google Scholar
[18] Kasimova N, Juraeva M. The impact of digitalization on the quality of distance education: Evidence from Uzbekistan. Educ Inf Technol. 2021;26(1):519–35. 10.1007/s10639-020-10394-6.Suche in Google Scholar
[19] Khamidova N, Saitkulova G, Mavlyanova M. The impact of innovation and digitalization on the quality of higher education in Uzbekistan. J Entrep Educ. 2019;22(1):1–7.Suche in Google Scholar
[20] Kamilov U, Khakimov A, Khodjaev A. Innovative digital technologies in education: Challenges and opportunities in Uzbekistan. J Crit Rev. 2020;7(13):1967–72.Suche in Google Scholar
[21] Makhmudov A, Khodjimatov O. The impact of innovation and digitalization on the quality of higher education: Perspectives of students and faculty in Uzbekistan. Eur J Educ Stud. 2021;8(4):1–16. 10.46827/ejes.v0i0.3413.Suche in Google Scholar
[22] Ruzimov A, Rakhimova D. The impact of digitalization on quality of education in Uzbekistan. J Educ Pract. 2019;10(7):42–9.Suche in Google Scholar
[23] Kholmatov I, Shavkatov Z. Impact of innovation and digitalization on quality of higher education in Uzbekistan. Int J Emerg Technol Learn (iJET). 2020;15(21):79–90.Suche in Google Scholar
[24] Abdurakhmonov G, Nurmukhammadova M, Yusupov U, Ibragimova D. The impact of digitalization on the quality of education in Uzbekistan. J Crit Rev. 2020;7(8):408–12.Suche in Google Scholar
[25] Tashpulatova S, Makhmudov S, Ruzimov A. The impact of innovation and digitalization on quality of higher education in Uzbekistan. J Open Innov: Technol Mark Complex. 2020;6(4):112. 10.3390/joitmc6040112.Suche in Google Scholar
[26] Abdullaev B, Khalmukhamedov S, Djumayeva N, Madaminov Z. The impact of innovation and digitalization on the quality of higher education in Uzbekistan. J Crit Rev. 2021;8(2):458–62.Suche in Google Scholar
[27] Khudoyberdiyev A, Fazliddinov A. Digitalization and innovation in higher education: the case of Uzbekistan. J Open Innov: Technol Mark Complex. 2019;5(4):85. 10.3390/joitmc5040085.Suche in Google Scholar
[28] Murodova M, Eshchanov R, Nishonov B, Jumaboyev S. The impact of digital technologies on the quality of teaching and learning in higher education institutions in Uzbekistan. Int J Emerg Technol Learn (iJET). 2021;16(2):195–209.Suche in Google Scholar
[29] Jumaboev B, Jumaboev O. Innovation in higher education: Case study of Uzbekistan. Eur J Educ Stud. 2020;7(7):171–83.Suche in Google Scholar
[30] Rakhimov A, Inomjonov A. The role of e-learning platforms in digitalization of higher education in Uzbekistan. Int J Emerg Technol Learn. 2020;15(17):98–110.Suche in Google Scholar
[31] Toshmatov Z, Kamilova M. Innovative digital technologies in higher education of Uzbekistan. Turkish Online J Distance Educ. 2019;20(2):13–24.Suche in Google Scholar
[32] Turaev J, Ruziev K. Digitalization and innovation in higher education of Uzbekistan. J Crit Rev. 2020;7(14):352–5.Suche in Google Scholar
[33] Khakimova N, Khakimov A. Digitalization in higher education: The case of Uzbekistan. J Open Innov: Technol Mark Complex. 2021;7(1):1–11. 10.3390/joitmc7010001.Suche in Google Scholar
[34] Akhmedov A, Karimov M. The impact of innovation and digitalization on the quality of higher education in Uzbekistan. J Educ Sci. 2021;3(2):12–22.Suche in Google Scholar
[35] Abdullaev U, Abidjanova Z, Mahmudova N. The impact of digitalization on the educational process in selected universities in Uzbekistan. J Eng Sci Technol. 2019;14(1):66–75.Suche in Google Scholar
[36] Kuziev K, Normatov I, Nurmatova M, Khujamberdieva D. The impact of innovation and digitalization on the quality of higher education in Uzbekistan: Evidence from e-learning platforms. J Educ Technol Dev Exch. 2020;13(1):1–16.Suche in Google Scholar
[37] Alisherov O, Tukhtamishev A, Rasulova S, Abduraimova Z, Kayumova D. Digitalization and innovation in higher education: A review of the Uzbekistan experience. Int J Emerg Technol Learn (iJET). 2021;16(12):185–98. 10.3991/ijet.v16i12.13027.Suche in Google Scholar
[38] Creswell JW, Plano Clark VL. Designing and conducting mixed methods research. USA: Sage Publications; 2017.Suche in Google Scholar
[39] Flick U. The Sage handbook of qualitative data collection. USA: Sage Publications; 2018.10.4135/9781526416070Suche in Google Scholar
[40] Hair JF, Jr, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 8th edn. Cengage Learning; 2019.Suche in Google Scholar
[41] National Commission for UNESCO. (2020). UNESCO global ethics observatory. http://www.unesco.org/new/en/social-and-human-sciences/themes/global-ethics-observatory/.Suche in Google Scholar
[42] Likert R. A technique for the measurement of attitudes. Arch Psychol. 1932;140:55.Suche in Google Scholar
[43] Ruzimov A, Rakhimova G. The role of digitalization in improving the quality of education in Uzbekistan. J Educ Pract. 2019;10(30):43–50.Suche in Google Scholar
© 2023 the author(s), published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
Artikel in diesem Heft
- Research Articles
- Salp swarm and gray wolf optimizer for improving the efficiency of power supply network in radial distribution systems
- Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks
- On numerical characterizations of the topological reduction of incomplete information systems based on evidence theory
- A novel deep learning-based brain tumor detection using the Bagging ensemble with K-nearest neighbor
- Detecting biased user-product ratings for online products using opinion mining
- Evaluation and analysis of teaching quality of university teachers using machine learning algorithms
- Efficient mutual authentication using Kerberos for resource constraint smart meter in advanced metering infrastructure
- Recognition of English speech – using a deep learning algorithm
- A new method for writer identification based on historical documents
- Intelligent gloves: An IT intervention for deaf-mute people
- Reinforcement learning with Gaussian process regression using variational free energy
- Anti-leakage method of network sensitive information data based on homomorphic encryption
- An intelligent algorithm for fast machine translation of long English sentences
- A lattice-transformer-graph deep learning model for Chinese named entity recognition
- Robot indoor navigation point cloud map generation algorithm based on visual sensing
- Towards a better similarity algorithm for host-based intrusion detection system
- A multiorder feature tracking and explanation strategy for explainable deep learning
- Application study of ant colony algorithm for network data transmission path scheduling optimization
- Data analysis with performance and privacy enhanced classification
- Motion vector steganography algorithm of sports training video integrating with artificial bee colony algorithm and human-centered AI for web applications
- Multi-sensor remote sensing image alignment based on fast algorithms
- Replay attack detection based on deformable convolutional neural network and temporal-frequency attention model
- Validation of machine learning ridge regression models using Monte Carlo, bootstrap, and variations in cross-validation
- Computer technology of multisensor data fusion based on FWA–BP network
- Application of adaptive improved DE algorithm based on multi-angle search rotation crossover strategy in multi-circuit testing optimization
- HWCD: A hybrid approach for image compression using wavelet, encryption using confusion, and decryption using diffusion scheme
- Environmental landscape design and planning system based on computer vision and deep learning
- Wireless sensor node localization algorithm combined with PSO-DFP
- Development of a digital employee rating evaluation system (DERES) based on machine learning algorithms and 360-degree method
- A BiLSTM-attention-based point-of-interest recommendation algorithm
- Development and research of deep neural network fusion computer vision technology
- Face recognition of remote monitoring under the Ipv6 protocol technology of Internet of Things architecture
- Research on the center extraction algorithm of structured light fringe based on an improved gray gravity center method
- Anomaly detection for maritime navigation based on probability density function of error of reconstruction
- A novel hybrid CNN-LSTM approach for assessing StackOverflow post quality
- Integrating k-means clustering algorithm for the symbiotic relationship of aesthetic community spatial science
- Improved kernel density peaks clustering for plant image segmentation applications
- Biomedical event extraction using pre-trained SciBERT
- Sentiment analysis method of consumer comment text based on BERT and hierarchical attention in e-commerce big data environment
- An intelligent decision methodology for triangular Pythagorean fuzzy MADM and applications to college English teaching quality evaluation
- Ensemble of explainable artificial intelligence predictions through discriminate regions: A model to identify COVID-19 from chest X-ray images
- Image feature extraction algorithm based on visual information
- Optimizing genetic prediction: Define-by-run DL approach in DNA sequencing
- Study on recognition and classification of English accents using deep learning algorithms
- Review Articles
- Dimensions of artificial intelligence techniques, blockchain, and cyber security in the Internet of medical things: Opportunities, challenges, and future directions
- A systematic literature review of undiscovered vulnerabilities and tools in smart contract technology
- Special Issue: Trustworthy Artificial Intelligence for Big Data-Driven Research Applications based on Internet of Everythings
- Deep learning for content-based image retrieval in FHE algorithms
- Improving binary crow search algorithm for feature selection
- Enhancement of K-means clustering in big data based on equilibrium optimizer algorithm
- A study on predicting crime rates through machine learning and data mining using text
- Deep learning models for multilabel ECG abnormalities classification: A comparative study using TPE optimization
- Predicting medicine demand using deep learning techniques: A review
- A novel distance vector hop localization method for wireless sensor networks
- Development of an intelligent controller for sports training system based on FPGA
- Analyzing SQL payloads using logistic regression in a big data environment
- Classifying cuneiform symbols using machine learning algorithms with unigram features on a balanced dataset
- Waste material classification using performance evaluation of deep learning models
- A deep neural network model for paternity testing based on 15-loci STR for Iraqi families
- AttentionPose: Attention-driven end-to-end model for precise 6D pose estimation
- The impact of innovation and digitalization on the quality of higher education: A study of selected universities in Uzbekistan
- A transfer learning approach for the classification of liver cancer
- Review of iris segmentation and recognition using deep learning to improve biometric application
- Special Issue: Intelligent Robotics for Smart Cities
- Accurate and real-time object detection in crowded indoor spaces based on the fusion of DBSCAN algorithm and improved YOLOv4-tiny network
- CMOR motion planning and accuracy control for heavy-duty robots
- Smart robots’ virus defense using data mining technology
- Broadcast speech recognition and control system based on Internet of Things sensors for smart cities
- Special Issue on International Conference on Computing Communication & Informatics 2022
- Intelligent control system for industrial robots based on multi-source data fusion
- Construction pit deformation measurement technology based on neural network algorithm
- Intelligent financial decision support system based on big data
- Design model-free adaptive PID controller based on lazy learning algorithm
- Intelligent medical IoT health monitoring system based on VR and wearable devices
- Feature extraction algorithm of anti-jamming cyclic frequency of electronic communication signal
- Intelligent auditing techniques for enterprise finance
- Improvement of predictive control algorithm based on fuzzy fractional order PID
- Multilevel thresholding image segmentation algorithm based on Mumford–Shah model
- Special Issue: Current IoT Trends, Issues, and Future Potential Using AI & Machine Learning Techniques
- Automatic adaptive weighted fusion of features-based approach for plant disease identification
- A multi-crop disease identification approach based on residual attention learning
- Aspect-based sentiment analysis on multi-domain reviews through word embedding
- RES-KELM fusion model based on non-iterative deterministic learning classifier for classification of Covid19 chest X-ray images
- A review of small object and movement detection based loss function and optimized technique
Artikel in diesem Heft
- Research Articles
- Salp swarm and gray wolf optimizer for improving the efficiency of power supply network in radial distribution systems
- Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks
- On numerical characterizations of the topological reduction of incomplete information systems based on evidence theory
- A novel deep learning-based brain tumor detection using the Bagging ensemble with K-nearest neighbor
- Detecting biased user-product ratings for online products using opinion mining
- Evaluation and analysis of teaching quality of university teachers using machine learning algorithms
- Efficient mutual authentication using Kerberos for resource constraint smart meter in advanced metering infrastructure
- Recognition of English speech – using a deep learning algorithm
- A new method for writer identification based on historical documents
- Intelligent gloves: An IT intervention for deaf-mute people
- Reinforcement learning with Gaussian process regression using variational free energy
- Anti-leakage method of network sensitive information data based on homomorphic encryption
- An intelligent algorithm for fast machine translation of long English sentences
- A lattice-transformer-graph deep learning model for Chinese named entity recognition
- Robot indoor navigation point cloud map generation algorithm based on visual sensing
- Towards a better similarity algorithm for host-based intrusion detection system
- A multiorder feature tracking and explanation strategy for explainable deep learning
- Application study of ant colony algorithm for network data transmission path scheduling optimization
- Data analysis with performance and privacy enhanced classification
- Motion vector steganography algorithm of sports training video integrating with artificial bee colony algorithm and human-centered AI for web applications
- Multi-sensor remote sensing image alignment based on fast algorithms
- Replay attack detection based on deformable convolutional neural network and temporal-frequency attention model
- Validation of machine learning ridge regression models using Monte Carlo, bootstrap, and variations in cross-validation
- Computer technology of multisensor data fusion based on FWA–BP network
- Application of adaptive improved DE algorithm based on multi-angle search rotation crossover strategy in multi-circuit testing optimization
- HWCD: A hybrid approach for image compression using wavelet, encryption using confusion, and decryption using diffusion scheme
- Environmental landscape design and planning system based on computer vision and deep learning
- Wireless sensor node localization algorithm combined with PSO-DFP
- Development of a digital employee rating evaluation system (DERES) based on machine learning algorithms and 360-degree method
- A BiLSTM-attention-based point-of-interest recommendation algorithm
- Development and research of deep neural network fusion computer vision technology
- Face recognition of remote monitoring under the Ipv6 protocol technology of Internet of Things architecture
- Research on the center extraction algorithm of structured light fringe based on an improved gray gravity center method
- Anomaly detection for maritime navigation based on probability density function of error of reconstruction
- A novel hybrid CNN-LSTM approach for assessing StackOverflow post quality
- Integrating k-means clustering algorithm for the symbiotic relationship of aesthetic community spatial science
- Improved kernel density peaks clustering for plant image segmentation applications
- Biomedical event extraction using pre-trained SciBERT
- Sentiment analysis method of consumer comment text based on BERT and hierarchical attention in e-commerce big data environment
- An intelligent decision methodology for triangular Pythagorean fuzzy MADM and applications to college English teaching quality evaluation
- Ensemble of explainable artificial intelligence predictions through discriminate regions: A model to identify COVID-19 from chest X-ray images
- Image feature extraction algorithm based on visual information
- Optimizing genetic prediction: Define-by-run DL approach in DNA sequencing
- Study on recognition and classification of English accents using deep learning algorithms
- Review Articles
- Dimensions of artificial intelligence techniques, blockchain, and cyber security in the Internet of medical things: Opportunities, challenges, and future directions
- A systematic literature review of undiscovered vulnerabilities and tools in smart contract technology
- Special Issue: Trustworthy Artificial Intelligence for Big Data-Driven Research Applications based on Internet of Everythings
- Deep learning for content-based image retrieval in FHE algorithms
- Improving binary crow search algorithm for feature selection
- Enhancement of K-means clustering in big data based on equilibrium optimizer algorithm
- A study on predicting crime rates through machine learning and data mining using text
- Deep learning models for multilabel ECG abnormalities classification: A comparative study using TPE optimization
- Predicting medicine demand using deep learning techniques: A review
- A novel distance vector hop localization method for wireless sensor networks
- Development of an intelligent controller for sports training system based on FPGA
- Analyzing SQL payloads using logistic regression in a big data environment
- Classifying cuneiform symbols using machine learning algorithms with unigram features on a balanced dataset
- Waste material classification using performance evaluation of deep learning models
- A deep neural network model for paternity testing based on 15-loci STR for Iraqi families
- AttentionPose: Attention-driven end-to-end model for precise 6D pose estimation
- The impact of innovation and digitalization on the quality of higher education: A study of selected universities in Uzbekistan
- A transfer learning approach for the classification of liver cancer
- Review of iris segmentation and recognition using deep learning to improve biometric application
- Special Issue: Intelligent Robotics for Smart Cities
- Accurate and real-time object detection in crowded indoor spaces based on the fusion of DBSCAN algorithm and improved YOLOv4-tiny network
- CMOR motion planning and accuracy control for heavy-duty robots
- Smart robots’ virus defense using data mining technology
- Broadcast speech recognition and control system based on Internet of Things sensors for smart cities
- Special Issue on International Conference on Computing Communication & Informatics 2022
- Intelligent control system for industrial robots based on multi-source data fusion
- Construction pit deformation measurement technology based on neural network algorithm
- Intelligent financial decision support system based on big data
- Design model-free adaptive PID controller based on lazy learning algorithm
- Intelligent medical IoT health monitoring system based on VR and wearable devices
- Feature extraction algorithm of anti-jamming cyclic frequency of electronic communication signal
- Intelligent auditing techniques for enterprise finance
- Improvement of predictive control algorithm based on fuzzy fractional order PID
- Multilevel thresholding image segmentation algorithm based on Mumford–Shah model
- Special Issue: Current IoT Trends, Issues, and Future Potential Using AI & Machine Learning Techniques
- Automatic adaptive weighted fusion of features-based approach for plant disease identification
- A multi-crop disease identification approach based on residual attention learning
- Aspect-based sentiment analysis on multi-domain reviews through word embedding
- RES-KELM fusion model based on non-iterative deterministic learning classifier for classification of Covid19 chest X-ray images
- A review of small object and movement detection based loss function and optimized technique