The conventional methods adopted by the formers for leaf disease detection and classification can be monotonous and unreliable. It is challenging for formers sometimes to attempt and anticipate the type of disease manually. The inability to early diagnose the disease and erroneous predictions may damage the crop, resulting in loss of crop production. To prevent losses and increase crop production, computer-based image classification methods can be adopted by the formers. Several methods have been suggested and utilized to predict crop plant diseases using pictures of unhealthy leaves. Investigators are currently making significant advances in the detection of plant diseases by experimenting with various methodologies and models. Artificial Neural Networks (ANNs) stand out as a widely employed machine learning method for effectively classifying images and predicting diseases. Alongside ANNs, other prevalent algorithms include Linear Regression (LNR), Random Forest Algorithm (RFA), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and k-nearest Neighbor (KNN). Combining these algorithms has been explored in various studies to enhance accuracy. This review examines their application in classifying diseases in citrus crop leaves, focusing on metrics like Accuracy, Precision, and Sensitivity. Each algorithm has its strengths and weaknesses in disease identification from leaf images. The accuracy and effectiveness of these algorithms depend significantly on the quality and dimensionality of the leaf images. Therefore, a reliable leaf image database is crucial for developing a robust machine-learning model for disease detection and analysis.
The study aimed at investigating the extent to which teachers valued play-based learning as a facilitating pedagogy in early childhood education curriculum in Sidama Regional State. A study employed mixed research approach with convergent parallel design. A total of 427 participants were selected from various educational backgrounds, including teachers, PTA members, principals, experts and ECE coordinators. The instruments were self-constructed questionnaires and interviews guiding questions. Descriptive and inferential statistics were used to analyze closed-ended quantitative data while open-ended qualitative data were narrated. The findings indicated that large number of pre-primary school teachers were not valuing play-based learning as a mediating pedagogy for indoor-and outdoor activities. This lack of emphasis on play-based learning was attributed to misconceptions and a lack of awareness about effective pedagogical practices. As a result, the traditional teaching methods were prevalent, prohibiting the implementation of play-based pedagogy in early childhood education. To address the issues the study recommends that teacher training colleges focus on raising awareness through training and seminars on the benefits of play-based learning in early childhood education. Furthermore, provide on-job training and professional development opportunities for facilitators can help enhance their knowledge, attitudes and skills in implementing play – based learning strategies effectively.
This paper explored the long-term leadership of Zelensky given that when the original article The Zelensky Files: Strategies and approaches for university leaders was published, it was still early days in the war. Zelensky, it seemed went from an obscure, challenged peacetime leader to a global ‘Ambassador for Freedom’ in a very short time. Indeed, as noted in the original article, leadership in all its diverse guises is messy, complex, unpredictable, and often enigmatic. Talented leaders fail every day; other average leaders emerge as the right leader for the right reasons and purposes; and perhaps with a little luck become leadership icons. Zelensky exemplifies the latter but, in reality he also is talented and has an instinctive and refined blend of pragmatism and common sense making him the right choice for Ukraine and freedom. Zelensky’s long-term leadership strategies include 1) the leader’s core values and approaches comprise his/her leadership foundation; 2) recognition that leadership decision making is difficult and often heart wrenching; 3) Partnerships are essential strategies for leaders and organizations; 4) the right people in the right positions is more important than talent; and 5) the leader’s singular and only role is to lead. The world of the university seems at times a universe away from the battlefield and yet when we delve down for a closer look, we find the most remarkable alignments where leadership strategies turn out to be fluid, agile, and adaptive to the needs of many diverse leaders and organizations, wartime and peacetime, and beyond. Ukraine’s universities have faced the destruction of war. One in five institutions in Ukraine have been damaged or destroyed. Student enrolments have declined and many foreign students left the country whilst research output overall has been estimated to have declined between 10-18%. Indeed, even with all these challenges, this is where an inspirational, transformative leader who exudes confidence, belief in the cause and love of country during one of the most serious crises in Ukraine’s history since the Holodomor (Ukrainian Famine, 1932-33), can inspire a nation. And, despite the ravages of war, the university doors are open and the classroom lights have remained on in most institutions. The images of students taking online courses in shelters and bunkers whilst others risk their lives sitting by a fountain or in a park for better Internet access all in the name of freedom. The indelible point here is education is freedom. Education is innovation. Education is the future. Education is peaceful revolution. And, education is the heart and soul of the human condition. The Zelensky Files are not a leadership panacea for all the complex issues facing the modern university. A word that may best describe the leadership continuum is kaleidoscope. The kaleidoscope of leadership often seen as a changing array of shapes, sizes and colors. The kaleidoscope of leadership with its changing approaches, strategies and blends over time with the Zeitgeist norms of the era becoming the catalyst for exploring new situational contexts, evolving trends and developments and yet accepting there is no one single silver bullet style of leadership for resolving all issues. The Zelensky Files do, however, contribute to our journey to better understand that elusive, enigmatic endeavor – leadership.
This study investigates the influence of gender, previous computing knowledge, institution type and interpersonal skills on the self-efficacy of final year students of Computer Science in Southwest, Nigeria. Leveraging on survey data collected from 408 final year students of Computer Science across 9 universities, the research provides empirical evidence into the influence of some factors associated with the self-efficacy of undergraduate students. The findings revealed that interpersonal skills and previous computing knowledge influences the self-efficacy of the final year undergraduate students while the self-efficacy of the students does not differ across gender and school type. Implications for both curriculum developers and students were discussed, and the need to enhance the self-efficacy of the students for workplace competitiveness was underscored.
Timor-Leste began re-engineering the educational system after suffering the effects of a drawn-out struggle to attain independence. Despite the opportunities provided for out-of-school children to enlist in the new education system, and the projects bankrolled by international organizations, teachers in rural schools in Timor-Leste are confronted with teaching and learning-related challenges. This paper presents the outcomes of an experiment on the usability of blended learning instructional strategy to support students’ civic knowledge and social skills. A pretest-posttest quasi experimental design was used to evaluate the usability of a Blended Learning Instructional Strategy on 52 secondary school students in the junior cadre in rural areas in Nigeria. The data analysis suggested that the combination of blended learning and conventional lecture method has the capacity of inculcating civic knowledge and social skills in learners in rural schools. The research determined the trajectories of male and female learners’ performance using their academic abilities after being taught civic concepts and socially-related skills with conventional teaching methods and computer-assisted teaching. The results from the Nigerian study were used to present best practices for citizenship and social development teachers in using a blended learning instructional strategy in rural schools in Timor-Leste. However, further research is recommended on using a blended learning instructional strategy in Timor-Leste and other developing nations.
The use of technology in science education is almost evident in every school across the globe. Measuring the impact of technology in science classrooms has become a favorite field of many research practitioners. Therefore, this present undertaking conducted a systematic review of the methods and tools used by different researchers, plus the students’ perspectives and experiences regarding the impact of technology in science lessons. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were utilized in selecting, coding, and analyzing the research articles subject to this review. In total, 15 articles were reviewed from major academic databases such as PubMed, ERIC, Scopus, Springer, and Google Scholar, and 100 were originally under examination. The review results suggest that articles from 2020-2024 used varied research designs, with quantitative survey design being the most common. The data collection tool is primarily a questionnaire, which can be a Likert or a test. Most articles reported their validity and reliability data, but many failed to divulge the data. Moreover, the students' perceptions and experiences of using technology inside the science classroom were reasonably positive. The threat to validity, if eliminated, can allow for conclusive and undoubtful findings that the impact of technology in the science class is positive.
Introduction. It is known that the educational process in any educational institution is the cornerstone of the institution, where this research came to clarify the process of organizing e learning within the educational activities within the educational institution. The platform and methods of providing technical support. Methodology. This study was implemented by using some methods including: the analysis of some previous studies on the subject of the study, in addition to making an analysis of some techniques used in distance learning in addition to studying and analyzing some government educational standards for higher education with the generalization of the results of the study for consideration them in the educational process. As for the experimental methods, they are represented in the analysis of student activities and the illumination of the faculty at the university, with some questions for discussion. The results. Among the most important results obtained in this study are determining the requirements for establishing training courses in the field of e-learning, in addition to submitting a proposal to develop an experimental model for educational courses in the field of e-learning and its basic components. Among the most important additional results obtained in this work it is to reveal the advantages of organizing the individual work mechanism for students, using LMS Moodle, whether for the teacher or the student. This is in addition to reconsidering the possible models needed to motivate faculty members while working in the Moodle educational system. Where these results came in response to improving the educational process within the educational institution (university). Conclusion. It is known that any educational institution takes into account the quality in the educational process, and to ensure quality is maintained, the control and oversight systems are improved, which leads to controlling the quality of the use of e-learning, which in turn works to evaluate the effectiveness in the use of the English language, in addition to making some administrative decisions, which are aimed at E-learning department.
Many companies cite cost, scalability, and data integrity as the top three driving factors. Most have long-term plans for the cloud, with 25% planning to use them for most of their market data needs over the next year. However, the most impressive potential benefit of the cloud lies in its ability to provide the powerful analytics and machine learning tools needed to develop AI solutions, as well as unlock the potential for increased profitability. Cloud technologies provide fast and cost-effective on-demand access to large amounts of data. Cloud users can quickly validate the results of experiments, avoiding lengthy approval and implementation processes and costs that previously held the pace of innovation. This research addresses the main problems facing remote work, including distance education, during the situation related to the COVID-19 pandemic, by using current technologies. Where the authors studied the identification, analysis and organization of the most important problems facing cybersecurity related to remote work applications.
Artificial intelligence, deep learning, machine learning, robotics and digital transformation have revolutionized industries, while creating new opportunities together with challenges and implications in the society. This comprehensive survey explores the applications, prospects, problems, and future perspectives of artificial intelligence, deep learning, machine learning, robotics, and digital transformation in the contemporary society. The objectives of this research included to explore practical applications of these emerging technologies and industrial trends, examine the impact and potential opportunities of the technologies and innovations, assess the challenges and problems associated with implementation of such disruption technologies and transformations, and determine future trends and advancements through which the technologies can be utilized to achieve sustainable prospects. Research methodology involved collecting and analyzing relevant scholarly articles and reports from electronic journals, electronic books as well as internet and web based information. First, this comprehensive search was conducted in academic databases such as Scopus, ACM Digital Library, and Google Scholar. Secondly, critical review was done based on vital information regarding applications of these technologies, problems faced in implementation, future prospects and advancements in the field. With the digital transformation, this research has significant implications since artificial intelligence, deep learning, machine learning and robotics are applied in various industries and economies. Applications of these technologies in healthcare and medical, manufacturing and transportation, climate adaptation and agriculture, business and trade, information science and computing as well information communication technology have lead to improved efficiency, accuracy, and decision-making. By leveraging the capabilities of these technologies, significant progress has been made towards achieving sustainability and combating climate change challenges. Additionally, artificial intelligence, deep learning, machine learning, robotics, and digital transformation have profound implications for employment patterns, job skills, and workforce dynamics. Developments and connections in these emerging and disruption technologies present tremendous opportunities for humanities, organizations, industries and broad societies.
This paper presents a systematic literature review on creditworthiness prediction, a critical aspect of financial risk assessment. With a focus on informing lending decisions and mitigating risks in financial institutions, the review examines methods, algorithms, and features commonly utilized in creditworthiness prediction. Through systematic searches across high-impact academic databases such as Scopus, ScienceDirect, IEEE Xplore, and SpringerLink, 25 relevant papers were collected and analyzed. Classification and regression methods emerged as predominant approaches, with Decision Trees, Random Forest, and Support Vector Machines identified as effective algorithms. Additionally, hybrid models combining traditional machine learning with deep learning techniques demonstrated promising performance. Features encompassed loan/application information, employment and income, financial history, demographics, and external factors. The findings provide insights into current practices and highlight opportunities for future research, including the integration of emerging technologies like blockchain and explainable AI, and the exploration of alternative data sources. This review contributes to advancing understanding and informs the development of more effective credit risk assessment models to support informed lending decisions and enhance financial stability.
Linking skills training and academic education is a formidable challenge in many professional fields. At modern military academies, officer cadets learn military skills and strategic thinking, fostered by skills training and academic education respectively. As an example, we briefly elaborate on these two learning tracks at the Royal Military Academy of the Netherlands Defence Academy. However, skills training and academic education are often implemented in a non-integrated manner. Because officers have to integrate military skills and strategic thinking during actual military operations, it is paramount that officer cadets learn how to integrate these in a meaningful way. Therefore, we designed an innovative integrated instructional design (ID) model that aims to meet the needs of both military training and academic education. We herein describe the six-step design process of the resulting so-called TrEd ID model, based on the Nine events of instruction model and STAR Legacy, linked through the First Principles of Instruction. The TrEd ID model provides common ground to military instructors and civilian academicians at a military academy, encouraging mutual understanding and collaboration. Future research is needed to understand the potential value of the TrEd ID model in bridging the gap between skills training and academic education, and how to optimally prepare officer cadets for their roles.
Crops like tomatoes are vital to farmers' livelihoods in Sri Lanka, where agriculture is a key economic pillar. But growing tomatoes comes with a lot of difficulties, not the least of which is the possibility of certain diseases that can destroy crops. The timely implementation of interventions and reduction of losses are contingent upon the early discovery of these disorders. Using convolutional neural networks (CNNs) and image processing techniques, this study offers a novel solution to this problem by detecting tomato leaf illnesses. One unique aspect of this study is the use of a custom dataset made up of photos of Sri Lankan tomato leaves from several farms in Embilipitiya, Suriyawewa an area noted for being susceptible to several tomato illnesses. The dataset includes a variety of disease categories that are common in the local agricultural setting, such as tomato early blight, tomato Septoria leaf spot, tomato curl, and tomato leaf minor. The quality of the dataset is improved using pre-processing methods including segmentation and picture enhancement. The dataset is then used to train a CNN architecture for the purpose of classifying diseases. The efficiency of the suggested method is demonstrated by the experimental findings, which show that it can accurately identify and classify tomato leaf diseases. The system that has been built provides an automated and effective tool for early disease diagnosis, which facilitates timely intervention and efficient management approaches. Utilising a localised dataset improves the system's resilience and adaptability, which makes it ideal for implementation in Sri Lankan tomato farms.
This study was designed to examine the consciousness of undergraduate students in Nigerian universities about the security risks of online social networking. Data was collected from 336 students using Qualtrics, an online survey tool. To a large extent, 53.4% of the respondents reported concerns about the specific data that other social media users could possess about them due to their participation in social networking. As high as 72.6% reported that they are aware of pretenders in the OSN; they are very vigilant in adding people to their friends’ list and seriously consider security (75.9%) before posting their photos to avoid exploitation. Indices of the security consciousness are not as high. Except for social media experience (p=0.112) and past privacy invasion (p=0.209), the rest of the variables explained the security risk consciousness of the students.
This study assessed the floods related factors affecting teaching and learning activities in floods prone schools in Chemba district, Dodoma region, Tanzania. The findings revealed floods factors such as schools’ inaccessibility due to impassable roads accompanied by distance to teaching and learning activities in floods prone schools due to pupils' psychological impacts, disruption of the school calendar, and increased insecurity. Moreover, the communities and schools employed different strategies to increase access to primary education, such as roads and footpaths repairing and maintenance, community participation in drainage activities, establishing satellite schools, and enforcing by-laws and remedial classes. Therefore, the study recommends government-community partnerships to address challenges in flood-prone areas to increase school-age children’s access to primary education.
Lightning is a natural occurrence which is created through the mixture of hot and cold air in the cloud. Sudden occurrence of lightning has caused damages to many lives and properties, for this reason; there is a need to develop a system that can predict lighting occurrence for people to take necessary precaution. However, accurately predicting lightning has been a challenge among researchers, as they find it difficult to select the right approach and algorithms to use when predicting lightning. Thus, this paper presents a systematic literature review on the best techniques for lightning prediction by reviewing relevant papers that are systematically collected based on the inclusion and the exclusion criteria from four different academic databases which includes Scopus, IEEE Xplore, Science direct, and SpringerLink. The findings from the review shows that the Random Forest algorithm is mostly used for lightning prediction and has generally out performed all other algorithms that have been used in lightning prediction in remote region. Also the review finds out that there is an inverse relationship between predicting system accuracy and lead time. Another observation in the research is that numerical weather prediction predicts more accurately compare to geo satellite prediction.
Cybertechnologies have become so important to human daily engagements that society can hardly do anything outside these ubiquitous technologies. These technologies have a great impact on the economy, as well as the social well-being of citizens in developed and third world countries. The dependence on digital technologies is useful in every sector of society. The consistent call for the adoption of digital technologies in education has seen different governments in advanced and developing nations of the world introducing the use of information and communication technologies (ICT) at the different facets of education. This introduction of ICT has brought in the use of the Internet in education which in turn has exposed the learners to the various threats that come with exposure to the Internet or cybertechnology. The internet is believed to have played a prominent role in facilitating the Fourth Industrial Revolution (4IR) in the different sectors of the world economy, most especially education. However, cybertechnology has brought a new dimension of threats ranging from cyber-bullying, identity theft, scamming, advanced fee fraud, and so many other dangers to humans’ daily existence. None of the citizens in the developed and developing nations of the world is immune from these crimes committed in cyberspace; therefore, there is a need to introduce school children to cybersecurity education as early as possible. It is in light of the foregoing that this systematic review proposes the Action Cybercrime Prevention Programme to equip school children with knowledge and skills that would minimise their exposure to dangers inherent in the use of information and communication technology (ICT) and the Internet for education and social purposes. This systematic review discusses the intergenerational effects of the proposed Action Cybercrime Prevention Programme on young learners to educate their parents or grandparents from falling victim to cybercrimes.
Programming is a difficult subject to learn and teach. When it comes to students learning basic programming information and skills, university-level introductory programming courses (Java, C++, Visual Basic, and Python) are critical. Students' achievement is negatively impacted by a negative attitude about programming. As a result, the study discovered the impact of students' perceptions in university computer programming courses. The study covered students studying Computer Science from the University of Ghana. A survey descriptive design with a quantitative technique was used in this investigation. The population of the study was 2,030 with 368 sample size. Purposive sampling was utilized to choose University of Ghana, Legon as the study's location. The study's participants were chosen using a stratified random sampling technique. Closed-ended questionnaire was used for data collection. The SPSS version 26 and PROCESS Macro were used to analyze the data. Respondents’ data were examined applying both inferential and descriptive statistics. The study revealed that students see programming as unfamiliar was the highest perception of programming to students. The study found that students see programming as easy with dedication was the lowest perception of programming to students. In conclusion, the significant impact of perception of students in Computer Programming account for 84% of the contribution of factors that influence self-efficacy.
Globally, higher education institutions are increasingly digitising their operations. A bustling and expanding ecosystem of digital platforms in higher education includes online teaching and research, decision-making using learning and business analytics, and building "smart" campuses. Universities do not digitalise on their own but depend on proprietary digital platforms. This study focuses on how digitalisation impacts higher education institutes in Sri Lanka. This study used a methodology based on a qualitative survey, and the research used personal interviews to collect the required data. The research findings disclosed that although all stakeholders within the higher education institutes had to adapt to forced digitalisation, most were not ready to accept it as the main element defining current-day higher education. Higher education institutes, students, and teachers are the mainly affected parties in the forced digitalisation because of Covid-19. The main factors influencing these parties were inadequate digital literacy, poor financial capabilities, and lack of basic requirements for a digitalised learning environment. However, even with minimum resources, learning is underway in almost all higher education institutes, but it resulted in a digital divide, primarily impacting students and their learning abilities.
Facial affective computing has gained popularity and become a progressive research area, as it plays a key role in human-computer interaction. However, many researchers lack the right technique to carry out a reliable facial affective computing effectively. To address this issue, we presented a review of the state-of-the-art artificial intelligence techniques that are being used for facial affective computing. Three research questions were answered by studying and analysing related papers collected from some well-established scientific databases based on some exclusion and inclusion criteria. The result presented the common artificial intelligence approaches for face detection, face recognition and emotion detection. The paper finds out that the haar-cascade algorithm has outperformed all the algorithms that have been used for face detection, the Convolutional Neural Network (CNN) based algorithms have performed best in face recognition, and the neural network algorithm with multiple layers has the best performance in emotion detection. A limitation of this research is the access to some research papers, as some documents require a high subscription cost. Practice implication: The paper provides a comprehensive and unbiased analysis of existing literature, identifying knowledge gaps and future research direction and supports evidence-based decision-making. We considered articles and conference papers from well-established databases. The method presents a novel scope for facial affective computing and provides decision support for researchers when selecting plans for facial affective computing.
At present, modern information technologies are developing very rapidly. A lot of different software tools are being created to improve and simplify people's lives. This is especially true in the context of distance learning. In the context of online learning, the traditional form of conducting dictations needs to be transformed. Therefore, it is possible to propose automation of the process of conducting and checking dictations using software. The paper analyzes scientific research and publications of the current state of language synthesis technologies and text similarity testing. With the help of C# and the language synthesis libraries of Microsoft, Google, Amazon, software has been developed that allows the user to listen and type text, and then automatically check it with the initial sample. The conducted testing for different types of errors showed the possibility and expediency of development the system for conducting and checking dictations. Follow-up activities will focus on improving the effectiveness of the program. The use of such programs will help improve the organization of conducting and checking dictations during distance learning and self-training of students.
