Educational system innovative development, innovation management and marketing technologies and tools active improvement, learning technologies improvement and multiplication have become an integral attributes of educational technology of the majority countries in the world. Innovations in educational system development is the basis of a state’s innovative and technological policy. The need to improve educational system and introduce innovative technologies is an essential prerequisite able to ensure countries into the world economic community untrammeled integration. In this (...) context it should be noted that modern information and communication technologies application enables to significantly speed up information search and transfer process, transform mental activity nature, and automate human labor. (shrink)

Delicate Tissue Tumors (STT) are a type of sarcoma found in tissues that interface, backing, and encompass body structures. Due to their shallow recurrence in the body and their extraordinary variety, they seem, by all accounts, to be heterogeneous when seen through Magnetic Resonance Imaging (MRI). They are effortlessly mistaken for different infections, for example, fibro adenoma mammae, lymphadenopathy, and struma nodosa, and these indicative blunders have an extensive unfavorable impact on the clinical treatment cycle of patients. Analysts have proposed (...) a few AI models to characterize cancers, however none have sufficiently tended to this misdiagnosis issue. Likewise, comparative investigations that have proposed models for assessment of such cancers generally don't think about the heterogeneity and the size of the information. Thusly, we propose an AI based approach which joins another strategy of pre handling the information for highlights change, resampling methods to dispense with the predisposition and the deviation of precariousness and performing classifier tests in light of the and Deep learning Algorithm as Artificial brain organization. -/- Tumors (STT). (shrink)

The goal of the study is to analyse the relation between students' digital learning and transversal skills, as well as between students' digital learning and virtues. In the correlative study, 73 teachers of Class 12 of general education institutions participated, filling out a questionnaire in the Google Docs environment. As a result of the theoretical analysis, the criteria for digital learning have been identified -access to digital technologies, cooperation, teachers’ digital competence and availability of activities in (...) compliance with cognitive engagement levels. The findings of the correlation analysis show that the availability of activities in compliance with cognitive engagement levels is the most important in the development of transversal skills,while in the inculcation of virtues -cooperation. The access to digital technologies has a statistically significant correlation with both transversal skills and virtues. The teachers’ digital competence promotes digital literacy and such virtues as responsibility and wisdom. The recommendations developed as part of the study include compilation and promotion of the positive experiences of digital learning at the state and local government level; continuous monitoring and feedback analysis of purposeful digital learning and educational quality at school and classroom level; the application of digital learning resources based on the offer of activities in compliance with the students' cognitive engagement levels at the personal level. Keywords:Digital technologies, cooperation, digital competence, cognitive engagement. (shrink)

Low physics learning in the Philippines is a prevailing concern the education sector must resolve, so initiating technological interventions in teaching the subject, including flipped classrooms, and introducing a chatbot such as ManyChat Delivering Online-Offline learning could be helpful to improve scientific literacy in learning Electromagnetic (EM) waves. This study aimed to determine the significant learning differences between conventional teaching (CT), Flipped Classroom (FC), and ManyChat Delivering Online-Offline (MCDOO) Learning in teaching EM waves. Previous research (...) showed that flipped classrooms are effective in Physics lessons. At the same time, only a few studies have been found using MCDOO learning effectively in other subjects, yet to be in Physics lessons. The researcher used the randomized posttest-only control group experimental research design with four groups; Conventional Teaching (CT) as the control group while the experimental groups are: Flipped Classroom (FC), ManyChat Delivering Online-Offline (MCDOO) Learning, and lastly, the combined FC+MCDOO learning who underwent different learning sessions (traditional teaching, FC session via google classroom, MCDOO session and FC+ MCDOO via Messenger App respectively). One-Way ANOVA was performed to determine whether there were any significant learning differences. The results showed in the first and second competencies; the null hypotheses are accepted, so all the differences in all four groups are not significant there is not much of a difference between the scores of CT and FC, CT and MCDOO, and FC and MCDOO learning; however, in the third learning competency, the null hypothesis is rejected so then the Tukey Post Hoc test was conducted to point out where is/are the significant difference/s. The Tukey Post Hoc test revealed that CT and FC+MCDOO learning differs significantly. It means that FC+MCDOO learning boosts higher learning achievement than conventional teaching, FC alone, or MCDOO alone. This study concludes that using just one technological intervention in Physics teaching simply cannot produce a significant impact on the physics literacy of the students in EM learning. Utilizing a combination of technological approaches can make a meaningful difference to the literacy rate in Physics lessons, specifically electromagnetic waves. (shrink)

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of (...) synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate (...) the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

The use of technology has become an important part of the learning process in and out of the class. Technology continues to grow in importance as a tool to help teachers facilitate language learning for their learners. This study focuses on the role of using new technologies in learning English as a second/foreign language. It discussed different attitudes which support English language learners to increase their learning skills through using technologies. In this paper, the (...) researcher defined the term technology and technology integration, explained the use of technology in language classroom, reviewed previous studies on using technologies in improving language learning skills, and stated certain recommendations for the better use of these technologies, which assist learners in improving their learning skills. The literature review indicated that the effective use of new technologies improves learners’ language learning skills. (shrink)

This study developed and validated E-learning Self-directed Interactive Modules (E-SelfIMo) for Earth Science. The study employed Research and Development method, using the Borg and Gall development procedure, in creating eight e-modules using Kotobee software, evaluating them by experts and students, and determining their effectiveness in terms of students' conceptual understanding. Experts agreed that E-SelfIMo met the DepEd standards for non-printed learning materials, and students attested to their high validity in content, format, and usefulness. Pretest and posttest results for (...) the effectiveness of E-SelfIMo revealed that students exposed to the E-SelfIMo have progressed from “Near Mastery” to “Near Full Mastery” in the chosen Earth Science topics, showing more significant improvement in their conceptual understanding than the control group. This study concludes that E-SelfIMo supports interactive, active, and self-directed learning thereby, improving students’ motivation, engagement, interest, and performance. The E-SelfIMo could be used with technology integration as a teaching and learning tool. -/- . (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify (...) cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. (shrink)

The study aimed to investigate the language learning strategies and their contribution to the language competence of English major students in the teacher education program at Kapalong College of Agriculture Sciences and Technology. The researcher utilized a mixed-method design using the convergent parallel approach. Participants were English major students across all year levels at the college institution, with 204 students randomly selected for the quantitative phase and approximately 10 students purposively selected for the qualitative phase: 5 for in-depth interviews (...) and 5 for focused group discussions. The results indicated that both the level of language learning strategies and language competence were high; the findings were rejected, which reveals that there is significant relationship between them. Further, the findings from both the quantitative and qualitative phases largely converged however, there was one divergent indicator. The study confirmed that language learning strategies significantly impact the enhancement of language competencies among English major students, facilitating effective and efficient language use in realistic contexts. It is recommended that language learners must also prioritize memory strategies despite from the indicators’ lowest mean based from the results, such as mnemonic devices and spaced repetition techniques, to enhance vocabulary and grammar retention. Additionally, to improve speaking competency, students should engage in practical applications of the language within their specific contexts, allowing them to practice and develop fluency, essential for effective communication in professional, social, and academic environments. (shrink)

Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging (...) experts. Biomedical experts and radiologists have resulted in a widening disparity, causing an excess and overwhelming workload on these healthcare professionals (Chen et al., 2021). Several studies indicate that the present-day biomedical radiologist is now saddled with the daunting task of interpreting an image almost every 10 seconds to keep pace with the burgeoning clinical demands (McDonald et al., 2015; Hosny et al., 2018; Lantsman et al., 2022). This cognitive drain has invariably led to inevitable consequences such as delays in diagnosis and an amplified risk of diagnostic errors – thus, the biomedical imaging aspect is in dire need of methods that would aid accurate diagnostics and analytics for improved decision making. In this review, the importance of AI-related technologies such as ML and/or DL methods are reviewed in relation to the processing of medical or biomedical images along with their potentials, challenges, and possible suggestions for future studies in the health landscape. The focus will be on machine learning methods associated with the medical field of image classification. (shrink)

English is essential as an effective communication tool in both local and international contexts. In addition to being used in schools, it is also a teaching tool in colleges and universities. ESL (English as a Second Language) classes are now required in all educational institutions and can't be skipped. When learning a second language, anyone must be physically, mentally, and emotionally involved to communicate and understand what is being said. This systematic review employed qualitative documentary research and adopted content (...) analysis. Following a random selection of reliable and legitimate sources, purposive sampling was used to conduct a content analysis of the selected papers. For this systematic review, sources from EBSCO, Google Scholar, Scopus, Web of Science, and ScienceDirect were used. The findings reveal that English-language obstacles in this article include English learners' writing, speaking, listening, reading, translation, and interpretation skills. The conclusion focuses on English learning obstacles and their improvement through teaching methods and educational technology adoption. The recommendation is to look into quantitative and qualitative research for further study. (shrink)

Coordination is a key problem for addressing goal–action gaps in many human endeavors. We define interpersonal coordination as a type of communicative action characterized by low interpersonal belief and goal conflict. Such situations are particularly well described as having collectively “intelligent”, “common good” solutions, viz., ones that almost everyone would agree constitute social improvements. Coordination is useful across the spectrum of interpersonal communication—from isolated individuals to organizational teams. Much attention has been paid to coordination in teams and organizations. In this (...) paper we focus on the looser interpersonal structures we call active support networks, and on technology that meets their needs. We describe two needfinding investigations focused on social support, which examined four application areas for improving coordination in ASNs: academic coaching, vocational training, early learning intervention, and volunteer coordination; and existing technology relevant to ASNs. We find a thus-far unmet need for personal task management software that allows smooth integration with an individual’s active support network. Based on identified needs, we then describe an open architecture for coordination that has been developed into working software. The design includes a set of capabilities we call “social prompting”, as well as templates for accomplishing multi-task goals, and an engine that controls coordination in the network. The resulting tool is currently available and in continuing development. We explain its use in ASNs with an example. Follow-up studies are underway in which the technology is being applied in existing support networks. (shrink)

Collaborative learning emphasizes student-to-student interaction and the instructor’s role as a facilitator. Collaborative Online International Learning (COIL) was founded in 2005 by the State University of New York (SUNY) to help schools adapt their single classroom courses to an online, collaborative format and establish strong collaborations with professors with whom they would join classes and co-teach using SUNY COIL conferences and website, as well as pre-established partnerships between the institutions. However, as the globe becomes increasingly interconnected, educational challenges (...) aimed at cultivating intercultural competency become more important (Ceo-DiFrancesco & Bender-Slack, 2016). That is why this study aimed to (1) understand the lived experience of students who went under the COIL program in relation to SDG 12, (2) review the new knowledge students obtained when they took part in the COIL program in relation to SDG 12, and (3) discover the challenges students encountered while participating in the COIL program in relation to SDG 12. The researcher surveyed Filipino and Japanese students who participated in the COIL program concerning SDG12 from St. Patrick School of Quezon City, Kwansei Gakuin University, and Meiwa Senior High School. The study employed qualitative phenomenological research, and pertinent data were obtained through an open-ended survey questionnaire. The study was analyzed using Colaizzi’s descriptive phenomenological method. The results showed that the lived experiences of students who went under the COIL program in relation to SD12 have something to do with all their learnings and enjoyable experiences, intercultural and global interactions, and environmental discussions and action plans related to SDG 12. Moreover, students learned and understood better the negative impact of waste on the environment, environmental awareness and practices, the culture and norms in the Philippines and Japan, and the importance and benefits of collaboration. Lastly, the study also revealed the different challenges students encounter while participating in the COIL program in relation to SDG 12, such as cultural differences, language barriers, technical difficulties, and difficulties in experimentation. Thus, it was recommended to learn to adapt to everyone and adjust according to countries’ different cultures, practice and be proficient with the English language, have backup devices and platforms to use to ease technological problems, and improve implementations of experiments. (shrink)

Clinical practice is a part of the integral learning method in nursing education. The use of information and communication technologies (ICT) in clinical learning is highly encouraged among nursing students to support evidence-based nursing and student-centered learning. Through the information-processing lens of the mindsponge theory, this study views performance expectancy (or perceived usefulness) and effort expectancy (or perceived ease of use) as results of subjective benefit and cost judgments determining the students’ ICT using intention for supporting (...) clinical learning, respectively. Therefore, the study examines whether effort expectancy moderates the relationship between performance expectancy and the intention to use ICT among Moroccan nursing students. The Bayesian Mindsponge Framework (BMF) analytics was employed on a dataset of 702 Moroccan nursing students. We found that nursing students’ performance expectancy is positively associated with the intention to adopt ICT and social media, while effort expectancy positively moderates this relationship. Moreover, the model with effort expectancy as the moderator was discovered to have a significantly larger weight than the model with effort expectancy as the direct predictor, validating our information-processing reasoning. This study provides a new perspective on the interplay between the benefit and cost perceptions of ICT use to support clinical learning methods in nursing education. Moreover, given the limited education resources, especially in developing countries like Morocco, we recommend nursing education and training prioritize communicating the benefits of ICTs and social media over ease of use. Doing so would help improve the effectiveness and efficiency of nursing education and training while lowering costs. (shrink)

Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS have (...) revolutionized traditional educational methods and addresses the challenges and limitations, including data privacy and system biases. The discussion also covers future directions in ITS development, highlighting emerging trends and potential advancements. The goal is to provide a thorough understanding of how ITS use AI to enhance educational experiences and suggest avenues for future research in this evolving field. (shrink)

The guiding premise of this chapter is that we, as teachers in higher education, must consider how the content and form of our teaching can foster inclusivity through a responsiveness to neurodiverse learning styles. A narrow pedagogical focus on lectures, textual engagement, and essay-writing threatens to exclude neurodivergent students whose ways of learning and making sense of the world may not be best supported through these traditional forms of pedagogy. As we discuss in this chapter, we, as engineering (...) ethics educators, designed and implemented a new engineering ethics exercise with which we aimed to promote inclusivity at the levels of form and content. At the content level, students were invited to critically engage with inclusivity-undermining ableist assumptions in technology development. This took shape, at the form level, through a hands-on ‘material tinkering’ workshop in which students collaboratively and creatively altered (or ‘hacked’) artifacts used in contexts of disability and healthcare, so as to operationalize values of inclusivity and accessibility. Our hunch was that this hands-on tinkering workshop would simultaneously encourage a meaningful way of engagement with these ethical issues and values, while also enacting a more inclusive learning environment by enriching the range of pedagogical activities and learning formats available to our students.As we aim to show in this chapter, we believe this hunch largely panned out – though there are clear areas for future improvement pertaining to the pilot exercise itself and the research we conducted on the exercise. We begin by offering a description of our tinkering exercise. We discuss the exercise’s source of inspiration (Sect. 16.2.1) and its implementation (Sect. 16.2.2), which is visually captured via photographic documentation. We then discuss (Sect. 16.3) how we utilized a triangulated research method to assess the pedagogical value of the exercise. After we discuss our findings, we conclude by identifying areas for future improvement (Sect. 16.4). (shrink)

It has become a fact that fluency and competency in utilizing the advancement of technology, specifically the computer and the internet is one way that could help in facilitating learning in mathematics. This study investigated the effects of Dynamic Geometry Software (DGS) and Computer Algebra Systems (CAS) in teaching Mathematics. This was conducted in Zamboanga del Sur National High School (ZSNHS) during the third grading period of the school year 2015-2016. The study compared the achievement and attitude towards Mathematics (...) between those students taught with DGS and CAS and those students who were taught without DGS and CAS. A quasi-experimental pretest-posttest control group design was used with two groups which was matched according to their mathematical level. An achievement test was developed and administered to assess the students’ achievement in Mathematics. Aiken’s attitude scale was also used to assess the students’ attitude towards Mathematics. The data were treated with One-Way Analysis of Covariance (ANCOVA) and t-test at 0.05 level of significance. The study revealed a significant difference between the mathematics achievement between students taught with DGS and CAS and those who were taught without it. The integration of DGS and CAS in Mathematics lessons leads to better academic achievement result compared with that of the conventional method of teaching. Moreover, there was no significant difference on the attitude level of the students between the two groups. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify (...) cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We (...) examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. (shrink)

This study aimed to determine the experiences of male and female students on Flexible Learning (FL) during the COVID-19. A qualitative method using a phenomenological approach was used to ascertain the students' experiences, while written questionnaires were utilized to gather the information. The 24 students taking Development Communication and Public Administration were chosen based on set criteria. Both male and female flexible learning participants appreciated its benefits, including convenience and reduced academic stress. They both faced technology-related challenges and (...) suggested improvements. However, differences emerged in their experiences. Males found FL fun, emphasizing family time, while females highlighted skill development and concerns about teaching methods. Females also mentioned practical skill development not emphasized by males. These variations may stem from differing learning preferences and experiences. Variations in male and female experiences in Flexible Learning highlight the need for teachers to tailor instruction, offer mental health support, and incorporate skill development. Improved teacher training and clear guidelines are crucial, as are gender-inclusive program design and regular feedback mechanisms to create equitable and effective educational experiences. (shrink)

How can we improve business ethics education for the twenty first century? This study evaluates the effectiveness of a visual case exercise in the form of a 3D immersive game given to undergraduate students at two UK Universities as part of a mandatory business ethics module. We propose that due to evolving learning styles, the immersive nature of interactive games lends itself as a vehicle to make the learning of ethics more ‘concrete’ and ‘personal’ and therefore more engaging. (...) To achieve this, we designed and built an immersive 3D simulation game in the style of a visual case. The effectiveness of the game was evaluated using a mixed methods approach measuring recognised and adapted constructs from the technology acceptance model. Results demonstrate that students found the game beneficial to their learning of ethics with the development of knowledge and skills applicable to the real world and that they engaged with the process due to game elements. Findings demonstrate the potential for the development of simulated games to teach ethics at all levels and modes of delivery and the contribution of this type of visual case model as a pedagogic method. (shrink)

The guiding premise of this chapter is that we, as teachers in higher education, must consider how the content and form of our teaching can foster inclusivity through a responsiveness to neurodiverse learning styles. A narrow pedagogical focus on lectures, textual engagement, and essay-writing threatens to exclude neurodivergent students whose ways of learning and making sense of the world may not be best supported through these traditional forms of pedagogy. As we discuss in this chapter, we, as engineering (...) ethics educators, designed and implemented a new engineering ethics exercise with which we aimed to promote inclusivity at the levels of form and content. At the content level, students were invited to critically engage with inclusivity-undermining ableist assumptions in technology development. This took shape, at the formal level, through a hands-on ‘material tinkering’ workshop in which students collaboratively and creatively altered (or ‘hacked’) artifacts used in contexts of disability and healthcare, so as to operationalize values of inclusivity and accessibility. Our hunch was that this hands-on tinkering workshop would simultaneously encourage a meaningful way of engagement with these ethical issues and values, while also enacting a more inclusive learning environment by enriching the range of pedagogical activities and learning formats available to our students. As we aim to show in this chapter, we believe this hunch largely panned out - though there are clear areas for future improvement pertaining to the pilot exercise itself and the research we conducted on the exercise. We begin by offering a description of our tinkering exercise. We discuss the exercise’s source of inspiration (2.1) and its implementation (2.2), which is visually captured via photographic documentation. We then discuss (3) how we utilized a triangulated research method to assess the pedagogical value of the exercise. After we discuss our findings, we conclude by identifying areas for future improvement (4). (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, (...) precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using machine learning (...) algorithms. By focusing on the optimization of key parameters, the study aims to improve the predictive power of models and reduce computational costs. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine learning algorithms are (...) improving imaging analysis and reducing diagnostic errors. Furthermore, the paper discusses advancements in genetic screening, liquid biopsies, and biomarkers, emphasizing their potential in identifying high-risk individuals and supporting personalized treatment plans. The paper provides a forward-looking perspective on the future of breast cancer detection, addressing both the opportunities and challenges associated with the adoption of these cutting-edge technologies to improve patient outcomes. (shrink)

In order for students to succeed, especially in times of crisis like the Covid 19 Pandemic, they must be trained to be self-sufficient in their language studies. This research investigates using a self-access language learning strategy in an emergency virtual reading class during the covid 19 pandemics to improve language learners' Autonomy. It employed a descriptive correlational research design. The study involved 89 randomly selected language students in one University of Peru. Results of the study showed that the students (...) can utilize internet as their learning resource using devices they have. This shows that all of the students have devices to be used for their online and blended language learning. It is interesting to note the all the sampled students have access to internet for their online language learning. Hence the students have a motivated level of learning Autonomy in reading classes. As to the assessment of internet self-access reading skills of the students. This shows that the students have a favorable attitude towards using the internet in their reading lessons. It shows that the students have a fair level of technology acceptance and utilization for their language learning. Self-Access Skills in Reading was shown to be closely linked to Students' Learning Autonomy. This suggests that the relevant factors in the research have a positive correlation. This means that the students are more likely to adopt online self-access reading skills if they have a good attitude about learning Autonomy. This result shed light on the gap in addressing students' online language learning engagement in the COVID 19 era, particularly in the context of Peru. (shrink)

Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using machine learning (...) algorithms. By focusing on the optimization of key parameters, the study aims to improve the predictive power of models and reduce computational costs. (shrink)

This study investigated the impact of collaborative learning on mathematics achievement and attitudes in sixth-grade students, comparing it to traditional didactic teaching. A quasi-experimental research design was utilized in which sixth-grade students were randomly assigned to either control or experimental groups. Pre- and post-tests assessed mathematics achievement using curriculum-aligned tests. In addition, attitudes toward mathematics were measured using the ‘attitude towards mathematics’ inventory developed by Tapai and Marsh in 2004. Both groups exhibited similar pre-test levels. The experimental group received (...) collaborative learning, while the control group received traditional teaching. Post-tests after a 12-week intervention showed significant improvements in the experimental group’s mathematics achievement, regardless of initial achievement levels. Positive changes in attitudes toward mathematics were also observed in the experimental group, with some progress in the control group. Collaborative learning appears promising for enhancing mathematics achievement and nurturing positive attitudes in elementary students. (shrink)

COVID-19 began in China in December 2019. As of January 2021, over a hundred million instances had been reported worldwide, leaving a deep socio-economic impact globally. Current investigation studies determined that artificial intelligence (AI) can play a key role in reducing the effect of the virus spread. The prediction of COVID-19 incidence in different countries and territories is important because it serves as a guide for governments, healthcare providers, and the general public in developing management strategies to battle the disease. (...) The prediction proved beneficial in the early months of 2020 to alert nations and territories in danger of an outbreak, allowing them to take preventative measures. Despite the fact that the COVID-19 outbreak has expanded to practically every location on the planet, the prediction has value in terms of monitoring the intensity of the spread and recovery, as well as determining the likelihood of a sequel or tertiary epidemic. COVID-19, like influenza, could become a seasonal or recurring epidemic in the future. COVID-19 activity must so be predicted and monitored now and in the future. (shrink)

Digital Literacy (DL) is defined as the ability to use information and communication technology to communicate with cognitive and technical skills. One of the Digital Literacy is Mobile-Assisted Language Learning (MALL) or mobile phones-based language learning. Merits of this study are worthy of helping learners easier understand the language learning materials presented by either guided face to face in the classroom or self-learning out of the school. The study used experimental and control classes to compare the (...) results that the significance of Mobile-Assisted Language Learning in learning language could be prescribed. Refer to the initial students’ ability, this study designed pre and post-test for the experiment. Furthermore, participants were 40 students of second-year students at the English Education Department. Same topics were taught for experiment and control class in alter methods. The results claimed that the using post-test of MALL had merits on improving experimental group more than control group performance. The results declared that using MALL would encourage students learning activities such as self-language learning, enriching vocabulary, promoting teacher’s pedagogic, student’s cognitive, writing skill, reading comprehension, speaking, grammar and listening skill. Lastly, this paper implied MALL as a reference for future curriculum and material development. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized (...) through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized (...) through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might (...) be made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve (...) response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We (...) examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements and strengthening ABKS against emerging security vulnerabilities are future research priorities. (shrink)

The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in real-time analytics.

The object of the present study is to propose a technologically-based method for developing Regulation of Cognition (RC) among pre-service teachers in a pedagogical problem context. The research intervention was carried out by two groups during a Teaching Training Workshop, based on the IMPROVE instructional method, which was implemented in the Learning Management System (LMS). The first group (N=53) investigated the pedagogical problems with "dual perspectives (teacher and learner), and the other group (N=47) analyzed the same problems from a (...) teacher perspective only. The triangulated research design provided three sets of data of RC (e.g. statements on Metacognitive Awareness Inventory, Educational Data Mining, and observations on actual teaching). The results were indicative of the advantage that was obtained by the dual perspective group (LMS+2P), which has manifested in most components of RC, as compared with the single based intervention (LMS+1P). (shrink)

In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate (...) recognition. In this work to resolve the above problem, automatic Speech enhancement shows that deep learning techniques effectively eliminate background noise. Using Deep leaning models for four states were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers. The proposed RNN method could be implemented in personal assistant systems to give better and more appropriate state-based interactions between humans. In the simulation results shows Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method. -/- . (shrink)

he health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high (...) accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care.

This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive (...) search improvements a. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, (...) precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high (...) accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. A. (shrink)

Technology-enhanced learning settings are changing quickly and complexly in the contemporary digital era, making it possible for students with disabilities to learn more effectively than before. The words "multisensory" and "media" together, however, suggest that this strategy entails incorporating several sensory modalities in educational media to improve learning experiences for children with disabilities. It can entail integrating visual, aural, tactile, and kinesthetic elements to meet various learning requirements and styles. This article examines how Mulsemedia, one of these (...) cutting-edge technologies, enhances learning methodologies, improving the teaching and learning of unique pedagogies and emphasizing teaching and learning-related modules for students with special needs. The researchers used a qualitative research design to understand and review the ten papers with secondary data based connected to mulsemedia in special education: A novel teaching approach for the next generation. The article also describes the extremely encouraging outcomes of case studies conducted with engineering students with disabilities in several schools in India. This critical article finds that multimedia-enhanced instruction significantly improves special students' learning experiences and ability to learn new information for future development. (shrink)

Concept maps are found to be useful in eliciting knowledge, meaningful learning, evaluation of understanding and in studying the nature of changes taking place during cognitive development, particularly in the classroom. Several experts have claimed the effectiveness of this tool for learning science. We agree with the claim, but the effectiveness will improve only if we gradually introduce a certain amount of discipline in constructing the maps. The discipline is warranted, we argue, because science thrives to be an (...) unambiguous and rigorously structured body of knowledge. Since learning science may be seen as a process where a novice is expected to be transformed into an expert, we use the context of learning science for making the proposal. Further, we identify certain anomalies in the evaluation of concept maps, and suggest that the evaluation should be based on semantics of the linking words (relation types) and not on graphical criteria alone. (shrink)

The proliferation of Internet of Things (IoT) technologies presents a promising avenue for fostering inclusive societies through community development and education initiatives. This paper explores the potential of leveraging IoT to address societal inequalities and empower marginalized communities. Through a multidisciplinary lens, the paper examines the intersection of IoT, community development, and education, elucidating how IoT-enabled solutions can contribute to building more resilient, connected, and equitable societies. By harnessing IoT devices, sensor networks, and data analytics, community development efforts can (...) be augmented, enabling better resource allocation, infrastructure management, and service delivery. Additionally, IoT-enabled educational interventions offer opportunities to enhance learning experiences, improve access to educational resources, and promote lifelong learning for individuals of all backgrounds. However, alongside the promise of IoT, ethical considerations surrounding data privacy, security, and digital divide must be addressed to ensure equitable access and participation. Through case studies and best practices, the study highlights successful examples of IoT implementations in community development and education, while also identifying key challenges and opportunities for future research and practice. Finally, the study advocates for a collaborative and inclusive approach to leveraging IoT for community development and education, emphasizing the importance of empowering individuals and communities to participate actively in shaping their futures. Keywords: Internet of Things, Community development, Education, Inclusive societies, Sensor. (shrink)