109 research outputs found
Data privacy in knowledge discovery
No full textThis thesis addresses data privacy in various stages of extracting knowledge embedded in databases. Advances in computer networking and database technologies have enabled the collection and storage of vast quantities of data. Legal and ethical considerations might require measures to protect an individual's privacy in any use or release of the data. In this thesis, we address the problem of preserving privacy in the two following cases: (1) in distributed knowledge discovery; (2) in situations where the output of a data mining algorithm could itself breach privacy. We present results in two different models, namely secure multiparty computation (SMC) and differential privacy. The first part of the thesis presents privacy preserving protocols in the SMC model. Secure multiparty computation involves the collaborative computation of functions based on inputs from multiple parties. The privacy goal is to ensure that all parties receive only the final output without any party learning anything beyond what can be inferred from the output. Within this framework we address the problem of preserving privacy in the preprocessing and the data mining stages of knowledge discovery in databases. For the preprocessing stage, we present private protocols for the imputation of missing data in a dataset that is shared between two parties. For the data mining stage, we introduce the notion of arbitrarily partitioned data that generalizes both horizontally and vertically partitioned data. We present a privacy-preserving protocol for k-means clustering of arbitrarily partitioned data. We also develop a new simple k-clustering algorithm that was designed to be converted into a communication-efficient protocol for private clustering. The second part of the thesis deals with privacy in situations where the output of a data mining algorithm could itself breach privacy. In this setting, we present private inference control protocols in the SMC model for On-line Analytical Processing systems. In the differential privacymodel, the goal is to provide access to a statistical database while preserving the privacy of every individual in the database, irrespective of any auxiliary information that may be available to the database client. Under this privacy model, we present a practical privacy preserving decision tree classifier using random decision trees.Ph.D.Includes abstractVitaIncludes bibliographical referencesby Geetha Jagannatha
A dangerous but powerful idea - counter acceleration and speed with slowness and wholeness
No full textThe dangerous idea is that school reform, in India in particular, but across the world too, is impossible. Changing education, at the systemic level or at the institutional or school level, or educating teachers and school leaders in change can be classified as largely first order change - that of school improvement, which involves doing more of the same but doing it better (where the focus is on efficiency) and that of school re-structuring, which involves re-organising components and responsibilities (where the focus is on effectiveness). Geetha Narayanan is Principal Investigator with Project Vision at the Centre for Education Research Training and Development (CERTAD) within the Srishti School of Art Design and Technology in Bangalore, India. She has dedicated her career to finding and establishing new models of education that are creative, synergistic and original in their approach to learning. Read the article and listen to audio of the author discussing her ideas
Tetranchyroderma hystrix Remane 1926
No full textTetranchyroderma hystrix Remane, 1926 Records from India. KERALA: Neendakara—Rajan & Nair (1979). Habitat. It has been recorded in well sorted sand with grain size ranging mostly from 295 to 592 µm Remarks. This species has been recorded by Rajan & Nair (1979) from Kerala in their ecological work, along with other gastrotrichs species and meiofauna. There is no drawing and other taxonomic data of this species provided by them or any other author from India. Consequently, we consider this species finding as a doubtful record that require more evidence to prove the presence of this species on the Indian coast.Published as part of Chatterjee, Tapas, Priyalakshmi, Geetha & Todaro, M. Antonio, 2019, An annotated checklist of the macrodasyidan Gastrotricha from India, pp. 495-510 in Zootaxa 4545 (4) on page 503, DOI: 10.11646/zootaxa.4545.4.3, http://zenodo.org/record/261830
Ethical Guidelines for the thoughtful Implementation of AI in Higher Education
No full textAs artificial intelligence (AI) integrates into education systems, concerns regarding its ethics become magnified. This chapter addresses the need for ethical frameworks on AI applications regarding the basic values of education: equity, transparency, and accountability. With rapid AI expansion in teaching and learning, systemic bias, student privacy, and stakeholder responsibilities emerge as burning issues
Recent Advances in Generative AI and Their Impact on Education: Exploring Self-Efficacy in Learning Environments
No full textAbstract:The study analyzes recent advances in generative AI and examines how these technologies interact with student self-efficacy within educational settings. It discusses how innovative AI tools can enrich learning experiences and aims to outline potential roles for these tools in boosting learners’ confidence and motivation.This study emphasis on the comprehensive understanding of how GenAI technology will increase confidence levels of a student into academic work.
Method:A quantitative approach was employed with a sample of 161 students from diverse disciplines. Participants completed a self-efficacy scale and answered items related to their experience with generative AI tools. Statistical analysis were conducted to explore relationships between AI engagement and self-efficacy levels.
Results:The analysis's findings show a positive relationship between the application of generative AI and improved self-efficacy in pupils. The outcomes include increased confidence in learning activities and a stronger willingness to take on difficult tasks.The mean score on the Self-Efficacy (SE) scale was 3.12 , SD = 0.58, indicating that on average, the participants reported a relatively high level of self-efficacy. This suggests a sample of students who, in general, feel confident in their ability to overcome challenges and succeed academically.Regarding generative AI usage, the data showed a wide range of engagement. The mean frequency of use was 3.45, SD = 1.15, with a significant portion of students reporting that they "often" or "very often" use GenAI tools for academic purposes. The most commonly reported uses were for brainstorming ideas (78% of users), followed by drafting outlines (65%) and revising text (55%). A smaller percentage reported using AI for complex tasks such as generating code (28%) or scientific summaries (22%).
Conclusions:The findings suggest that introducing generative artificial intelligence into educational programs might be beneficial to the growth of students; nevertheless, additional research is necessary. The findings of this study highlight the importance of self-efficacy as a potential mediator in learning advances offered by artificial intelligence. Future research should investigate specific uses of artificial intelligence and the ways in which these applications affect the teaching and learning processes
Recent Advances in Generative AI and Their Impact on Education: Exploring Self-Efficacy in Learning Environments
No full textAbstract:The study analyzes recent advances in generative AI and examines how these technologies interact with student self-efficacy within educational settings. It discusses how innovative AI tools can enrich learning experiences and aims to outline potential roles for these tools in boosting learners’ confidence and motivation.This study emphasis on the comprehensive understanding of how GenAI technology will increase confidence levels of a student into academic work.
Method:A quantitative approach was employed with a sample of 161 students from diverse disciplines. Participants completed a self-efficacy scale and answered items related to their experience with generative AI tools. Statistical analysis were conducted to explore relationships between AI engagement and self-efficacy levels.
Results:The analysis\u27s findings show a positive relationship between the application of generative AI and improved self-efficacy in pupils. The outcomes include increased confidence in learning activities and a stronger willingness to take on difficult tasks.The mean score on the Self-Efficacy (SE) scale was 3.12 , SD = 0.58, indicating that on average, the participants reported a relatively high level of self-efficacy. This suggests a sample of students who, in general, feel confident in their ability to overcome challenges and succeed academically.Regarding generative AI usage, the data showed a wide range of engagement. The mean frequency of use was 3.45, SD = 1.15, with a significant portion of students reporting that they "often" or "very often" use GenAI tools for academic purposes. The most commonly reported uses were for brainstorming ideas (78% of users), followed by drafting outlines (65%) and revising text (55%). A smaller percentage reported using AI for complex tasks such as generating code (28%) or scientific summaries (22%).
Conclusions:The findings suggest that introducing generative artificial intelligence into educational programs might be beneficial to the growth of students; nevertheless, additional research is necessary. The findings of this study highlight the importance of self-efficacy as a potential mediator in learning advances offered by artificial intelligence. Future research should investigate specific uses of artificial intelligence and the ways in which these applications affect the teaching and learning processes
Ethical Guidelines for the thoughtful Implementation of AI in Higher Education
No full textAs artificial intelligence (AI) integrates into education systems, concerns regarding its ethics become magnified. This chapter addresses the need for ethical frameworks on AI applications regarding the basic values of education: equity, transparency, and accountability. With rapid AI expansion in teaching and learning, systemic bias, student privacy, and stakeholder responsibilities emerge as burning issues
Advancing cancer care through artificial intelligence: from innovative models to clinical decision-making and regulatory integration
No full textAbstract Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools across the oncology drug development pipeline, spanning from early discovery and compound screening to clinical trial optimization and regulatory oversight. With the growing complexity of cancer biology and the rising demand for precision medicine, AI technologies offer new strategies to accelerate therapeutic innovation, reduce development costs, and improve clinical outcomes. This review synthesizes recent advancements in AI-driven oncology, integrating perspectives from computational modeling, predictive analytics, clinical decision-making, and evolving regulatory frameworks. We highlight how AI-enabled platforms are being employed to identify druggable targets, optimize molecular design, predict drug target interactions, and support preclinical and clinical decision-making. Special attention is given to advanced architectures such as cascade deep forest models, deep learning networks, and the transformative impact of large language models and multimodal AI, including AlphaFold 3, which have demonstrated superior performance in drug target interaction prediction and de novo compound generation. Beyond technical accuracy, we emphasize the importance of “model actionability,” a concept rooted in the ability of AI models to reduce diagnostic and therapeutic uncertainty, thus enhancing their practical value in clinical oncology. Furthermore, we discuss the U.S. Food and Drug Administration’s (FDA) proactive engagement with AI/ML applications, particularly in the context of clinical trials, safety monitoring, and real-world evidence generation. The integration of AI into regulatory science underscores the need for transparency, contextual validation, and risk-based oversight. By bridging scientific innovation with clinical utility and regulatory expectations, this review underscores the pivotal role of AI in advancing oncology drug development and shaping the future of cancer care
Reconceptualizing Higher Education: Challenges of Inclusive Teaching Methods and Digital Innovation in the New Normal
No full textIntroduction: The pandemic has posed unprecedented challenges to healthcare and higher education. It has encouraged faster migration from traditional modes of learning to digital means and called for inclusive and adaptive educational strategies. As institutions begin to redefine their pedagogical approaches, integrating inclusive practices into digital education is paramount for equity and accessibility in this new normal. The intent of this chapter is to investigate the effectiveness of inclusive pedagogical principles within online higher education. The possible issues include teaching methods, learner-centered content delivery, and formative assessments that encourage inclusive e-learning environments in the post-pandemic age.Methods:A qualitative review methodology is employed to synthesize current research and case studies related to inclusive digital teaching practices. Through this analysis, the study considers institutional frameworks that support hybridity in time and place, flexible learning models, and advanced digital pedagogies to examine their effect on teaching efficacy and student inclusivity.Results:The results show that the flexible learning structure and hybrid pedagogical models have fostered learner involvement and developmental engagement in a significative way. Institutions that merge digital innovation with inclusive teaching strategies show better adaptability to and educational outcomes for varied student populations.Conclusions:That study concluded that inclusive digital pedagogy is vital for the establishment of equitable higher education within the new norm. The study recommends that educational leaders intentionally and strategically make decisions that align with long-term inclusive educational objectives. Institutions need to rethink teaching practices to establish accessible, student-centered, and technologically enriched learning environments
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