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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Prashanth, V. Alabdeli, Haideer Thottan, Sunaina Sangeet Ananthi, S. Saint Leo University Department of Computer Science FL United States Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Dhanalakshmi Srinivasan College of Engineering and Technology Department of Artificial Intelligence and Data Science Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

The identification of real and fake news has become difficult task in the recent years due to more similar feature between them. As the issues of fake news are leading the miscommunication to the reads, various automated software has been introduced for the detection of fake news. The existing models tried to advance the identification of the fake new but failed to reach the expected outcomes due to overfitting issues and limited feature handling abilities. To overcome the existing issues the Latent Dirichlet Allocation based Term Frequency and Inverse Document Frequency with Brown-bear Optimization Algorithm-Bidirectional Long Short-Term Memory (LDA based TF-IDF with BOA-BiLSTM) is developed for the detection of fake news. The news data is collected and get cleaned by tokenization and normalization techniques. Then the LDA based TF-IDF model is used for the extraction of significant details of the news text data. Further the BOA-BiLSTM algorithm is used for the detection of fake news. The developed LDA based TF-IDF with BOA-BiLSTM algorithm has exhibited the better result having accuracy of 0.9489 and recall of 0.9012 for ISOT fake news dataset compared to the existing Knowledge Graphs for multi-domain fake news detection (KG-MFEND) model. © 2025 IEEE.

关键词： Deep learning

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Comparative Evaluation of Several Convolutional Neural Network Architectures in Diabetic Retinopathy Classification

Comparative Evaluation of Several Convolutional Neural Netwo...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Simeon Yuda Prasetyo Nurhasanah Muhammad Fadlan Hidayat Zahra Nabila Izdihar Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The increasing global prevalence of diabetes mellitus has brought diabetic retinopathy (DR) to the forefront as a significant cause of visual impairment and blindness among adults. DR, a complication of diabetes, can be broadly categorized into Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), each comprising distinct clinical stages with varying severity. The accurate and timely detection and classification of these stages are essential for preserving vision, a task that grows in importance as the worldwide diabetes epidemic continues to rise, with an estimated 693 million cases projected by 2045. In response to this healthcare challenge, the development of efficient automated systems for DR diagnosis and classification has emerged as a critical research focus. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have demonstrated substantial promise in addressing this issue. This research presents a comparative evaluation of three prominent CNN architectures, namely Le-Net, AlexNet, and VGG16, in the context of DR classification, aiming to provide valuable insights into their performance. Among these architectures, VGG16 emerges as the standout performer, achieving an accuracy of 74.4%. This outcome underscores the potential of VGG16 as a robust tool for DR diagnosis, with its deeper and more intricate architecture proving adept at capturing intricate retinal features. As the research landscape evolves, future directions may encompass exploring newer model variations, incorporating more extensive and diverse datasets, fine-tuning hyperparameters, and enhancing the interpretability of deep learning models for real-world medical applications, ultimately propelling the field toward more efficient and accurate DR diagnosis and classification.

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Automatic Multiple Choice Question Generation: A Systematic Literature Review

Automatic Multiple Choice Question Generation: A Systematic ...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Matthew Martianus Henry Nur Adhianti Heryanto Mahmud Isnan Kuncahyo Setyo Nugroho Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Despite their drawbacks, multiple-choice questions (MCQ) have been widely used to assess the students' understanding of lectures through examinations. The development of automatic MCQ generation is beneficial, especially for educators. As a starting point for further development, a Systematic Literature Review (SLR) is conducted to uncover current trends, future challenges, and opportunities in automatic MCQ generation. Previously, an SLR was conducted, but it lacks coverage of the utilization of transformer-based models. This SLR covers the development of automatic MCQ generation using either traditional or advanced approaches such as Transformers. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to gather the data from Scopus, IEEE Xplore, SpringerLink, arXiv, and Semantic Scholar. The included articles must be open-access computer science conference papers or journal articles and written in English less than five years ago. Four independent reviewers analyzed the research workflow, evaluation metric, and dataset used in each study. There are 18 included studies, where 17% (n = 3) studies are from 2024, 33% (n = 6) studies are from 2023, 22% (n = 4) studies are from 2022, 11% (n = 2) studies are from 2021, and 17% (n = 3) studies are from 2020. There are 33% (n = 6) of the studies used the traditional feature-based engineering approach, 39% (n = 7) of the studies used the Transformer-based model fine-tuning approach, and the remaining used novel approaches. The study found that BERT variants are the most utilized Transformer-based model in automatic MCQ. The research notes some challenges, but also open various opportunities for further research, including Large Language Model (LLM) utilization for automatic MCQ generation, the utilization BERT-based models for standardized machine-learned evaluation metrics, and the initiative for the creation of an MCQ dataset benchmark.

关键词： Measurement Semantics Reinforcement learning Benchmark testing Ontologies Transformers Question generation Prompt engineering Standards Systematic literature review

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A Vine Copula Model for Climate Trend Analysis using Canadian Temperature data

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Journal of data science 2021年第1期19卷 37-55页

作者： Zhuang, Haoxin Diao, Liqun Yi, Grace Y. Department of Statistics and Actuarial Science University of Waterloo Waterloo ON Canada Department of Statistical and Actuarial Science Department of Computer Science University of Western Ontario Ontario ON Canada

Climate change is widely recognized as one of the most challenging, urgent and complex problem facing humanity. There are rising interests in understanding and quantifying climate changing. We analyze the climate trend in Canada using Canadian monthly surface air temperature, which is longitudinal data in nature with long time span. Analysis of such data is challenging due to the complexity of modeling and associated computation burdens. In this paper, we divide this type of longitudinal data into time blocks, conduct multivariate regression and utilize a vine copula model to account for the dependence among the multivariate error terms. This vine copula model allows separate specification of within-block and between-block dependence structure and has great flexibility of modeling complex association structures. To release the computational burden and concentrate on the structure of interest, we construct composite likelihood functions, which leave the connecting structure between time blocks unspecified. We discuss different estimation procedures and issues regarding model selection and prediction. We explore the prediction performance of our vine copula model by extensive simulation studies. An analysis of the Canada climate dataset is provided. © 2021 The Author(s).

关键词： climate change composite likelihood longitudinal data prediction

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Low-rank Matrix Bandits with Heavy-tailed Rewards

arXiv

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arXiv 2024年

作者： Kang, Yue Hsieh, Cho-Jui Lee, Thomas C.M. Department of Statistics University of California Davis United States Google Department of Computer Science UCLA United States

In stochastic low-rank matrix bandit, the expected reward of an arm is equal to the inner product between its feature matrix and some unknown d1 by d2 low-rank parameter matrix with rank r d1 ∧ d2. While all prior studies assume the payoffs are mixed with sub-Gaussian noises, in this work we loosen this strict assumption and consider the new problem of low-rank matrix bandit with heavy-tailed rewards (LowHTR), where the rewards only have finite (1 + δ) moment for some δ ∈ (0, 1]. By utilizing the truncation on observed payoffs and the dynamic exploration, we propose a novel algorithm called LOTUS attaining the regret bound of order O (d 3 2 r 1 2 T 1 1+δ /Drr)1 without knowing T, which matches the state-of-the-art regret bound under sub-Gaussian noises [22, 25] with δ = 1. Moreover, we establish a lower bound of the order Ω(d δ 1+δ r δ 1+δ T 1 1+δ ) = Ω(T 1 1+δ ) for LowHTR, which indicates our LOTUS is nearly optimal in the order of T. In addition, we improve LOTUS so that it does not require knowledge of the rank r with O (dr 3 2 T 1+δ 1+2δ ) regret bound, and it is efficient under the high-dimensional scenario. We also conduct simulations to demonstrate the practical superiority of our algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Artificial Intelligence and Aesthetic Judgment

arXiv

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arXiv 2023年

作者： Hullman, Jessica Holtzman, Ari Gelman, Andrew Department of Computer Science Northwestern University United States Department of Computer Science University of Chicago United States Department of Statistics Department of Political Science Columbia University United States

Generative AIs produce creative outputs in the style of human expression. We argue that encounters with the outputs of modern generative AI models are mediated by the same kinds of aesthetic judgments that organize our interactions with artwork. The interpretation procedure we use on art we find in museums is not an innate human faculty, but one developed over history by disciplines such as art history and art criticism to fulfill certain social functions. This gives us pause when considering our reactions to generative AI, how we should approach this new medium, and why generative AI seems to incite so much fear about the future. We naturally inherit a conundrum of causal inference from the history of art: a work can be read as a symptom of the cultural conditions that influenced its creation while simultaneously being framed as a timeless, seemingly acausal distillation of an eternal human condition. In this essay, we focus on an unresolved tension when we bring this dilemma to bear in the context of generative AI: are we looking for proof that generated media reflects something about the conditions that created it or some eternal human essence? Are current modes of interpretation sufficient for this task? Historically, new forms of art have changed how art is interpreted, with such influence used as evidence that a work of art has touched some essential human truth. As generative AI influences contemporary aesthetic judgment we outline some of the pitfalls and traps in attempting to scrutinize what AI generated media means. © 2023, CC BY.

关键词： Distillation

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Bioinformatics Analysis of Oral Squamous Cell Carcinomas and Their Interaction to Identify Molecular Signatures 1st

Bioinformatics Analysis of Oral Squamous Cell Carcinomas and...

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1st International Symposium on Artificial Intelligence, ISAI 2022

作者： Sarker, Bandhan Rahaman, Md. Matiur Khan, Suman Sinha, Jayashri Deb Barman, Subhabrata Department of Statistics Faculty of Science Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj-8100 Dhaka Bangladesh Department of Computer Science and Engineering Bengal Institute of Technology and Management West Bengal Santiniketan721657 India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia721657 India

ISBN: (纸本)9783031224843

Squamous epithelium is the origin of solid tumor oral squamous cell carcinoma (OSCC). Every year, nearly 400,000 OSCC patients are added to the cancer database. Presently, chemo-radiotherapy is the main important adjuvant treatment for OSCC;nevertheless, clinical resistance (drug resistance) to chemotherapy still leads to a poor prognosis of OSCC patients. Identification of potential genes and drugs might be a significant lead for the analyses of OSCC research. We aimed to identify molecular signatures for the diagnosis of OSCC patients. Statistical methods (ANOVA, limma and SAM) were used to identify differentially expressed genes (DEGs) from two datasets GSE111585 and GSE115119. Considering the cutoff values less than 0.05 and |log FC| greater than 1, we obtained 27 up-regulated and 25 down-regulated common DEGs. Protein-protein interaction (PPI) network determined hub genes (AR, ETS1, MET, PDGFB and VAV3) using STRING database. Other biomolecules: Reporter transcription factors (HIF1A, MYC, FOXP3, E2F4, WT1, PURA, ZEB1 and USF2), microRNAs (hsa-miR-589-3p, hsa-miR-155-5p and hsa-miR-301b-3p) associated with hub-genes were determined. For the hub genes, we also performed GO and KEGG enrichment analysis. We constructed gene-drug interaction using the DGIdb database and identify targeted drugs (IMATINIB, BROMOCRIPTINE, NIFEDIPINE, PIRETANIDE and TRIAMTERENE) for OSCC. Our study suggested that the biomarkers (hub-genes, TFs and miRNAs) and the discovered drugs might be a therapeutic target of OSCC. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Transcription factors

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Scalable Cosmic AI Inference using Cloud Serverless Computing with FMI

arXiv

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arXiv 2025年

作者： Staylor, Mills Fathkouhi, Amirreza Dolatpour Islam, Md Khairul O’Hara, Kaleigh Goudjil, Ryan Ghiles Fox, Geoffrey Fox, Judy Department of Computer Science University of Virginia United States School of Data Science University of Virginia CharlottesvilleVA United States

Large-scale astronomical image data processing and prediction is essential for astronomers, providing crucial insights into celestial objects, the universe’s history, and its evolution. While modern deep learning models offer high predictive accuracy, they often demand substantial computational resources, making them resource-intensive and limiting accessibility. We introduce the Cloud-based Astronomy Inference (CAI) framework to address these challenges. This scalable solution integrates pre-trained foundation models with serverless cloud infrastructure through a Function-as-a-Service (FaaS) Message Interface (FMI). CAI enables efficient and scalable inference on astronomical images without extensive hardware. Using a foundation model for redshift prediction as a case study, our extensive experiments cover user devices, HPC (High-Performance Computing) servers, and Cloud. CAI’s significant scalability improvement on large data sizes provides an accessible and effective tool for the astronomy community. The code is accessible at https://***/UVA-MLSys/AI-for-Astronomy. © 2025, CC BY-SA.

关键词： Cloud platforms

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Integrating Individual and Social Contexts into Self-Reflection Technologies

Integrating Individual and Social Contexts into Self-Reflect...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Bhattacharjee, Ananya Kulzhabayeva, Dana Reza, Mohi Kumar, Harsh Seong, Eunchae Wu, Xuening Rifat, Mohammad Rashidujjaman Bowman, Robert Kornfield, Rachel Mariakakis, Alex Ahmed, Syed Ishtiaque De Choudhury, Munmun Doherty, Gavin Czerwinski, Mary P. Williams, Joseph Jay Department of Computer Science University of Toronto TorontoON Canada Department of Psychology University of Toronto TorontoON Canada School of Computer Science and Statistics Trinity College Dublin Dublin Ireland Department of Preventive Medicine Northwestern University Evanston United States School of Interactive Computing Georgia Institute of Technology Atlanta United States Microsoft Research Redmond United States

ISBN: (纸本)9781450394222

Technology for promoting reflection can help people better understand their emotions and thought patterns, eventually motivating them to take action to adopt healthy or productive behaviors. However, existing work has often viewed users individualistically, addressing people's behaviors and emotions rather than recognizing the external factors that shape them (e.g., economic status, culture). We envision that the individualistic approaches can be extended and reimagined in ways that can consider such broader contexts. We believe such a shift in the design of interventions will help individuals reflect in a more holistic manner, supporting collaborative reflection processes that involve more than one person. With these aims in mind, we will discuss the two questions in our workshop. First, what individual and social contexts should HCI researchers consider while promoting reflection? Second, what role can various forms of technology (e.g., just-in-time adaptive interventions, peer-support platforms) play in supporting and augmenting reflective practices? Through our workshop, we hope to bring together a community of multidisciplinary researchers and practitioners who aim to design and develop reflection interventions that are situated within the fabric of users' individual and social contexts. © 2023 Owner/Author.

关键词： Reflection

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Fraud Fighters - How AI and ML are Revolutionizing UPI Security

Fraud Fighters - How AI and ML are Revolutionizing UPI Secur...

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science, Technology, Engineering and Management (ICSTEM), International Conference on

作者： S. K. Lokesh Naikl Ajmeera Kiran Vadapally Praveen Kumar Shanmukh Mannam Yesarapu Kalyani Manda Silparaj Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Departmert of Computer science Engineering (Data science) CVR College of Engineering Hyderabad Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering (CS) CVR College of Engineering Hyderabad

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

Unified Payments Interface (UPI) is a highly efficient and securely encrypted monetary mechanism extensively deployed across India for the immediate transfer of funds between various bank accounts of the involved parties. However, with the rise of digital payments, UPI fraud has become a serious problem. Since more and more transactions are taking place online, scammers have discovered new ways to take advantage of holes in the system and steal money from blind people. The consequences of UPI fraud can be devastating for individuals and businesses alike. Not only does it result in financial losses, but it can also damage reputations and erode trust in the system. That's where AI and ML come in. By leveraging the power of artificial intelligence and machine learning, we can effectively detect potentially fraudulent Unified Payments Interface transactions. By using advanced algorithms and machine learning strategies, we can detect as well as prevent fraud more effectively than ever before. The goal of this project is to identify frauds by analyzing public data, managing imbalanced data, adapting to changes in fraud approaches, and minimizing false alarms. The primary goal is to utilize Machine Learning algorithms and Artificial Intelligence that have been recently developed for this objective.

关键词： Machine learning algorithms Accuracy Machine learning Blindness Fraud Cryptography Business

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