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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Joshi, Shivani Kumar, Rajiv Rai, Vipin Dwivedi, Avinash Tripathi, Vikash Chandra G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Nvidia Ai and Learning & Research Center Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model's training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously © 2024 IEEE.

关键词： accuracy Artificial Neural Networks (ANN) exploratory data analysis high-risk patients ICU patient admission medical technology mortality outcomes periodic updates prediction model

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A Two Stream Fusion Assisted Deep Learning Framework for Stomach Diseases Classification

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computers, Materials & Continua 2022年第11期73卷 4423-4439页

作者： Muhammad Shahid Amin Jamal Hussain Shah Mussarat Yasmin Ghulam Jillani Ansari Muhamamd Attique Khan Usman Tariq Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusPakistan Department of Information Sciences University of Education Lahore(Multan Campus)Pakistan Department of Computer Science HITEC University TaxilaPakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary *** technique is sensitive to these ***,fake samples cause AI and DL model to produce diverse *** attacks that successfully implemented in real world scenarios highlight their applicability even *** this regard,minor modifications of input images cause“Adversarial Attacks”that altered the performance of competing attacks ***,such attacks and defensive strategies are gaining lot of attention by the machine learning and security *** use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy(WCE).However,using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding *** a result,it took weeks to generate patients test report,which is tiring and strenuous for ***,researchers come out with the solution to adopt computerized technologies,which are more suitable for the classification and detection of such *** far as the classification is concern,the adversarial attacks generate problems in classified *** days,to handle this issue machine learning is mainstream defensive approach against adversarial ***,this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method(FGSM)and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid *** obtained on the WCE images which are vulnerable to adversarial attack are 96.30%accurate and prove that the proposed defensive model is robust when compared to competitive existing methods.

关键词： WCE images adversarial attacks FGSM noise salt and pepper noise feature fusion deep learning

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Brain Tumor Detection Using ADARN Optimizer 2

Brain Tumor Detection Using ADARN Optimizer

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar, Rajiv Dwivedi, Avinash Joshi, Shivani Tripathi, Vikas Chandra Nvidia Ai Learning & Research Center G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

The use of MRI scans to identify and categorise brain tumors is the main focus of this study. The goal is to develop a precise and reliable method for early identification and accurate categorisation of brain tumours, including pituitary neoplasms, meningiomas, and gliomas. Deep learning methods were utilized to analyze MRI image databases, resulting in a 96% identification accuracy for brain tumors and a 98% categorization accuracy for the three types. This research demonstrates the potential of deep learning techniques for accurate brain tumor identification and categorization. The early identification and precise categorization of brain tumors can assist medical professionals in making informed decisions about the best treatment options for patients, leading to better outcomes and survival rates. The article provides an extensive review of current methods for identifying and classifying brain tumors using MRI data. It emphasizes the critical importance of this field of study in improving patient outcomes and reducing unnecessary treatments. © 2024 IEEE.

关键词： Brain Tumor Classification Convolutional Neural Network identification Image Augmentation MRI Scans

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Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

Genre Classification of Bangla Poem Using Machine Learning a...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Pasha, Syed Tangim Islam, Ashraful Rahman, Mohammed Masudur Ahmed, Eshtiak Ahmed Foysal, Md. Ferdouse Zahangir Alam, Md Daffodil International University Department of Computing and Information System Bangladesh Independent University Center for Computational and Data Sciences Bangladesh Independent University Department of Computer Science and Engineering Bangladesh International Standard University Department of Computer Science and Engineering Bangladesh Daffodil International University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350337617

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%. © 2023 IEEE.

关键词： Semantics

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Time Series Analysis and Forecasting of Air Quality Index of Dhaka City of Bangladesh

Time Series Analysis and Forecasting of Air Quality Index of...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Sakib, Sheikh Rahmatulla Sara, Kamarun Nahar Hossain Rasel, Md. Tahmid Asif, Md. Masudul Islam Nahid, Md. Aynul Hasan Rahman, Md. Saifur Mridha, M.F. Islam, Ashraful Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Independent University Bangladesh Center for Computational and Data Sciences Dhaka Bangladesh

ISBN: (纸本)9798350337617

In Dhaka, the capital city of Bangladesh, various sources including vehicle emissions, industrial activities, brick kilns, building sites, and open rubbish burning contribute to the air pollution problem. To assess the air quality, the Air Quality Index (AQI) is utilized, which categorizes air quality based on pollutant concentration. In this study, we have built ARIMA, Auto-ARIMA, SARIMAX, and VAR models to predict the air quality of Dhaka. Unlike previous studies, we have utilized hourly air pollutants factors such as PM2.5, PM10, SO2, CO, NO2, and O3 to forecast air quality. Our novel approach enables us to predict the monthly and weekly air quality of Dhaka city. Our analysis reveals that the SARIMAX model, which takes into account seasonal patterns, trends, and external factors, is the most accurate in predicting Dhaka city's air quality. The model's prediction performance is assessed using statistical indicators such as mean absolute percentage error and root mean square error. The study highlights that the SARIMAX model could aid policymakers in evaluating the efficacy of air pollution control measures. © 2023 IEEE.

关键词： Air quality

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Efficient Distributed Sequence Parallelism for Transformer-based Image Segmentation

Efficient Distributed Sequence Parallelism for Transformer-b...

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IS and T International Symposium on Electronic Imaging 2024: High Performance Computing for Imaging 2024

作者： Lyngaas, Isaac Meena, Murali Gopalakrishnan Calabrese, Evan Wahib, Mohamed Chen, Peng Igarashi, Jun Huo, Yuankai Wang, Xiao National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Radiology Duke University DurhamNC United States Riken Center for Computational Science Tokyo Japan National Institute of Advanced Industrial Science and Technology Tokyo Japan Department of Computer Science Vanderbilt University NashvilleTN United States

We introduce an efficient distributed sequence parallel approach for training transformer-based deep learning image segmentation models. The neural network models are comprised of a combination of a Vision Transformer encoder with a convolutional decoder to provide image segmentation mappings. The utility of the distributed sequence parallel approach is especially useful in cases where the tokenized embedding representation of image data are too large to fit into standard computing hardware memory. To demonstrate the performance and characteristics of our models trained in sequence parallel fashion compared to standard models, we evaluate our approach using a 3D MRI brain tumor segmentation dataset. We show that training with a sequence parallel approach can match standard sequential model training in terms of convergence. Furthermore, we show that our sequence parallel approach has the capability to support training of models that would not be possible on standard computing resources. © 2024, Society for Imaging science and Technology.

关键词： Image segmentation

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Know-Thy-Basis: Decomposing F26 for Lightweight S-box Implementation

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2024年第4期2024卷 763-794页

作者： Sau, Dilip Sarkar, Sumanta Saha, Dhiman Mandal, Kalikinkar Center for Computational & Data Sciences Indian Institute of Technology Kharagpur India University of Warwick Coventry United Kingdom de.ci.phe.red Lab Department of Computer Science and Engineering Indian Institute of Technology Bhilai India University of New Brunswick FrederictonNB Canada

A recent trend has shown constructions of 6-bit S-boxes that are mostly focused on their cryptographic elegance, while their lightweight aspects have not really been addressed well. This paper attempts to plug-in this existing research gap where we show how the composite structure of the extension field F26 could be leveraged. An earlier well-known example is an efficient implementation of AES S-box using the tower field extension of F28. The case of F2ab is completely different from any tower field as the implementation varies as per the choice of extension – for instance, F(2a)b or F(2b)a, where a and b are prime. Thus, it makes the implementation of S-boxes over F26 = F2(2×3) very interesting. In this work, we systematically study the composite field structure of F26 from a hardware standpoint for a class of S-boxes that are power mapping or their affine equivalents. We analyze the hardware efficiency with respect to different representations of the field extension, i.e., F(22)3or F(23)2. Furthermore, for each extension, we investigate the impact of various choices of bases – for instance, we present the evidence of the effect that normal or polynomial bases have on the implementation. This gives us further insight on the choice of basis with respect to the field extension. In the process, we present a special normal basis, when used in conjunction with F(23)2 results in the least (or very close to least) area in terms of GE for the 18 (6 quadratic and 12 cubic) S-boxes studied in this work. The special normal basis reported here has some algebraic properties which make it inherently hardware friendly and allow us to predict the area reduction, without running a tool. Overall, this work constitutes an extensive hardware characterization of a class of cryptographically significant 6-bit S-boxes giving us interesting insights into the systematic lightweight implementation of S-boxes without relying on an automated tool. © 2024, Ruhr-University of Bochum. All rights

关键词： Cryptography

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CryoBench: diverse and challenging datasets for the heterogeneity problem in cryo-EM 24

CryoBench: diverse and challenging datasets for the heteroge...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Minkyu Jeon Rishwanth Raghu Miro Astore Geoffrey Woollard Ryan Feathers Alkin Kaz Sonya M. Hanson Pilar Cossio Ellen D. Zhong Department of Computer Science Princeton University Princeton NJ Center for Computational Biology and Center for Computational Mathematics Flatiron Institute New York NY Center for Computational Biology and Center for Computational Mathematics Flatiron Institute New York NY and Department of Computer Science University of British Columbia Vancouver BC Canada

ISBN: (纸本)9798331314385

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining high-resolution 3D biomolecular structures from imaging data. Its unique ability to capture structural variability has spurred the development of heterogeneous reconstruction algorithms that can infer distributions of 3d structures from noisy, unlabeled imaging data. Despite the growing number of advanced methods, progress in the field is hindered by the lack of standardized benchmarks with ground truth information and reliable validation metrics. Here, we introduce CryoBench, a suite of datasets, metrics, and benchmarks for heterogeneous reconstruction in cryo-EM. CryoBench includes five datasets representing different sources of heterogeneity and degrees of difficulty. These include conformational heterogeneity generated from designed motions of antibody complexes or sampled from a molecular dynamics simulation, as well as compositional heterogeneity from mixtures of ribosome assembly states or 100 common complexes present in cells. We then analyze state-of-the-art heterogeneous reconstruction tools, including neural and non-neural methods, assess their sensitivity to noise, and propose new metrics for quantitative evaluation. We hope that CryoBench will be a foundational resource for accelerating algorithmic development and evaluation in the cryo-EM and machine learning communities. Project page: https://***.

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CoTinker: Designing a Cross-device Collaboration Tool to Support computational Thinking in Remote Group Work in High School Biology 22

CoTinker: Designing a Cross-device Collaboration Tool to Sup...

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12th Nordic Conference on Human-computer Interaction: Participative Computing for Sustainable Futures, NordiCHI 2022

作者： Musaeus, Line Have Sørensen, Marie-Louise Stisen Kjerstein Palfi, Blanka Sára Iversen, Ole Sejer Klokmose, Clemens Nylandsted Petersen, Marianne Graves Computer Science Aarhus University Denmark Center for Computational Thinking and Design Aarhus University Denmark Department of Computer Science Aarhus University Denmark

ISBN: (纸本)9781450396998

Providing opportunities for students to collaborate while developing their computational thinking (CT) is a challenge, particularly for remote collaboration. To address this issue, an interdisciplinary team derived a set of design rationales (integration of learning activities, scaffolding progression, cross-device interaction and supporting collaboration in all activities) and developed CoTinker: a collaborative, cross-device tool designed to foster equity in the form of equal participation amongst students. Through a pilot study, we investigated high-school students (n=15), working with CT activities supported by CoTinker, in the form of a computer model of a biological phenomenon. We found that CoTinker was able to support students' remote collaborative work and participation equity. Furthermore, students were able to take advantage of cross-device interaction using their laptop and mobile phones in concert. We conclude that the design rationales and CoTinker design can serve as useful starting points for enabling equitable collaboration amongst students working with CT. © 2022 ACM.

关键词： Students

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BEND: BENCHMARKING DNA LANGUAGE MODELS ON BIOLOGICALLY MEANINGFUL TASKS 12

BEND: BENCHMARKING DNA LANGUAGE MODELS ON BIOLOGICALLY MEANI...

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12th International Conference on Learning Representations, ICLR 2024

作者： Marin, Frederikke I. Teufel, Felix Horlacher, Marc Madsen, Dennis Pultz, Dennis Winther, Ole Boomsma, Wouter Enzyme Research Novozymes A/S Department of Computer Science University of Copenhagen Denmark Digital Science & Innovation Novo Nordisk A/S Denmark Department of Biology University of Copenhagen Denmark Computational Health Center Helmholtz Center Munich Germany DTU Compute Technical University of Denmark Denmark

The genome sequence contains the blueprint for governing cellular processes. While the availability of genomes has vastly increased over the last decades, experimental annotation of the various functional, non-coding and regulatory elements encoded in the DNA sequence remains both expensive and challenging. This has sparked interest in unsupervised language modeling of genomic DNA, a paradigm that has seen great success for protein sequence data. Although various DNA language models have been proposed, evaluation tasks often differ between individual works, and might not fully recapitulate the fundamental challenges of genome annotation, including the length, scale and sparsity of the data. In this study, we introduce BEND, a Benchmark for DNA language models, featuring a collection of realistic and biologically meaningful downstream tasks defined on the human genome. We find that embeddings from current DNA LMs can approach performance of expert methods on some tasks, but only capture limited information about long-range features. BEND is available at https://***/frederikkemarin/BEND. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Blueprints

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