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2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Dharuman, Gautham Ward, Logan Ma, Heng Setty, Priyanka V. Gokdemir, Ozan Foreman, Sam Emani, Murali Hippe, Kyle Brace, Alexander Keipert, Kristopher Gibbs, Thomas Foster, Ian Anandkumar, Anima Vishwanath, Venkatram Ramanathan, Arvind Data Science and Learning Division United States Argonne Leadership Computing Facility Argonne National Laboratory United States Computer Science Department University of Chicago United States Nvidia Inc. United States California Institute of Technology United States

ISBN: (纸本)9798400707858

Large language models (LLMs) trained on vast biological datasets can learn biological motifs and correlations across the evolutionary landscape of natural proteins. LLMs can then be used for de novo design of novel proteins with specific structures, functions, and physicochemical properties. We employ a pre-trained genome-scale language model that uses codons as tokens and integrate it into a workflow for targeted generation of sequences. Our framework suggests new gene sequences that are ranked for downstream evaluation by metrics that collectively capture extensive sequence-specific, biophysical, and biochemical properties. We demonstrate our integrated workflow to design novel variants of the enzyme, malate dehydrogenase (MDH), that exhibit more favorable activation energies than their natural counterparts (reduction of 4.01 kJ/mol) with sustained sequence generation rates of ~104/hr and simulation rates of ~102/hr on 64 nodes of Polaris with about 99.7 system utilization during the run. © 2023 ACM.

关键词： Physicochemical properties

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Predicting Agricultural Crop Damage Caused by Unexpected Rainfall Using Deep learning

Predicting Agricultural Crop Damage Caused by Unexpected Rai...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Bhushan Fulkar Pawan Patil Gaurav Srivastav Promod Mahale Department of Artificial Intelligence and Data science Faculty of Engineering and Technology Wardha Maharashtra India Artificial Intelligence and Data science Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Efficient allocation of resources and timely agricultural interventions depend on the precise identification of crop loss at the field parcel level. Using recent data from 2018 to 2023, this study investigates the integration of deep learning techniques with real-time field photography to classify agricultural field parcels into those experiencing crop loss and those not. We build and assess deep learning models specifically for crop loss classification using a dataset from the Finnish Food Authority (FFA) that combines on-the-ground photos taken during field visits with comprehensive field parcel information. In this work, we use convolutional neural networks (CNNs) to examine the visual characteristics that we have extracted from field photos in order to identify the subtleties that may indicate crop health or possible loss. Our goal is to improve the model's generalization capabilities over a variety of crop types and agricultural landscapes by training it on a combination of field parcel data and corresponding photographs.

关键词： Deep learning Training Visualization Uncertainty Rain Crops Real-time systems

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Training for the Unexpected: Enhancing Driver Preparedness Through Hazard Awareness

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SSRN 2025年

作者： Kuipers, Jorrit Mulder, Max Voskes, Maran Green Dino BV Netherlands Department of Control & Operations Faculty of Aerospace Engineering Delft University of Technology Netherlands Department of Learning Data Analytics and Technology Faculty of Behavioural Management and Social Sciences University of Twente Netherlands

BackgroundCar driving education in the Netherlands is transforming, and a national curriculum has been introduced to produce safer drivers. This National Curriculum Driver Training B proposes using driving simulators to support practical training and includes hazard examinations to reinforce safe driving, which will be obligatory in Europe. This study aimed to deepen the understanding of the relationships between hazard training and testing on driving simulators and supervised (pre-licensed) and unsupervised (post-licensed) driving. This study builds on Kuipers, De Winter, and Mulder's (2023) research, investigating the relationships between personal characteristics, pre-licence-accompanied driving, self-reported post-licence driving behaviour, and driving performance scores during simulator lessons. Methods We comprehensively analysed simulator data and insights from a questionnaire completed by Dutch car drivers (total n = 2,879) who underwent simulator training between 2008 and 2023. We compared the driving performance of respondents who received hazard training (HT) with the performance of a control group who did not receive HT training in four driving phases: simulator training, on-road training, examination, and post-licensed driving. ResultsWe found distinct differences in performance. Simulator scores, on-road training hours, and test attempts differ significantly. HT increased practical driving education efficiency by 12.4% and reinforced the development of a safe driving style that educational experts recognised and rewarded. In contrast to this positive effect during education, we found no significant effects when driving unsupervised. Results show that whereas drivers think they are sufficiently proficient after obtaining their license, a real improvement in safety performance becomes visible after the first twelve months. This confirms the general finding that young drivers are the most vulnerable to accident involvement in the first year of driving. Re

关键词： Curricula

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Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI data 5

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses. © 2024 IEEE.

关键词： Convolutional neural networks

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Rainfall Prediction using Novel Approach for Enhancing Crop Yield 2

Rainfall Prediction using Novel Approach for Enhancing Crop ...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Seelam, Visweswararao Vikruthi, Sriharsha Bhagavatham, Naresh Kumar Nagendrudu, P.V.V.S.D. Srinivasarao, Goda Chakravarthi M, Ayyappa PACE Institute of Technology and Sciences Dept of CSE India B V Raju Institute of Technology Dept of CSE Telangana Narsapur Medak502313 India Vignana bharathi Institute of Technology Dept of CSE Hyderabad India Aditya University Department of Artificial Intelligence and Machine Learning Surampalem India KL University Vaddeswaram Dept of CSE Andhra Pradesh Guntur India KKR & KSR Institute of Technology & Sciences Dept of CSE-Data Science Andhra Pradesh Guntur India

ISBN: (纸本)9798331519056

In India, agriculture is one of the fastest-growing fields to improve the country's economy. Based on several factors like climatic conditions, predicting rainfall in these stages is one of the most significant tasks. In recent years, unexpected and unpredictable weather conditions have been observed in some places. This study mainly focused on developing the Hybrid algorithm (HA) to predict rainfall in the early stages and find accurate weather conditions. The proposed approach also called as Novel Approach that improves the algorithm's strength by adopting the advanced preprocessing and feature extraction technique. The training model random forest (RF) also used to find the accurate patterns from the given dataset samples. The preprocessing technique focused on cleaning and transforming the raw data to remove the noise from the input dataset, improving the data quality. The proposed approach combines Autoencoders and Linear Regression (LR) to accurately predict rainfall. Extensive experiments are carried out to evaluate the performance of the hybrid algorithm using historical meteorological data from multiple regions with varying climatic conditions. The results show that the hybrid algorithm outperforms traditional methods and individual machine-learning models in terms of accuracy, robustness, and generalization. © 2024 IEEE.

关键词： Adversarial machine learning

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Vehicle Prediction for BUS Identification Output from our Route Testing Real Time Algorithm 5

Vehicle Prediction for BUS Identification Output from our Ro...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Misba, M. Ramya, R. Dickson, Joel Sharmila, L. Kavitha, J. Udayakumar, K. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu India St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India Bethlehem Institute of Engineering Tamil Nadu Karungal India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331530013

Traffic generation in Indian cities is of a mixed nature, comprising over a dozen types of vehicles that can be broadly categorized into slow-moving and fast-moving vehicles. This study highlights the challenges associated with the inefficient operation of the existing bus system. GPS and mobile networks form integral components of the current system, enabling data storage and management using PostgreSQL. The system provides robust and reliable data to fulfill requests from other software applications. Operating on cloud platforms, it is compatible with all major web browsers. The proposed algorithm continuously tracks the real-time location of buses and their corresponding WMS (Web Map Service) location, alerting users through marker notifications. This paper also explores measures to evaluate the efficiency and effectiveness of bus services. Recognizing that an inadequate public transport system impedes economic growth and development, this study emphasizes the need to enhance the overall quality and accessibility of public transportation. The findings aim to improve the operational efficiency of bus services, contributing to a more effective and sustainable transport system. © 2024 IEEE.

关键词： Buses

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Estimating fish weight growth in aquaponic farming through machine learning techniques

Estimating fish weight growth in aquaponic farming through m...

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Intelligent Technologies (CONIT), International Conference on

作者： Purushottam Kumar Pranav Tiwari U Srinivasulu Reddy CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won’t be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions

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Sentiment Analysis Using Machine learning Methods

Sentiment Analysis Using Machine Learning Methods

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2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Badholia, Abhishek Diwan, Tarun Dhar Narooka, Preeti Khatkale, Pravin B Vishnoi, Ankit Kaushik, Keshav Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India Atal Bihari Vajpayee University Bilaspur India Manipal University Artificial Intelligence and Machine Learning School of Computer and Engineering Jaipur India Sanjivani University Maharashtra Kopargaon India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Punjab Amity School of Engineering and Technology Mohali India

ISBN: (纸本)9798350390049

Machine learning (ML) will be utilized to evaluate sentiment analysis in this project. This study aims to do this. Sentiment analysis is a popular natural language processing area. This is a natural language processing subfield. This section identifies textual senses. Due to the growing availability of digital text across platforms, sentiment analysis is now vital for many applications. This is because sentiment analysis has become essential. This technology might be used for market research, political analysis, and consumer feedback. A few instances. This study analyzes several machine learning methods, to name a few. This category includes SVM, Naive Bayes, Random Forest, and RNNs. To clarify, several datasets are used to assess these methodologies. To get more information. Preprocessing and feature engineering are crucial to sentiment analysis model construction, as this paper shows. The research emphasises these two phases. In addition, it provides insights into how these approaches are used in constantly changing digital situations. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving the Quality of Diabetic data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Biradar, Divya Dattangire, Rahul Vaidya, Ruchika Inti, NagaSuryaShivani University of Texas at Arlington Computer Science ArlingtonTX76013 United States Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer and Information Science ArlingtonTX76013 United States

ISBN: (纸本)9798331539948

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The 'Diabetes' data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach's effective impact in correcting and consistent data within the healthcare dataset. © 2024 IEEE.

关键词： Electronic health record

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