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AI-Driven CASGN Framework for Evaluating Descriptive Answers in Online Education

AI-Driven CASGN Framework for Evaluating Descriptive Answers...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sridevi Ponmalar, P. Vinodini, S. Srithar, V. Morankar, Gouri Jebaseelan, S. D. Sundarsingh Harikrishnan, R. School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai India Chennai600119 India PeriyarManiammai Institute of Science & Technology Department of Computer Applications Vallam Thanjavur India Shri Ramdeobaba College of Engineering and Management Nagpur India Sathyabama Institute of Science and Technology Department of Eee Chennai119 India Symbiosis Institute of Technology Symbiosis International Deemed University Pune Campus Pune412115 India

ISBN: (纸本)9798331543617

There is an enormous demand for automated assessment systems, which can handle the bulk assessments efficiently and effectively. While existing systems perform well in grading objective questions, they break down with subjective or descriptive answers that require a deeper analysis of context and understanding. To address this challenge, we have proposed the AI-Enhanced Context-Aware Semantic Grading Network-state-of-the-art algorithm for instant auto-descriptive response scoring in e-assessments. The AI-CASGN utilizes state-of-the-art AI technologies, particularly NLP and deep learning models-very specifically transformer architectures-during context-based semantic analysis. Beyond mere keyword matching, the system scores what students say and how relevant it is. This takes the concept of grading to an even more fine-tuned art. Scores change with depth and accuracy in the answer given to show complete evaluation. With the dynamic grading framework, adaptation on scores both regarding the accuracy and depth of the answer for a precise evaluation is possible. Inbuilt adaptive learning within the system performance evolves day by day by learning from the new data. AI-CASGN is designed to handle biases in mitigation by considering that there may be different patterns for a response to ensure that responses with varied wording but conceptually correct are fairly graded. It will have to deal with the volumes at scale, hence it is envisaged to process large volumes of responses in minimum time and with high accuracy to be deployed in real time in any educational institution. In future, the system will also provide personalized feedback to students for the purpose of continuous learning and improvement. This represents a scalable, real-time, unbiased tool that assesses subjective answers in modern online learning environments. © 2024 IEEE.

关键词： Semantics

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Leveraging Internet of Things (IoT) Sensors and Deep Learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep Learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep Learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep Learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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A Voice Enabled Grammar Correction System using Transformers 3

A Voice Enabled Grammar Correction System using Transformers

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Jain, Prakhar Bahirat, Nishtha Yashwantrao Shibi, C.Sherin Babitha Lincy, R. Jency Rubia, J. Arunarani, A.R. SRM Institute of Science and Technology School of Computing Department of Computational Intelligence Tamil Nadu Chengalpattu Kattankulathur India Sri Eshwar College of Engineering Computer and Communication Engineering Department Tamil Nadu Coimbatore India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of ECE Tamil Nadu Avadi India

ISBN: (纸本)9798350372120

This paper introduces a groundbreaking real-time grammar error detection and correction system (named 'Error Ears') that adapts to linguistic interactions. At the core of the proposed approach lies the utilization of the potent 'T5' model, renowned for its capability to comprehend and produce human-like text. The proposed workflow meticulously covers essential stages including data pre-processing, model training, and fine-tuning. By fine-tuning the 'T5' model on a diverse dataset of sentences with numerous grammatical errors, the goal is to empower it to distinguish complex contextual nuances in speech. Preliminary results indicate that 'Error Ears' represents a substantial advancement over traditional correctional systems. This system excels at swiftly identifying and rectifying intricate spoken language errors, encompassing structural and contextual details. What sets it apart is its ability to provide real-time contextual suggestions, rendering it especially valuable during virtual meetings, presentations, and everyday conversations. 'Error Ears' is poised to redefine spoken language communication standards by enhancing clarity and precision. Its potential utility extends across a spectrum of contexts and linguistic environments, ensuring more efficient and accurate oral communication. In the digital age, this study opens up new avenues for a better comprehension and expression of language, resulting in more effective and transparent communication. © 2024 IEEE.

关键词： Data handling

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Salt Deposits Identification Using Enhanced U-net-based Mixed Attention Loss Function for Semantic Segmentation 2

Salt Deposits Identification Using Enhanced U-net-based Mixe...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Anitha, D. Al-Attabi, Kassem Priyanka, M.S. Soujanya, Thota Bhuvaneshwarri, I. Srm Institute Of Science And Technology School Of Computing Department Of Computational Intelligence Tamilnadu Kattankulathur India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of AI-ML Bengaluru India Sr University School Of Agricultural Sciences Telangana Warangal India Government College Of Engineering Department Of Information Technology Tamil Nadu Erode India

ISBN: (纸本)9798350381665

Numerous areas of Earth's surface with huge accumulations of oil and gas have high salt deposits under surface. Determining such deposits assists several countries in enhancing the storage capacity of petroleum reserves and exploring new ones. However, determining such a deposit is challenging because expert seismic images need human interpretation of salt bodies, which causes biased and greatly variable translations. This research proposes the Enhanced U-net with Mixed Attention Loss Function (EU-net-based MALF) to automatically and accurately identify whether or not the seismic images have salt deposit regions. Initially, the images are gathered from Tomlinson Geophysical Services (TGS) dataset to evaluate the model performance. Then, the nearest neighbor interpolation method and min-max normalization are employed for image resizing and normalizing. At last, the EU-net-MALF is performed for semantic segmentation. The EU-net-MALF achieves a better accuracy of 98.26% compared to existing methods like fully convolutional DenseNet, U-Net and deep supervision, and Interactive Salt Segmentation based on Convolutional Neural Network and GraphCut (SaltISCG). © 2024 IEEE.

关键词： Image enhancement

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Revolutionizing Aquaculture Practices: IoT and Deep Learning-based Remote Monitoring for Sustainable Fish Farming 5

Revolutionizing Aquaculture Practices: IoT and Deep Learning...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Chelladurai, S. Prince Shiny, G.Susan Jeevitha, S.V. Swapna, P. Pavithra, R. Arulraj, S. Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Chennai Kattankulathur India Sethu institute of Technology Department of information technology Kariapatti Virudhunagar Tamil Nadu Pulloor626115 India Bannari Amman Institute of Technology Department of information technology Erode India Velammal college of engineering and technology Department of Cse Madurai India Rathinam Technical Campus Department of Ece Coimbatore India

ISBN: (纸本)9798331504403

Aquaculture plays a vital role in meeting the growing demand for seafood while addressing sustainability concerns in fisheries. To optimize fish growth and ensure efficient resource utilization an innovative IoT and Deep Learning-based remote monitoring system is proposed tailored for aquaculture operations. Our system integrates sensor technology, IOT connectivity, and Deep Learning algorithms to provide real-time monitoring and control of key environmental parameters such as water temperature, pH levels, oxygen concentration, and feeding behavior. By leveraging a network of sensors deployed in aquaculture facilities, our system collects and analyzes environmental data to optimize aquaculture practices and ensure optimal conditions for fish growth. Through a user-friendly interface accessible via web or mobile applications, aqua-culturists can remotely monitor the status of their facilities and adjust operational parameters as needed. Additionally, our system utilizes Deep Learning algorithms to analyze historical data and predict optimal feeding schedules, thereby enhancing feed efficiency and fish growth rates. Field trials demonstrate the effectiveness of our IOT and Deep Learning-based monitoring system in improving fish health and productivity while reducing environmental impacts and operational costs. Our findings highlight the potential of IoT and Deep Learning technologies in revolutionizing aquaculture practices, promoting sustainability, and enhancing food security in fisheries © 2024 IEEE.

关键词： Fish

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Real-Time Pill Identification for the Visually Impaired Using Deep Learning 6

Real-Time Pill Identification for the Visually Impaired Usin...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Dang, Bo Zhao, Wenchao Li, Yufeng Ma, Danqing Yu, Qixuan Zhu, Elly Yijun San Francisco Bay University Computer Science FremontCA United States School of Software Engineering University of Science and Technology of China Anhui Hefei China School of Electronics and Computer Science University of Southampton Southampton United Kingdom Stevens Institute of Technology Computer Science HobokenNJ United States College of Computing Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798350353174

The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. Utilizing the YOLO framework, the application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices. The system incorporates Text-to-Speech (TTS) to provide immediate auditory feedback, enhancing usability and independence for visually impaired users. Our study evaluates the application's effectiveness in terms of detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community. © 2024 IEEE.

关键词： Deep Learning Healthcare Mobile Application Pill Identification Visual Impairment YOLO Framework

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Survey on Finger Vein Recognition using Convolutional Neural Network 2

Survey on Finger Vein Recognition using Convolutional Neural...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Pandey, Surya Prabhu, Nagendra Kumar, Adithya Suresh New Horizon College of Engineering Computer Science and Engineering Bangalore India Srm Institute of Science Technology Kattankulathur School of Computing Department of Computational Intelligence Chennai603203 India

ISBN: (纸本)9781665400527

Vein identification, also known as vascular technology, it is a biometric identification approach that analyses the blood patterns visible on the skin's surface. Despite the fact that it is used by FBI and CIA for community security and criminal investigations, such as forensic investigation and law enforcement, this recognition method is still in development. Vein recognition technology offers great solutions to challenges. Some challenges are ID or password leakage and then reusing it, lost smart cards or keys. Finger Vein Recognition can become most promising for high security applications. Our paper explains about the different cutting-edge machine learning algorithms that are applied so far. At the end, a comparative analysis is provided. © 2022 IEEE.

关键词： Machine learning

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Mandatory Automated Safety Kernel (MASK): Face Mask Recognition Using Deep Learning Computer Vision 2

Mandatory Automated Safety Kernel (MASK): Face Mask Recognit...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Dinesh, G. Singh, Amarjeet Shinz, Nehar Sravanya, Mogalluru Venkata Agrawal, Gaurangi Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9781665400527

This paper involves utilizing modules to create an efficient and accurate machine learning face mask detection model that can give an accuracy close to 100 percent and a value loss of less than 5%. This paper sees up to the fact that it is the local clusters that need to take care of themselves and this is what will eventually help the society on the larger scale as a whole and aims to aid the concerning authorities in keeping their crowd wearing masks at their respective premises. This is done by programming a system that can recognize the visuals from a camera at the entrance of such premises flagging offenders who do not wear a face mask and notifying authorities as it happens. This can be implemented in private organizations like educational institutions or company offices making it possible to combine it with a facial recognition model thereby notifying the offenders themselves via their cell phones by fetching their information from database resources that would be already existing in the case of these private organizations. © 2022 IEEE.

关键词： Face recognition

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Retrieval Augmented Generation for Liquid Sodium Facility Documentation Processing

Retrieval Augmented Generation for Liquid Sodium Facility Do...

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2024 Transactions of the American Nuclear Society on Winter Conference and Expo, ANS 2024

作者： Prantikos, Konstantinos Pallikaras, Venetis Pantopoulou, Maria Tsoukalas, Lefteri H. Heifetz, Alexander School of Nuclear Engineering Purdue University West LafayetteIN47906 United States Nuclear Science and Engineering Division Argonne National Laboratory ArgonneIL60439 United States College of Computing Georgia Institute of Technology AtlantaGA30332 United States

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Agentic Retrieval Augmented Generation for Advanced Reactor Thermal Hydraulic System

Agentic Retrieval Augmented Generation for Advanced Reactor ...

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2024 Transactions of the American Nuclear Society on Winter Conference and Expo, ANS 2024

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