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arXiv 2023年

作者： Liu, Xiaojuan Li, Maojun Yin, Tao School of Mathematical Sciences University of Electronic Science and Technology of China Sichuan Chengdu611731 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper studies a non-singular coupling scheme for solving the acoustic and elastic wave scattering problems and its extension to the problems of Laplace and Lamé equations and the problem with a compactly supported inhomogeneity is also briefly discussed. Relying on the solution representation of the wave scattering problem, a Robin-type artificial boundary condition in terms of layer potentials whose kernels are non-singular, is introduced to obtain a reduced problem on a bounded domain. The wellposedness of the reduced problems and the a priori error estimates of the corresponding finite element discretization are proved. Numerical examples are presented to demonstrate the accuracy and efficiency of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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IoT-Enhanced Path Planning and Obstacle Avoidance for Autonomous Mobile Robots

IoT-Enhanced Path Planning and Obstacle Avoidance for Autono...

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Advancement in Technology (ICONAT), International Conference for

作者： N Selvanathan Sr Ramya C Sureshkumar R. Kiruthiga J Akshya Mani Deepak Choudhry M. Sundarrajan Department of Information Technology Sona College of Technology Salem Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Department of Information Technology KGiSL Institute of Technology Coimbatore Department of AI&DS Bannari Amman Institute of Technology Erode Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Path planning and obstacle avoidance play a key role in how well autonomous mobile robots work and stay safe when they move through tricky areas. Traditional path-planning methods often can’t keep up when new obstacles pop up or existing ones change, which makes them less useful in the real world. To fix these problems, the proposed research work brings in a new IoT-powered path planning and obstacle avoidance system that uses the $\mathrm{D}^{*}$ Lite and Deep Reinforcement Learning (DRL) algorithms. Our approach brings together D* Lite’s quick replanning skills with DRL’s ability to learn and adapt made even better by live data from IoT sensors. We’ve put our model through its paces in different simulated environments set up to be as tricky as real-world navigation. Quantitative analysis shows it’s a big step up from older models. Adding IoT data leads to a 30% boost in how paths are planned and cut the time robots need to get through complex routes by 20%. Plus, using DRL helps robots deal with moving obstacles 25% better, which means fewer potential crashes. This is a big step forward in the field of autonomous robotics. It gives a robust answer that would not just be reactive to the given environmental inputs but also, through proactivity, prompt possible obstacles, further contributing to smoother and safer output. As we move along with the development of our methodology, we foresee broad applications in many sectors enhancing the autonomy and efficiency of robotic systems working in dynamic and unpredictable environments.

关键词： Measurement Adaptation models Navigation Statistical analysis Heuristic algorithms Robot sensing systems Path planning Mobile robots Collision avoidance Autonomous robots

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A Voice Enabled Grammar Correction System using Transformers

A Voice Enabled Grammar Correction System using Transformers

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

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

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

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.

关键词： Adaptation models Accuracy Speech recognition Oral communication Linguistics Web conferencing Transformers

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Identification of Cyberbullying and Finding Target User's Intention on Public Forums

Identification of Cyberbullying and Finding Target User's In...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Lalitha Krishnasamy Kiruthika M Manjunatha Swamy C Vigneshwaran T Sharon Roji Priya C D. Vijay Anand Department of CSE School of Engineering and Technology Christ University Bengaluru India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Information Technology Kongu Engineering College Erode India

Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study.

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Identifying Transient candidates in the Dark Energy Survey using Convolutional Neural Networks

arXiv

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arXiv 2022年

作者： Ayyar, Venkitesh Knop, Robert Awbrey, Autumn Andersen, Alexis Nugent, Peter Lawrence Berkeley National Laboratory 1 Cyclotron Rd BerkeleyCA94720 United States Hariri Institute for Computing and Computational Science and Engineering Boston University BostonMA02215 United States Department of Astronomy University of California Berkeley BerkeleyCA94720 United States

The ability to discover new transient candidates via image differencing without direct human in-tervention is an important task in observational astronomy. For these kind of image classification problems, machine Learning techniques such as Convolutional Neural Networks (CNNs) have shown remarkable success. In this work, we present the results of an automated transient candidate identification on images with CNNs for an extant dataset from the Dark Energy Survey Supernova program (DES-SN), whose main focus was on using Type Ia supernovae for cosmology. By performing an ar-chitecture search of CNNs, we identify networks that efficiently select non-artifacts (e.g. supernovae, variable stars, AGN, etc.) from artifacts (image defects, mis-subtractions, etc.), achieving the efficiency of previous work performed with random Forests, without the need to expend any effort in feature identification. The CNNs also help us identify a subset of mislabeled images. Performing a relabeling of the images in this subset, the resulting classification with CNNs is significantly better than previous results, lowering the false positive rate by 27% at a fixed missed detection rate of 0.05. © 2022, CC BY.

关键词： Convolution

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Comprehensive Analysis of Arabic Sentiment Analysis using Lexicon and Machine Learning based Approaches

Comprehensive Analysis of Arabic Sentiment Analysis using Le...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： M. Maheswari Ammar Hameed Shnain Ayesha Siddiqua H C Nagaraj N. Shilpa Department of Computational Intelligence School of Computing SRM Institue of Science and Technology Kattankulathur India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of ECE School of Engineering SR University Warangal India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Arabic sentiment analysis (ASA) is a vital field in Natural Language Processing (NLP) that utilizes Machine Learning (ML) techniques to understand the complex emotional tones that are present in Arabic text. This survey discovers the use of ML in Arabic sentiment analysis, which aims to implement various methodologies for sentiment classification. The goal is to develop a robust model capable of translating nuanced sentiments expressed in different dialects and intricate linguistic structures. The methodology involves creating a labeled dataset, using preprocessing techniques, and utilizing machine learning algorithms such as linear classifiers and deep learning models. By extracting features, training the model, and evaluating its performance, the methodology improves the accuracy of sentiment evaluations. The results of this study highlight the effectiveness of machine learning models in accurately categorizing sentiments in Arabic text, as demonstrated by performance metrics such as precision, recall, and accuracy.

关键词： Surveys Training Support vector machines Measurement Sentiment analysis Accuracy Social networking (online) Reviews Feature extraction Standards

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Autonomous Decision-Making in Cyber-Physical Systems Using Double Deep Q-Learning and Multi-Agent Collaboration

Autonomous Decision-Making in Cyber-Physical Systems Using D...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Divyabarathi P Anbarasan R. Vasanthi R B. Leena Akshya J M. Sundarrajan Mani Deepak Choudhry Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Information Technology KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Bharathidasan Engineering College Thirupathur Tamil Nadu India Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The autonomous decision-making capabilities of cyber-physical systems (CPS) establish key elements that optimize the management of resources while distributing tasks and maximizing system operational efficiency. Deep Q-Learning (DQN) represents one approach along with Twin Delayed Deep Deterministic Policy Gradient (TD3) and Multi-Agent Proximal Policy Optimization (PPO) that demonstrates capability in handling decision tasks. The existing models face multiple implementation challenges including inaccurate Q-value estimations weak multi-agent relationships and restricted scalability for dynamic systems. The inadequate performance of present methods for energy efficiency and latency alongside poor collaborative coordination led to the necessity of developing a new approach. The new framework DDQN-MAC takes double-deep Q-Learning and combines it with multi-agent collaboration technology to enhance decision-making abilities specific to challenging environments. The method solves overestimation bias through separate action selection and evaluation processes that use multi-agent collaboration for dynamic task distribution and resource control. The designed model demonstrates enhanced performance in multiple critical evaluation measures. Benchmarks indicate that DDQN-MAC attained a superior completion rate of 95.4% while demonstrating reduced latency performance at 12.5 ms versus the other methods DQN (18.7 ms) and Multi-Agent PPO (92.3%). The research provides fundamental groundwork to guide upcoming investigations in hybrid model development while advancing complex multi-agent system capabilities for enhanced autonomous navigational choices within variable operational environments.

关键词： Q-learning Decision making Collaboration Estimation Cyber-physical systems Dynamic scheduling Energy efficiency Resource management Vehicle dynamics Optimization

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Optimal-rate error estimates and a twice decoupled solver for a backward Euler finite element scheme of the Doyle-Fuller-Newman model of lithium-ion batteries

arXiv

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arXiv 2024年

作者： Xu, Shu Cao, Liqun School of Mathematical Sciences Peking University Beijing100871 China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Scientific and Engineering Computing Beijing100190 China National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences Beijing100190 China

We investigate the convergence of a backward Euler finite element discretization applied to a multi-domain and multi-scale elliptic-parabolic system, derived from the Doyle-Fuller-Newman model for lithium-ion batteries. We establish optimal-order error estimates for the solution in the norms l2(H1) and l2(L2(Hrq)), q = 0,1. To improve computational efficiency, we propose a novel solver that accelerates the solution process and controls memory usage. Numerical experiments with realistic battery parameters validate the theoretical error rates and demonstrate the significantly superior performance of the proposed solver over existing solvers. Copyright © 2024, The Authors. All rights reserved.

关键词： computational efficiency

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An Efficient Method for Seeds Classification and Quality Testing Using Fast R-CNN

An Efficient Method for Seeds Classification and Quality Tes...

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International Conference on Inventive Research in computing Applications (ICIRCA)

作者： S. Ashwini B. Srinivasa Rao Anil Shirgire K. R. Kannan T Ch Anil Kumar G. Sivashankar Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Dr D Y Patil Institute of Technology Pune Maharashtra India Department of ECE Hindusthan Institute of Technology Coimbatore Tamilnadu India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh India Department of Computational Intelligence SRM University Chennai Tamilnadu India

Research into agriculture is gaining an increasing attention in recent years. The newest innovations use a variety of computational technology to make agricultural tasks easier. Seed quality is the most important of many factors that determine agricultural output. Further insights into quality manufacturing, seed quality control, and impurity discovery can be gained by proper seed classification. Seeds have traditionally been sorted into different categories based on their appearance (including their color, texture, and shape). This is often done by experts who must visually inspect each sample, which is a laborious and time-consuming process. Seed sorting is a labor-intensive process that can be greatly improved by automating the inspection process. This study presents a classification system for seeds using Fast RCNN, inspired by the success of computational intelligence methods in other image classification challenges. The suggested method includes five stages: wiener filter preprocessing; HE image enhancement; binarization; adaptive watershed segmentation; and Rapid R-CNN model training. The proposed approach outperforms other two models such as CNN and RNN.

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Precision Enhanced Breast Cancer Prediction Using Deep Learning Models

Precision Enhanced Breast Cancer Prediction Using Deep Learn...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Abhiram Kunchapu I.R. Oviya Balu Bhasuran Amrita Vishwa Vidyapeetham Computer Science and Engineering - AI Amrita School of Computing Chennai India Bakar Computational Health Sciences Institute (BCHSI) University of California San Francisco Mission Bay CA USA

Breast cancer is still a major worldwide health con- cern, which emphasises how important it is to have accurate and trustworthy diagnostic techniques. Our goal is to improve breast cancer identification in this scenario by utilizing mammography pictures from the RSNA Breast Cancer dataset. Leveraging the power of deep learning, our methodology integrates the SEResNet and ConvNeXtV2 models, simultaneously exploring and comparing various methods. To enhance the performance of our models, we incorporated changes in activation functions by replacing ReLU with GELU, an alternative activation function. Furthermore, we improved downsampling by introducing sepa- rate downsampling layers in the model architecture, optimizing spatial downsampling, and employing 3×3 convolutions with a stride of 2 at the start of each stage and 1×1 convolutions with a stride of 2 at the shortcut connection. The results achieved are highly promising, with a remarkably low loss of 0.0445 and impressive performance metrics. The F1 score stands impressively high at 0.96, complemented by a recall score of 0.95 and a precision of 0.97, showcasing exceptional performance, The accuracy score is also impressive at 92.5. These findings underscore the pivotal role of precision in elevating breast cancer detection accuracy, providing a path for more accurate diagnoses and ultimately improving patient outcomes. These enhancements significantly contributed to the overall performance of our breast cancer detection models, reinforcing the importance of precision in healthcare. This research strengthens the diagnostic tools available in the ongoing fight against breast cancer.

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