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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Chandralekha, E. Vinodhini, S. Kandasamy, V. Rama, P. Ravikumar, S. Saravanan, T.R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept.of CSE Chennai India Velammal Engineering College Dept. of Information Technology Chennai India Panimalar Engineering College Dept. of Information Technology Chennai India College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Chennai India SRM Institute of Science and Technology Dept. of Computational Intelligence Chennai India

ISBN: (纸本)9798350343632

Heart disease is a significant global health challenge, impacting many lives and often being a leading cause of mortality and illness. Detecting heart disease accurately and promptly is essential for effective medical care. Due to the complex interplay of risk factors and the intricate nature of diagnosis, advanced technologies like machine learning are being explored. This study introduces a comprehensive framework that employs machine learning techniques for heart disease detection. The framework involves critical stages such as data preprocessing, feature selection, and training ensemble classifiers. Experimental results highlight the importance of specific attributes in each classifier, offering insights into predictions. Notably, attributes like oldpeak, thalach, ca, cp, and thal are key features in the Random Forest classifier, while Gradient Boosting underscores ca, cp, thal, oldpeak, and age. AdaBoost and the Extra Trees Classifier reveal similar significant attributes. Evaluation metrics emphasize the models' robust performance, with the Extra Trees Classifier achieving the highest accuracy. This research contributes to a practical heart disease detection framework with potential for improving medical decision-making. © 2023 IEEE.

关键词： AdaBoost Extra Trees Classifier Gradient Boosting Machine Learning Random Forest

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Pandemic state’s impact on agriculture

Pandemic state’s impact on agriculture

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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Wilfred Blessing, N.R. Rohith Bhat, C. Sudha, I. Michael, G. Manikandan, T. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Oman Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9781032669663

The COVID-19 outbreak has significantly disrupted the agricultural system in India. In spite of this, the most recent quarterly GDP forecasts post-COVID scenario demonstrate the tenacity and durability of the Indian agricultural sector, which is anticipated to be the only sector to record a positive growth rate of 4% for fiscal years (FY) 2020 and 2021. Similarly, growth for the most recent quarter is projected to have been 5.89%, representing a reduction of 2.4% point. In this context, our goal is to compile a summary of the early evidence on the impact of COVID-19 on the agricultural sector in India, including production, marketing, and consumption, and then to design a set of pragmatic strategies to recover and thrive after the pandemic. According to the survey’s findings, the pandemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity prices, altering consumption patterns. The pandemic caused great emotional, social, economic, and bodily harm to everyone involved in India’s agriculture industry. The state has responded to the crisis by seizing the opportunity it presents and launching a variety of programmes and necessary changes. We suggest a ten-point plan that includes social safety nets, family farming, the trading of buffer stock, staggered procurement, and secondary agriculture in order to revitalise and prosper after the epidemic. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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Sequential infinite-dimensional Bayesian optimal experimental design with derivative-informed latent attention neural operator

arXiv

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arXiv 2024年

作者： Go, Jinwoo Chen, Peng School of Computational Science and Engineering College of Computing Georgia Institute of Technology 756 West Peachtree Street Northwest AtlantaGA30308 United States

We develop a new computational framework to solve sequential Bayesian optimal experimental design (SBOED) problems constrained by large-scale partial differential equations with infinite-dimensional random parameters. We propose an adaptive terminal formulation of the optimality criteria for SBOED to achieve adaptive global optimality. We also establish an equivalent optimization formulation to achieve computational simplicity enabled by Laplace and low-rank approximations of the posterior. To accelerate the solution of the SBOED problem, we develop a derivative-informed latent attention neural operator (LANO), a new neural network surrogate model that leverages (1) derivative-informed dimension reduction for latent encoding, (2) an attention mechanism to capture the dynamics in the latent space, (3) an efficient training in the latent space augmented by projected Jacobian, which collectively leads to an efficient, accurate, and scalable surrogate in computing not only the parameter-to-observable (PtO) maps but also their Jacobians. We further develop the formulation for the computation of the MAP points, the eigenpairs, and the sampling from posterior by LANO in the reduced spaces and use these computations to solve the SBOED problem. We demonstrate the superior accuracy of LANO compared to two other neural architectures and the high accuracy of LANO compared to the finite element method (FEM) for the computation of MAP points and eigenvalues in solving the SBOED problem with application to the experimental design of the time to take MRI images in monitoring tumor growth. We show that the proposed computational framework achieves an amortized 180× speedup. © 2024, CC BY-SA.

关键词： Eigenvalues and eigenfunctions

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An Audit on the Perspectives and Challenges of Hallucinations in NLP

An Audit on the Perspectives and Challenges of Hallucination...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Venkit, Pranav Narayanan Chakravorti, Tatiana Gupta, Vipul Biggs, Heidi Srinath, Mukund Goswami, Koustava Rajtmajer, Sarah Wilson, Shomir College of Information Sciences and Technology Pennsylvania State University United States Adobe Research United States School of Interactive Computing Georgia Institute of Technology United States Department of Computer Science & Engineering Pennsylvania State University United States

ISBN: (纸本)9798891761643

We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Through the examination of the literature, we identify a lack of agreement with the term 'hallucination' in the field of NLP. Additionally, to compliment our audit, we conduct a survey with 171 practitioners from the field of NLP and AI to capture varying perspectives on hallucination. Our analysis calls for the necessity of explicit definitions and frameworks outlining hallucination within NLP, highlighting potential challenges, and our survey inputs provide a thematic understanding of the influence and ramifications of hallucination in society. © 2024 Association for computational Linguistics.

关键词： computational linguistics

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Multi-Verse Optimization for Node Localization in Wireless Sensor Networks 3

Multi-Verse Optimization for Node Localization in Wireless S...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Pitchaimanickam, B. Muthuvel, P. Rajasekar, R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Tamilnadu Kattankulathur603203 India Saveetha Engineering College Saveetha Nagar Department of Ece Chennai Thandalam602117 India School of Computing Kalasalingam Academy of Research and Education Department of Cse Tamilnadu Krishnankoil India

ISBN: (纸本)9798350369083

Wireless Sensor Networks (WSN) are focused on increasing attention to develop their potential applications in healthcare, agriculture, object tracking, and energy management. Sensor nodes are placed in random locations. The information was gathered from the unknown positions of the sensor nodes, and this information may be useless for the applications. The accurate position of the node is required for more sensitive applications of WSN. This work proposes multi-verse optimization as a method for locating unknown nodes. Particle swarm optimization and firefly algorithms are contrasted with the suggested method. The experimental findings demonstrate that, compared to alternative algorithms, the suggested technique yields superior outcomes. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Secure E-Voting System using Deep Learning Techniques 2

Secure E-Voting System using Deep Learning Techniques

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2nd International Conference on Innovative Sustainable computational Technologies, CISCT 2022

作者： Kanchana, M. Gopirajan, P.V. Ramar, Kaladevi Hariharan, Shanmugasundaram School of Computing Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chennai India Department of Computer Science and Engineering Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665474160

Democracy is vital in many emerging economies because it places power in control of the general public, who choose government via the electoral procedure. Elections rely heavily on polling places and people. Numerous changes have happened in electoral processes since the year of the republic till now. Subsequently, Ballot sheets were implemented as a voting medium, but this was later replaced by electronic voting machines, in which voters must select from a list of buttons, each symbolizing a symbol. This paper proposes techniques for ensuring voter identification and privacy through fingerprint scanners and other biometric identification techniques-based machine learning algorithms. As a result, we offered a new approach to speed up the voting process and eliminate similar issues. For face identification, we employed Convolutional Neural Network (CNN) architectures such as Alex Net, Visual Geometry Group (VGG-16), and Random Forest Algorithms in this study. The accuracy provided by Alex Net, VGG-16, and Random Forest (R.F.) Algorithms was 94.16%, 98.45%, and 92%, respectively. VGG-16 architecture outperformed all other algorithms with a 98.45% accuracy. We utilized an ATmega328-based Arduino for finger detection. © 2022 IEEE.

关键词： Biometrics

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Comprehensive Analysis of Arabic Sentiment Analysis using Lexicon and Machine Learning based Approaches 2

Comprehensive Analysis of Arabic Sentiment Analysis using Le...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

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

ISBN: (纸本)9798350372892

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. © 2024 IEEE.

关键词： Adversarial machine learning

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Quantum Teleportation and EntanglementBased Quantum Blind Signature Protocol for Quantum Secure Communication in Security Service Bases 5

Quantum Teleportation and EntanglementBased Quantum Blind Si...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Sumathy, G. Suresh, A. Udendhran, R. Maheshwari, A. Arunprasath, S. Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Chengalpattu Tamil NaduKattankulathur Chennai603203 India

ISBN: (纸本)9798400709418

Quantum teleportation and entanglement-based quantum blind signature protocol were promising new technologies for secure communication between security service bases. They were secure against a variety of attacks, including eavesdropping, signatory disavowal, verifier denial, and forgery. The protocol worked by using quantum entanglement to create a shared secret state between the security service bases. This shared secret state was then used to generate a quantum signature for each message sent between the bases. The signature was then verified by the receiving base using the shared secret state. Compared to classical blind signature schemes, the main advantages of the quantum entanglement-based quantum blind signature protocol were its information-theoretic security and reduced complexity and noise. However, this protocol also faced some challenges, such as preparing and storing entangled states, performing quantum operations and measurements, and ensuring the trustworthiness of the third party. Despite these challenges, the quantum entanglement-based quantum blind signature protocol was a promising new technology for secure communication between security service like military bases. It had the potential to revolutionize the way that military bases communicated with each other, making it possible to send secure messages without revealing the content or identity of the messages. © 2023 ACM.

关键词： Secure communication

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Enhanced Fruit Fly Optimization Algorithm for Wrapper Based Feature Selection in Cancer Prediction

Enhanced Fruit Fly Optimization Algorithm for Wrapper Based ...

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Cognitive, Green and Ubiquitous computing (IC-CGU), International Conference on

作者： P. Saranya D. Viji Aditya Jangiti Department of Computational Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil nadu India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Nowadays, smart healthcare appliances generate vast amount of medical data. Such huge amount of data requires classification methodologies, through which disease diagnostics can be carried out. Chronic diseases like cancer can be predicted in advance to help the patients to take treatment in initial stages of the disease. This article proposes an improvised feature selection and classification -method for cancer prediction. Here we propose an enhanced fruit fly optimization algorithm in KNN classifier for searching strategy with wrapper methods for feature selection. Lung cancer data set, breast cancer data set and leukemia data set has been taken from the UCI repository for testing the performance of the proposed model. For Lung cancer data set, accuracy, specificity, sensitivity, and fscore value observed are 96.97 %, 96.28 %, 96.35 %, and 96.34% respectively. For breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 99.97 %, 98.2 %, 96.85 %, and 90.35% respectively. For Breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 96.67%, 98.28 %, 95.85 %, and 93.85% respectively. The proposed method works well with various dataset and shows prominent accuracy improvement over the existing systems.

关键词： Sensitivity Machine learning algorithms Lung cancer Medical services Feature extraction Prediction algorithms Breast cancer

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SPARSE RECOVERY OF ELLIPTIC SOLVERS FROM MATRIX-VECTOR PRODUCTS

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SIAM Journal on Scientific computing 2024年第2期46卷 A998-A1025页

作者： Schäfer, Florian Owhadi, Houman Computational Science and Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Computing + Mathematical Sciences California Institute of Technology PasadenaCA91125 United States

In this work, we show that solvers of elliptic boundary value problems in d dimensions can be approximated to accuracy ϵ from only (log(N) logd(N/ϵ)) matrix-vector products with carefully chosen vectors (right-hand sides). The solver is only accessed as a black box, and the underlying operator may be unknown and of an arbitrarily high order. Our algorithm (1) has complexity (N log2(N) log2d(N/ϵ)) and represents the solution operator as a sparse Cholesky factorization with (N log(N) logd(N/ϵ)) nonzero entries, (2) allows for embarrassingly parallel evaluation of the solution operator and the computation of its log-determinant, (3) allows for (log(N) logd(N/ϵ)) complexity computation of individual entries of the matrix representation of the solver that, in turn, enables its recompression to an (N logd(N/ϵ)) complexity representation. As a byproduct, our compression scheme produces a homogenized solution operator with near-optimal approximation accuracy. By polynomial approximation, we can also approximate the continuous Green's function (in operator and Hilbert-Schmidt norm) to accuracy ϵ from (log1+d(ϵ 1)) solutions of the PDE. We include rigorous proofs of these results. To the best of our knowledge, our algorithm achieves the best known trade-off between accuracy ϵ and the number of required matrix-vector products. © 2024 Society for Industrial and Applied Mathematics Publications. All rights reserved.

关键词： Principal component analysis

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