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Improving insulation performance with woven flax/PP hybrid composite reinforced by synthetic alumina and aluminum trihydrate

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Interactions 2024年第1期245卷 1-16页

作者： Selvam, Prabu Saravanan, P. Palanisamy, Satishkumar Umate, Laxmikant Saminathan, Rajasekaran School of Computer Science and Engineering VIT University Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Tiruchirappalli Campus Tiruchirappalli India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Directorate of Research Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences Maharashtra Wardha India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

The addition of microparticles of aluminum trihydrate (ATH) and alumina (AL) to flax-based polypropylene (PP) composites enhances their mechanical strength and fire resistance. The composite materials were produced via the vacuum bagging technique. Various weight ratios of fillers (ATH & AL) to polypropylene were obtained through the process of ultrasonication (3, 6, 9, and 12 wt%). The mechanical properties of the hybrid composites were thoroughly evaluated, including their thermal conductivity, flammability, flexural and tensile strengths, and moisture absorption. Based on XRD analysis, it has been determined that the flax composite contained AL and ATH microparticles. The research indicates that the use of additional synthetic fillers in the flax-based composite reduces both the burning and moisture absorption rates, in comparison to typical flax/PP composites. The incorporation of a hybrid composite consisting of 9 wt % AL and 3 wt % ATH results in a 10.36% enhancement in tensile strength and a 3.21% improvement in flexural strength. The improved interfacial bonding strength between the fibers and resin can be attributed to the uniform dissemination of ATH/AL particles. The stress transfer rate will be enhanced as a result of the efficient cross-linking it produces. This was verified by the utilization of scanning electron microscopy (SEM). The AL/ATH hybrid composites possess a thermal conductivity of 0.0596 W/mk as an additional characteristic. The thermal insulation property of the composites remains unaffected by the presence of flame retardant AL and ATH particles, as their thermal conductivity is below 0.065 W/mk. These composites are suitable for applications in the automotive, aerospace, and construction sectors, where lightweight, durable, and fire-resistant materials are needed for interior panels, insulation materials, and structural components. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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TuringQ: Benchmarking AI Comprehension in Theory of Computation

TuringQ: Benchmarking AI Comprehension in Theory of Computat...

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

作者： Zahraei, Pardis Sadat Asgari, Ehsaneddin Computer Engineering Department Sharif University of Technology Iran Qatar Computing Research Institute - QCRI Qatar

ISBN: (纸本)9798891761681

We present TuringQ, the first benchmark designed to evaluate the reasoning capabilities of large language models (LLMs) in the theory of computation. TuringQ consists of 4,006 undergraduate and graduate-level question-answer pairs, categorized into four difficulty levels and covering seven core theoretical areas. We evaluate several open-source LLMs, as well as GPT-4, using Chain of Thought prompting and expert human assessment. Additionally, we propose an automated LLM-based evaluation system that demonstrates competitive accuracy when compared to human evaluation. Fine-tuning a Llama3-8B model on TuringQ shows measurable improvements in reasoning ability and out-of-domain tasks such as algebra. TuringQ serves as both a benchmark and a resource for enhancing LLM performance in complex computational reasoning tasks. Our analysis offers insights into LLM capabilities and advances in AI comprehension of theoretical computer science. © 2024 Association for Computational Linguistics.

关键词： Open systems

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Big Mart Sales Prediction Using Machine Learning 10

Big Mart Sales Prediction Using Machine Learning

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Mondal, Sagar Debbarma, Anindita Prakash, B. Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai India Srm Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350353068

In today's competitive retail landscape, supermarkets like Big Marts meticulously track the sales data of each product to anticipate consumer demand and optimize inventory management. By analyzing this data, including identifying anomalies and trends through data mining techniques, retailers can develop predictive models using advanced machine learning algorithms. Techniques such as Xgboost, Linear Regression, Polynomial Regression, and Ridge Regression are employed to forecast sales volumes with greater accuracy than traditional methods. Through the application of these models, businesses like Big Mart can make informed decisions to enhance their operational efficiency and strategic planning, ensuring they stay ahead in the dynamic market environment. © 2024 IEEE.

关键词： Sales

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Logistic Regression Based Approach for Human Sentiment Analysis Across Domains

Logistic Regression Based Approach for Human Sentiment Analy...

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2024 International Conference on Advances in computing research on science engineering and Technology, ACROSET 2024

作者： Nagar, Sameer Bhanodia, Praveen Sethi, Kamal Kumar Rathore, Narendra Pal Singh Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

This research paper presents a pioneering approach to cross-domain sentiment analysis utilizing logistic regression, a widely employed technique for binary classification tasks. Sentiment analysis, crucial for understanding subjective information in text data, finds applications in market analysis, customer feedback processing, and brand management. Our methodology involves extracting sentiment features from text data, leveraging techniques such as bag-of-words or word embeddings, and training logistic regression models on labeled datasets from diverse domains. Unlike conventional methods relying solely on domain-specific features, our approach incorporates domain-independent features to facilitate knowledge transfer across domains. Through extensive experiments on various datasets including product reviews, social media posts, and news articles, we demonstrate the effectiveness of our approach in achieving competitive performance compared to state-of-the-art methods. Additionally, we explore the impact of domain adaptation techniques such as domain adversarial training and transfer learning on model performance, providing insights into the interpretability of sentiment models and enabling more accurate sentiment prediction in real-world scenarios across diverse domains. © 2024 IEEE.

关键词： Logistic regression

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Glaucoma Detection Using Convolutional Neural Network

Glaucoma Detection Using Convolutional Neural Network

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2024 International Conference on Advances in computing research on science engineering and Technology, ACROSET 2024

作者： Khichi, Divya Jangde, Priyanka Sethi, Kamal Kumar Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

One of the quintessential examples in ophthalmology is the early detection of glaucoma, a disease that accounts for significant irrecoverable vision loss when caught later. Conventional diagnostic methods are based on visual inspection, which may be time-consuming and open to subjective interpretation. We suggest a deep learning method using the Convolutional Neural Network (CNN) model to solve this. With the ORIGA data (a labeled fundus image collection where each has been identified as containing glaucoma), we trained CNN to learn about complex spatial patterns that may discriminate against the disease. We can see a reduction in loss and improvement in accuracy through the graphs;our model is learning well. A confusion matrix with highly accurate favorable rates also shows the effectiveness of CNN, and its predictions individually were accurate. Our CNN model reduces loss by up to 49.3% and improves validation accuracy by nearly 3.97%, which is highly competitive compared to standard logistic regression and machine learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Differential Privacy in Federated Learning Using Noise Multipliers: An Analysis on MNIST Dataset 4

Differential Privacy in Federated Learning Using Noise Multi...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Panchal, Yuvraj Sheikh, Rashid Sethi, Kamal Kumar Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331518981

Federated learning enables decentralized learning to run on multiple clients by training locally on each client's data and sharing new instances only on a central server. However, if privacy is not protected, sensitive information about any customer can be accessed via model updates. Differential privacy (DP) provides a robust method of preserving data privacy by combining controlled noise with gradient updates sent to a central server This paper explores the application of differential privacy to federated learning using different noise factors on the MNIST dataset. We investigate the trade-off between accuracy and privacy protection by analyzing test loss and accuracy over several communication channels, with noise factors of 0.2, 0.3, and 0.7. The results show that increased noise reduces model accuracy but provides better privacy protection. We also emphasize the importance of balancing accuracy and confidentiality in official learning processes to ensure for example safe yet efficient sampling operations. © 2024 IEEE.

关键词： Federated learning

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Optimal Logistic Map with DNA computing Model for Image Encryption 2

Optimal Logistic Map with DNA Computing Model for Image Encr...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Shiny, K.V. Wilfred Blessing, N.R. Jino, R. Palarimath, Suresh Mathumitha, V. Sutherlin Subitha, G. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Oman Department of Civil Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil nadu Kanyakumari District India

ISBN: (纸本)9798331542573

The logistic map with optimization with a novel Deoxyribonucleic Acid (DNA) sequence operation-based novel image encryption scheme is generated. The best mask is obtained by enhancing the excellence of DNA which is the significant advantage of this approach. According to the image decryption and encryption process, correct errors and the similarity of DNA sequences are decreased by the DNA cryptogram becoming an input pace for DNA calculation. The maximum PSNR of encrypted images is obtained by the logistic map function to improve the performance of image security Ant Lion optimization (ALO). The image encryption and decryption process generate optima point DNA sequence rules. DNA coding: utilizing the complementary rule, each nucleotide is then changed into its base pair for a random time or times;the times are produced by Chebyshev maps. The experimental investigations provided better UACI, Number of Pixel Exchange Rate (NPCR), and Peak Signal Noise Ratio (PSNR). This strategy is significant because it increases entropy, which is the fundamental property of randomness, withstands multiple statistical and differential attacks, and produces positive experimental outcomes. © 2024 IEEE.

关键词： Signal to noise ratio

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Demystifying XAI: Understanding of Applications, Limitations, Challenges, and Future Perspectives

Demystifying XAI: Understanding of Applications, Limitations...

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2024 International Conference on Advances in computing research on science engineering and Technology, ACROSET 2024

作者： Varshney, Pratham Singh Rathore, Narendra Pal Sethi, Kamal Kumar Rajput, Shiv Shankar Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Artificial Intelligence (AI) appears to be omnipresent, but its usability is currently a pain point as seen with automation bias. The General Data Protection Regulation (GDPR) has started solving this problem. The premise behind Explainable AI (XAI) is described as making AI decisions interpretable, gaining user trust and robustness of applications in healthcare and finance. This paper gives a survey of recent XAI advancements, providing types of algorithms, and examples of tasks. Despite the concept of XAI, we still have the black-box issue of AI, and especially in critical areas like healthcare with sensitive information, we need to protect user privacy. It is proposed that XAI can expose the decision-making process of the AI, to make the AI decision-making more reliable and trustworthy. Evaluation metrics and potential future directions for XAI are discussed, with a guiding principle: AI needs to be ethical, trustworthy, and user-friendly. © 2024 IEEE.

关键词： Decentralized finance

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Metaheuristic Secure Clustering Scheme for Energy Harvesting Wireless Sensor Networks

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computer Systems science & engineering 2023年第4期45卷 497-512页

作者： S.Nithya Roopa P.Anandababu Sibi Amaran Rajesh Verma Department of Computer Science and Engineering Kumaraguru College of TechnologyCoimbatore641049India Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of Computing Technologies SRM Institute of Science and TechnologyChennai603203India Department of Electrical Engineering King Khalid UniversityAbha61411Kingdom of Saudi Arabia

Recently,energy harvesting wireless sensor networks(EHWSN)have increased significant attention among research *** harvesting energy from the neighboring environment,the sensors in EHWSN resolve the energy constraint problem and offers lengthened network *** is one of the proficient ways for accomplishing even improved lifetime in *** clustering process intends to appropriately elect the cluster heads(CHs)and construct *** several models are available in the literature,it is still needed to accomplish energy efficiency and security in *** this view,this study develops a novel Chaotic Rider Optimization Based Clustering Protocol for Secure Energy Harvesting Wireless Sensor Networks(CROC-SEHWSN)*** presented CROC-SEHWSN model aims to accomplish energy efficiency by clustering the node in *** CROC-SEHWSN model is based on the integration of chaotic concepts with traditional rider optimization(RO)***,the CROC-SEHWSN model derives a fitness function(FF)involving seven distinct parameters connected to *** accomplish security,trust factor and link quality metrics are considered in the *** design of RO algorithm for secure clustering process shows the novelty of the *** order to demonstrate the enhanced performance of the CROC-SEHWSN approach,a wide range of simulations are carried out and the outcomes are inspected in distinct *** experimental outcome demonstrated the superior performance of the CROC-SEHWSN technique on the recent approaches with maximum network lifetime of 387.40 and 393.30 s under two scenarios.

关键词： Clustering wireless sensor networks network lifetime energy efficiency metaheuristics energy harvesting rider optimization

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