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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Kaushal, Arush Sehgal, Vivek Kumar Jaypee University of Information Technology Waknaghat Department of Computer Science and Engineering H.P. Solan India

ISBN: (纸本)9798350302288

Landslides, one of the most common natural disasters in the world, have caused numerous deaths, infrastructure damage, and monetary losses. In the past, scientists have employed a variety of techniques to predict landslide susceptibility. Unfortunately, there is currently a lack of the most precise algorithm that may be utilized to create a landslide susceptibility model. Therefore, the objective of the current work is to assess how well three distinct machine learning techniques - CNN (Convolutional Neural Network), KNN (K-Nearest neighbor), and ResNet (Residual Neural Network) - can predict landslide susceptibility. In this study, training datasets (70% of the total) and testing datasets (30% of the total) were randomly separated into 19 elements like photos, topographical data, and geological data related to past landslide locations etc. For susceptibility modeling, the following factors were taken into consideration: Additional aspects to take into account include soil texture, lithology, land use/cover, slope aspect, topographic moisture index, proximity to roads, streams, and lineaments, convergence index, profile, and plan curvatures. According to the experimental findings, these two machine learning models accuracy is greater than 0.95. The results show that the suggested technique performs with 98 percent accuracy when using the Resnet-50 model and 96 percent accuracy when using the CNN model. Additionally, it should be highlighted that ResNet performs better than CNN in terms of estimating the likelihood of a landslide occurring, but CNN outperformed less accurate. The suggested ResNet model is reliable and suitable for use in the study area's future hazard reduction and land use planning. © 2023 IEEE.

关键词： Machine learning

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An Integrated Approach to Enhance Legal information Retrieval of Sri Lankan Supreme Court Verdicts 5

An Integrated Approach to Enhance Legal Information Retrieva...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayasooriya, Asel Ahamed, Akeel Bandara, Yomal Gavindya, Chamod Kasthurirathna, Dharshana Abeywardhana, Lakmini Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept.of Computer Science & Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Legal professionals and law students often grapple with the arduous task of locating prior Supreme Court rulings for argument preparation and academic study, leading to a laborious and resource-intensive process. This study introduces an innovative approach to streamline the retrieval of legal information from Sri Lankan Supreme Court verdicts. The method centers on developing a Custom Named Entity Recognition (NER) model, boasting a remarkable accuracy exceeding 90%. This model efficiently extracts crucial legal entities from Supreme Court rulings, readily available on the Ministry of Justice's Supreme Court website. The high accuracy ensures precise entity extraction, followed by systematic organization within a dedicated database. Subsequently, a knowledge graph is formed by linking recorded legal entities, reducing information retrieval time to a mere 179 milliseconds, significantly outperforming existing methods. Moreover, a BART summarization model is crafted to generate concise, accurate, and insightful summaries of Supreme Court decisions, boasting an impressive ROGUE1 score of 85%. This approach revolutionizes legal information retrieval, delivering a user-friendly platform that enhances the identification of cases and fosters a deeper understanding, ultimately elevating the quality of legal research and practice. © 2023 IEEE.

关键词： Knowledge graph

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Cloud Security Analysis using Blockchain technology 2

Cloud Security Analysis using Blockchain Technology

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Meenakshi, K. Bharathi, B. Thangaraj, S. John Justin Sivasubramanian, S. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India R.M.K Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Cloud computing is now widely employed across a variety of IT-related fields. It has become an essential technology to address infrastructure and data service requirements with minimal effort, low-cost, and with a high degree of scalability. Although the use of cloud computing has increased significantly, information security concerns have not yet been fully resolved. There are still certain information security concerns that must be resolved in order for cloud computing to advance. In terms of integrity, authenticity, and secrecy, in particular, Blockchain has developed into an essential security instrument. The application of Blockchain in cloud computing security is further examined in this article after a review of the different security concerns in blockchain and cloud computing. The main challenge in the cloud computing is cloud computing security. Because data in the cloud must be transported via the Internet, data security becomes a top priority. It is necessary to maintain the primary data protection methods, such as integrity, accountability, privacies, access controls, authentications, and authorizations. Blockchain is a technological advancement that enhances cloud computing. Blockchain resolves cloud computing security problems. This study seeks to analyze and contrast various cloud environment security challenges with blockchain security difficulties. © 2023 IEEE.

关键词： Blockchain

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Prediction of the Pore-Pressure Built-Up and Temperature of Fire-Loaded Concrete with Pix2Pix

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computers, Materials & Continua 2024年第5期79卷 2907-2922页

作者： Xueya Wang Yiming Zhang Qi Liu Huanran Wang Mechanics and Materials Science Research Center Ningbo UniversityNingbo315211China Jinyun Institute Zhejiang Sci-Tech UniversityLishui321400China School of Civil Engineering and Architecture Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China

Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.

关键词： Fire loaded concrete spalling risk pore pressure generative adversarial network(GAN) Pix2Pix

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Open-Vocabulary Calibration for Fine-tuned CLIP 41

Open-Vocabulary Calibration for Fine-tuned CLIP

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Shuoyuan Wang, Jindong Wang, Guoqing Zhang, Bob Zhou, Kaiyang Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology Shenzhen China Department of Computer and Information Science University of Macau Taipa China William & Mary WilliamsburgVA United States School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Hong Kong Baptist University Hong Kong

Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few. In recent years, considerable efforts and resources have been devoted to adaptation methods for improving the downstream performance of VLMs, particularly on parameter-efficient fine-tuning methods like prompt learning. However, a crucial aspect that has been largely overlooked is the confidence calibration problem in fine-tuned VLMs, which could greatly reduce reliability when deploying such models in the real world. This paper bridges the gap by systematically investigating the confidence calibration problem in the context of prompt learning and reveals that existing calibration methods are insufficient to address the problem, especially in the open-vocabulary setting. To solve the problem, we present a simple and effective approach called Distance-Aware Calibration (DAC), which is based on scaling the temperature using as guidance the distance between predicted text labels and base classes. The experiments with 7 distinct prompt learning methods applied across 11 diverse downstream datasets demonstrate the effectiveness of DAC, which achieves high efficacy without sacrificing the inference speed. Our code is available at https://***/mlstat-Sustech/CLIP Calibration. Copyright 2024 by the author(s)

关键词： Calibration

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Event-Triggered Control for Obstacle Avoidance in Second-Order Nonlinear Multi-Agent Systems with Directed Graph 8

Event-Triggered Control for Obstacle Avoidance in Second-Ord...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Zhu, Zhipeng Zhang, Zhao Wang, Chenyu Zhou, Hongyan School of Computer Science and Software Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

In this paper, we study the obstacle avoidance problem of second-order nonlinear multi-agent systems (MASs) with directed graph based on event-triggered control. Firstly, the consensus requirement is accomplished by using the event-triggered consensus algorithm, and experiments are given for verification. Secondly, an improved artificial potential field (IAPF) method is proposed to solve the obstacle avoidance problem, and comparison experiments are made, from which the experimental results show that the IAPF method is significantly better than the traditional artificial potential field (TAPF) method under the same conditions. © 2024 SPIE.

关键词： Directed graphs

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A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

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computers, Materials & Continua 2022年第2期70卷 2365-2380页

作者： Tallat Jabeen Ishrat Jabeen Humaira Ashraf Nz Jhanjhi Mamoona Humayun Mehedi Masud Sultan Aljahdali Research Center for Modelling and Simulation(RCMS)NUST Islamabad44000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia College of Computer Science and Information Technology Jouf UniversityAl JoufSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in *** affects the whole world through personto-person *** virus spreads by the droplets of coughs and sneezing,which are quickly falling over the ***,anyone can get easily affected by breathing in the vicinity of the COVID-19 ***,vaccine for the disease is under clinical investigation in different pharmaceutical *** now,multiple medical companies have delivered health monitoring ***,a wireless body area network(WBAN)is a healthcare system that consists of nano sensors used to detect the real-time health condition of the *** proposed approach delineates is to fill a gap between recent technology trends and healthcare *** COVID-19 affected patient is monitored through WBAN sensors and network,a physician or a doctor can guide the patient at the right timewith the correct possible *** scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein,a Monte Carlo algorithm guided protocol is developed to probe a secured cipher *** cipher helps to avoid wireless network issues like packet loss,network attacks,network interference,and routing *** Carlo based covid-19 detection technique gives 90%better results in terms of time complexity,performance,and *** indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity,performance,and efficiency and thus,is advocated as a significant application for lessening hospital expenses.

关键词： COVID-19 coronavirus cryptography Monte Carlo algorithm edge computing

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Utilizing Deep Learning Methods for Resource Allocation in 6G Communication Networks 5th

Utilizing Deep Learning Methods for Resource Allocation in 6...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kavitha, R. Singh, Shweta Devrani, Rekha Kumar, Kakumanu Prabhanjan Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Department of Information Technology and M.Tech Integrated Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

(6G) conversation networks promise extra pace, coverage and capabilities than the current era (5G). To fulfill this promise, deep getting to know techniques can be used to allow better useful resource allocation choices. Deep mastering approaches can gather temporal correlations between node positions and sign characteristics to predict system variables over the years. This statistics may be used to higher control community sources, optimize spectral performance, and decrease latency. Improved resource allocation can also allow smarter visitors routing and selection making, specifically in terms of predicting consumer’s options and wishes. The proposed version uses convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enable seamless community connectivity. To determine resource allocations autonomously, it utilizes converting community topologies, consumer mobility, and a ramification of person site visitor’s profiles. The version optimizes resource allocation and cargo balancing in order to maximize throughput and decrease latency whilst still preserving machine stability. This permits the verbal exchange community to be greater adaptive while also increasing its efficiency. Ultimately, the proposed deep gaining knowledge of-primarily based aid allocation method provides a singular method to improve the overall performance and scalability of 6G verbal exchange networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Transforming Business Management Practices for Increased Production through Deep Learning and Cloud Computing integration 4

Transforming Business Management Practices for Increased Pro...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar, P. Santhosh Singh, Anupam Soniya Farhana, Shaik Choudhary, Amar Biswas, Susmi Srm Institute of Science and Technology Ramapuram Faculty of Engineering and Technology Department of Information Technology Chennai India Graphic Era Hill University Department of Computer Science and Engineering India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Sanskar College of Engineering Department of Management Ghaziabad India CVR College of Engineering Department of Computer Science and Engineering Hyderabad India Alliance University Department Electronics and Communication Engineering Bengaluru India Haldia Institute of Management MAKAUT Department of Management Science India

ISBN: (纸本)9798350364729

This research is vital to meet the need for improved predictive maintenance in manufacturing, thus enabling predictive operations and minimizing expensive downtime. The aim is to assess and compare different machine learning models like Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Trees( DT ) with respect to predicting maintenance requirements using historical sales data, social media trends, and machinery sensor readings. The methodology included gathering a dataset of 3,200 records from the industry and then the pre-processing phase to compute the preprocessing operations like data cleaning, normalization and feature selection. The various models were then trained and tested, using 70% of the data for training to generalise the model, with the rest of the unseen test set consisting of the remaining 30%. The models were evaluated based on performance metrics (accuracy, precision, recall, F1 score and Dice Similarity Coefficient or DSC). Results showed the proposed ANN model as the most accurate and efficient prediction mechanism in comparison with precision, recall and F1 score for remaining useful life (RUL) predictions. The SVM model also worked well, with an accuracy of 95.45% but low rates of false positives and negatives. Although the DT model was informative, its performance (93.34% accuracy) was less robust than that of our RF ensemble trainer. This research demonstrates the use of these findings to be applied in real-time maintenance prediction by ANN. This research would help industries and other manufacturing sectors to reduce downtime, achieve optimal planned maintenance scheduling and improve production efficiency. © 2024 IEEE.

关键词： Predictive maintenance

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An Improvement of K-means Algorithm for Big Data Clustering

An Improvement of K-means Algorithm for Big Data Clustering

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2023 International Conference on computer Applications technology, CCAT 2023

作者： Liu, Bingshan Zhang, Yaxuan Chen, Dandan Ministry of Education Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Nanjing210044 China Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China

ISBN: (纸本)9798350301540

Clustering data, the process of categorizing comparable data into distinct groups, is a fundamental task in data analysis. K-means is a widely used and fundamental clustering technique that employs iterative calculations to divide a given dataset into k clusters. It is particularly effective and adaptable for large datasets. However, determining the number of clusters and their centers in advance is a complex problem that often plagues researchers. In this article, we propose an innovative strategy to address these two obstacles by utilizing preprocessing to obtain the necessary information for k-means. We employ density-based clustering methods, represented by DENCLUE (DENsity-based CLUstEring), which can separate clusters regardless of the total number of clusters. DENCLUE is highly effective in producing excellent clusters and has received extensive attention. Our proposed method, Dek-means (Density-based kmeans algorithm), uses DENCLUE as a preliminary processing step to determine the quantity of clusters and calculate cluster centers. The resulting information is then passed on to k-means for final calculation and clustering. To evaluate the performance of our novel algorithm, we conducted tests on large datasets and analyze the results. The proposed method is guaranteed to converge to global minima, which improves the quality of the final result. © 2023 IEEE.

关键词： Large datasets

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