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Implementation of Nonlinear Computing Models and Classical Regression for Predicting of Soil Bearing Capacity 2nd

Implementation of Nonlinear Computing Models and Classical R...

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2nd international conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Ibrahim, Awaisu Shafiu Idris, Ahmad Civil Engineering Department Federal University Dutsin-Ma Katsina Nigeria Civil Engineering Department Bayero University Kano Kano Nigeria

ISBN: (纸本)9789819787111

To ensure the stability and safety of the structure against the risks of sliding along the sliding surface of civil engineering foundations, it is necessary to predict the soil's bearing capacity. Conventional analytical techniques are costly, time-consuming, and difficult. A relative comparison of software computational models is presented. Ultimate bearing capacity of soil was predicted in this study using Three different machine learning techniques namely: feed forward neural network (FFNN), support vector machine (SVM), and multilinear regression (MLR). Cohesion (C), internal friction angle (Ø), and ultimate bearing capacity are examples of input variables. 200 datasets were acquired from secondary sources, of which 175 were used for model training and 25 for model validation. The findings show that the proposed method for estimating the final bearing capacity has good predictive reliability. The findings demonstrate the effectiveness of the three models (FFNN, SVM, and MLR) in forecasting the ultimate bearing capacity of the three different foundation types, however, FFNN outperforms SVM and MLR by a little margin. Consequently, in order to compare the performance of the models, a number of error criteria have been taken into consideration, including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Likewise, the corresponding R2 values of ANN-strip (0.9501), ANN-square (0.9846), ANN-circular (0.9680, SVM-Strip (0.9627), SVM-Square (0.9915), SVM-Circular (0.9517), MLRA-Strip (0.9501), MLRA-Square (0.9549) and MLRA-Circular footing (0.9578) respectively. Utilizing soft computing can offer fresh perspectives and techniques to reduce the risk of correlation problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Feedforward neural networks

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CVE Severity Prediction from Vulnerability Description - A Deep learning Approach 2

CVE Severity Prediction from Vulnerability Description - A D...

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2nd international conference on machine learning and Data engineering, ICMLDE 2023

作者： Manjunatha, A. Kota, Kethan Babu, Anoop S. Sree Vivek, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India ICU Medical Cybersecurity Device R&D Chennai600006 India

The Common Vulnerabilities and Exposures (CVE) system is a widely used standard for identifying and tracking known vulnerabilities in software systems. The severity of these vulnerabilities must be determined in order to prioritize mitigation efforts. However, assigning severity to a vulnerability is a challenging task that requires careful analysis of its characteristics and potential impact. Considering the vast number of vulnerabilities identified every year, it is vital to automate the severity assignment, thereby reducing manual effort. This paper proposes a novel approach for predicting the severity of vulnerabilities based on their CVE description using GPT-2, a state-of-the-art language model. The CVSS severity values distribution imbalance is addressed using oversampling and contextual data augmentation techniques. This approach leverages the large-scale language modeling capabilities of GPT-2 to automatically extract relevant features from CVE descriptions and predict the severity level of the vulnerability. The model is evaluated on a test data set of 7,765 CVEs and achieves a high accuracy of 84.2% and an F1 score of 0.82 in predicting the severity of the vulnerabilities on the test data. A comparative analysis of this approach was done against state-of-the-art methods, demonstrating the superior performance of the proposed approach. Based on the results, the proposed approach could be considered a valuable tool for quickly and accurately identifying high-severity vulnerabilities, facilitating more efficient and effective vulnerability management practices. Furthermore, this approach could be extended to other natural language processing tasks related to vulnerability analysis and management. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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An Integrated Approach using Developer Profiles with Temporal Dynamics for Assignee Recommendation in Non-Reproducible Bugs 2

An Integrated Approach using Developer Profiles with Tempora...

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2nd international conference on machine learning and Data engineering, ICMLDE 2023

作者： Goyal, Anjali Sardana, Neetu Department of CSE SET Sharda University Greater Noida India Department of CSE & IT Jaypee Institute of Information Technology Noida India

A vital component of the software framework is bug reports. Bug tracking systems are employed for handling and closing the reported bugs. Unfxed bugs hinder software reliability and also camoufage other bugs. Non-Reproducible bugs deteriorate the quality of bug fxation significantly. Many times non-reproducible bugs are reopened and they lead to fxation as well. This improves the overall bug handling process. The fxation of re-opened non-reproducible bugs requires selection of a prominent developer having enough expertise for resolving the issue. This paper presents a novel framework for triaging of non-reproducible bugs called TriageExpert-Recency. The proposed framework, TriageExpert-Recency takes developer's expertise and time recency of usage of topic by developer into consideration while selecting an appropriate developer. The developer's expertise corresponding to a topic is computed on the basis of number of times developer worked on a similar topic. The time recency of usage of topic by developer is computed using the dates of historical bugs handled by the developer. Bug reports are gathered from the Mozilla Firefox and SeaMonkey projects' Bugzilla repositories for experimental review. Experimental results showed a hit ratio up to 68% for Top-5 developer list using TriageExpert-Recency framework for developer selection in Non-Reproducible bugs. he proposed approach achieved performance up to 70.18% for Mozilla Firefox and 74.8% for SeaMonkey projects respectively. Overall, it has been found that the proposed approach outperforms the existing techniques. © 2024 Elsevier B.V.. All rights reserved.

关键词： software reliability

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Design of a 9-DOF Radiation Resistant Robot for Nuclear Reactors’ Decommissioning 2nd

Design of a 9-DOF Radiation Resistant Robot for Nuclear Reac...

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2nd international conference on the Frontiers of Robotics and software engineering, FRSE 2024

作者： Wu, Tong Wang, Deying Yang, Haiyan Chen, Xu Zhang, Liang Mi, Zhifeng Beijing100048 China Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education Beijing University of Technology Beijing China

ISBN: (纸本)9789819630127

Due to the expiration of the design service life of some nuclear facilities, a large number of hazardous radioactive devices need to be dismantled. Therefore, special attention needs to be paid to the safety and reliability of the dismantling process to ensure the protection of workers and the environment. In response to this challenge, this article proposes an innovative solution: a 9-DOF (Degrees of Freedom) nuclear decommissioning robot composed of a 3-DOF gantry and a 6-DOF robotic arm. The core of this robot design lies in utilizing its high redundancy of 9 DOFs and the intelligence of machine learning to perform complex dismantling tasks. At the same time, to ensure the accuracy and safety of the operation, a forward and inverse kinematics analysis of the robot is required to gain a detailed understanding of its motion capabilities and limitations, making it more suitable for dismantling work in nuclear facilities. The ultimate goal of this research is to effectively recycle graphite reactors. Experiments show that the method of using this 9-DOF nuclear decommissioning robot combined with the RRT (Rapidly-exploring Random Tree) algorithm for path planning has certain feasibility in practical applications. This not only demonstrates the theoretical effectiveness of this technical solution, but also provides important reference value for future practical applications. Through this method, we can safely and effectively handle decommissioned nuclear facilities, thus reducing the potential threat of nuclear radiation to the environment and human health. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inverse kinematics

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Smart Contract Generation through NLP and Blockchain for Legal Documents 2

Smart Contract Generation through NLP and Blockchain for Leg...

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2nd international conference on machine learning and Data engineering, ICMLDE 2023

作者： Ahmed, Sayyed Usman Danish, Abutalha Ahmad, Nesar Ahmad, Tameem Department of Computer Engineering Z. H. College of Engineering & Technology Aligarh Muslim University UP Aligarh202002 India

The rise in legislation and the need for task automation systems has resulted in an amplified requirement for software development. This is aimed at improving the accuracy and efficiency of reading and interpreting laws in legal activities, allowing for enhanced precision and swiftness. Currently, there is a need for analysts to interpret written legislation and then encode it into computer programs, which often leads to errors. As cryptocurrencies gain popularity, there is growing interest in utilizing Blockchain technology in the legal field. Specifically, smart contracts can be used to integrate business rules into laws and automate blockchain management. However, the process of writing fast and high-quality code can be improved by leveraging artificial intelligence based techniques such as natural language processing (NLP) to help practitioners. Despite reviewing the current state-of-the-art, there is a lack of existing work that combines smart contracts and NLP in the context of legislation analysis. This study work generates intelligent code from legislation analysis, utilizing NLP and Blockchain for this purpose. In this research work a pilot prototype for smart contract generation is developed and initial code samples are presented. This work demonstrates a promising results with 96% accuracy in Name Entity Recognition (NER) and also highlights the importance of smart contract generation through NLP. © 2024 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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Using OpenCV Space Detection System 2nd

Using OpenCV Space Detection System

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2nd international conference on Innovations in Data Analytics, ICIDA 2023

作者： Bhatia, Sandeep Naib, Bharat Bhushan Goel, Amit Kumar Kumari, Khushboo Harsh, Ujjwal Mishra, Satyam School of Computing Science and Engineering Galgotias University Greater Noida Uttar Pradesh Greater Noida India School of Engineering and Technology Apeejay Stya University Sohna Gurugram Gurugram India

ISBN: (纸本)9789819749270

In Python, OpenCV is the open-source library for image processing, machine learning, and video capturing. It is an important part in real-time operation. With the use of the OpenCV, we can efficiently use the images as well as videos to identify the objects, faces, and handwriting of an object. We will only focus to object discovery from images utilizing OpenCV in this paper. It is especially a very useful library in Python for image capturing and for all the other processes. The growing number of vehicles and congestion in parking areas have led to significant difficulties for people in finding suitable parking spaces. To address this issue, researchers have been drawn to the emerging field of automatic smart parking systems. These systems utilize technology to assist drivers in locating and reserving parking spots. Our team has developed a vision-based smart parking system that surpasses the accuracy of existing hardware solutions. We have named it Counting Available Parking Space using Image Processing (CAPSuIP). This low-cost system utilizes a modified software Development Life Cycle (SDLC) to efficiently plan, analyze, and test its functionalities. The effective use of parking spots in urban settings is essential for reducing congestion and maximizing resource use. The Space Detection System (SDS) presented in this project was created using the OpenCV computer vision framework. The main goal of the system is to monitor and evaluate parking lots, automatically identifying and classifying each parking space's occupancy state. The primary elements and features of the SDS are described in this abstract. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Open source software

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LSTM-Based Portfolio Optimization with Gerber Covariance Estimator for Increased Robustness 2nd

LSTM-Based Portfolio Optimization with Gerber Covariance Est...

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2nd international conference on Data Science and Network engineering, ICDSNE 2024

作者： Mehta, Ishita Gupta, Kartik Department of Software Engineering Delhi Technological University Delhi New Delhi110042 India

ISBN: (纸本)9789819783359

Traditional methods of optimizing investment portfolios often rely on rigid models that are not able to fully capture the complexity and unpredictability of financial markets. Previous research has primarily centered around statistical analysis, which struggles to capture the complex interdependencies between data points that ML-based models may be able to. In this paper, we have constructed a hybrid model incorporating machine learning and statistical methods to propose a dynamic and data-driven approach to optimize the portfolio using a Long Short-Term Memory (LSTM) network and a covariance matrix based on the Gerber statistic. It estimates the return prices and consequently constructs a Gerber estimator covariance matrix. This yields two-fold advantages, added robustness and improved risk management. Our study utilizes S&P500 tech stocks to evaluate the model and constructs 3 different portfolios for asset allocation, i.e., Minimum CVaR, Mean Risk, and Hierarchical Risk Parity. The annualized Sharpe Ratio for HRP achieved is 1.75, and the metrics show effective risk management. The model is unique due to its hybrid nature which combines the data-centricity and the flexibility of LSTM to capture any non-linear patterns which is capable of adjusting to evolving market conditions. By leveraging the statistical rigor of the covariance matrix to yield promising results, the model can gauge the stochastic nature of financial markets to a reasonable extent, thereby helping to maximize returns and minimize risks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

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Bug Classification Using Stacking Classifier

Bug Classification Using Stacking Classifier

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Disruptive Technologies (ICDT), international conference on

作者： V. Kusumaniswar Mukesh Chinta Sk. Nawaz Shareff Computer Science & Engineering Department VR Siddhartha Engineering College Andhra Pradesh India

Stacking Classifier is an ensemble learning technique in machine learning where the outcome of various base classifiers is forwarded as an input for meta classifier which makes the final decision. In any software industry a triage team will allocate the bugs raised in the software to the designated developers, nevertheless, there is a potential that they may give the bug to the incorrect category of developers, wasting time and effort. It will be simple for the triager to allocate the issue to the appropriate developer if he is able to accurately identify the kind of bug. Also studying the complete bug report and identifying the type of bug is time taking process for the developer. This system identifies the variety of bug using machine learning approach. The primary input to differentiate the type of bug is the description written for the bug report. The description is fed to the natural language processing (NLP) algorithm as an input for text processing, which performs operations like word tokenization, word lemmatization and converting text into numerical representation. BERT (Bidirectional Encoder Representations from Transformers) model is utilized to convert text into numerical data. This system uses Random Forest, Gradient Boosting and Support Vector machine (SVM) as base classifiers and Logistic Regression as meta classifier. In this system, we can determine 3 kinds of bugs namely Concurrency, Semantic and Memory bugs using Stacking classifier. This paper presents a machine learning model which provides high precision than existing systems which utilizes other models like Decision Tree, Random Forest and Naive Bayes individually.

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An Intelligent Antenna Optimization Using machine learning Algorithm for 5G Applications 2nd

An Intelligent Antenna Optimization Using Machine Learning A...

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2nd international conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Ramasamy, R. Rajavel, V. Ghoshal, Dibyendu Jain, Rachit Nanmaran, R. Srimathi, S. Ramco Institute of Technology Tamilnadu Rajapalayam India National Institute of Technology Tripura Agartala India Madhav Institute of Technology and Science Gwalior India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9789819995172

In the suggested methodology, a microstrip patch antenna with a rectangular shape is employed, along with the utilization of modified ant colony optimization (MACO) algorithms. The operating frequency for the patch antenna is set at 28 GHz. The antenna is designed to exhibit a single-band response, analyzed using HFSS software and optimized results obtained using MATLAB tool. For the antenna design, the chosen substrate is FR4, with a dielectric constant of 4.4 and a thickness of 0.6 mm. The selection of FR4 substrate is based on its accessibility and cost-effectiveness. Various parameters are validated to evaluate the proposed antenna structure’s performance, including return loss, voltage standing wave ratio, radiation pattern, and gain. These measurements are further analyzed to optimize the antenna's performance using MACO approaches. The results of this suggested methodology demonstrate improved performance for the single-band microstrip rectangular patch antenna, making it a suitable choice for 5G applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Microstrip antennas

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Research on the Credibility Test Technology of Intelligent Autonomous System

Research on the Credibility Test Technology of Intelligent A...

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Sensors, Electronics and Computer engineering (ICSECE), IEEE international conference on

作者： Shixian Jiang Lijin Wu Bojiang Liu Kuangyu Jin Xiaomei Shen China Institute of Marine Technology & Economy Beijing China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

With significant breakthroughs in key technologies such as machine learning, computer vision, and natural language processing, artificial intelligence technology is accelerating the profound transformation of the military field from mechanization and informatization to intelligence, and traditional software testing methods are no longer applicable. This article first analyzes the credibility of artificial intelligence software, then elaborates on the testing techniques of artificial intelligence software, and finally summarizes the credibility evaluation system, providing technical support for the validation of artificial intelligence software.

关键词： software testing Source coding software algorithms Training data software Sensor systems Natural language processing software reliability software measurement Artificial intelligence

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