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Behaviour of Skirted Foundation Under different loading Conditions Using FEM Approach 1

Indian Geotechnical Conference, IGC 2021

作者： Katariya, Piyush Vanza, Mayur G. Applied Mechanics Department L. D. College of Engineering Ahmedabad India

ISBN: (数字)9789811969980

ISBN: (纸本)9789811969973

In case of foundation design for structures like Wind Turbine, there is a need to satisfy bearing capacity and settlement criteria to counter moment induced by environmental loading in addition to general loading. Foundation with skirts around the circumference are now considered to be a viable foundation alternative for a variety of offshore and onshore structures to satisfy the overall stability and to limit the dimensions of the foundation. By providing skirted foundation, the soil in between the skirts gets confined and increase the bearing capacity of same soil. Thus it will be very useful in diminishing the cost of stabilization of the soil. The present work focused on the 3d FEM analysis of octagonal raft foundation with and without skirts using Plaxis3d. The studied parameters incorporated the influence of loading conditions, length of skirts (l), and spacing of skirts (d) on bearing capacity and settlement of both the foundation condition. The enactment of a simplified skirted foundation model under vertical and moment loading is investigated for three skirts configuration with varying length of skirts and the stiffness value (E) of soil to check the efficiency of skirts and compared with non-skirted foundation. The results shows that the reduction in differential settlement and rotation due to moment loading with increase in length of skirts (l). Thus FEM analysis introduced in this investigation helped in better understanding and identifying the failure pattern of octagonal raft foundation with and without skirt. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Bearing capacity

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Correlating Relative density and Particle distribution at Specific Compaction Energy of Cohesionless Soil in Ahmedabad Region

Correlating Relative Density and Particle Distribution at Sp...

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Indian Geotechnical Conference, IGC 2021

作者： Marsonia, Shyamkumar N. Vanza, Mayur G. Applied Mechanics Department L. D. College of Engineering Ahmedabad India

ISBN: (纸本)9789811965128

Field compaction of soil usually involves different types of equipment with the compaction energy varying significantly. If can relative density be correlated with index properties of cohesionless soil, the developed correlations of relative density at different compaction energy are very useful in fieldworks. As per literature, for granular soil, relative density (dr) depends on the particle shape, size, particle size distribution, and variance in gradation curve. Present study involves development of correlation model, where multiple linear regression analysis is used to relate relative density at specific compaction energy of soil by its index properties. Thirty samples of cohesionless soil from Ahmedabad region collected and tested for soil properties, i.e. grain size distribution (Fc%, d10, d30, d50, d60, Cu, Cc), specific gravity (Gs), density index test for maximum and minimum void ratio (emax and emin), laboratory compaction test (standard and modified) for void ratio (es and em). Based on tested index properties of cohesionless soil, multiple linear regression analysis carries out in SPSS software and individual regression model developed for all assign output variables emax, emin, es and em, which are predicted by Fc%, d10, d30, d50, d60, Cu, Cc, Gs input variables. By relating above regression model, predictive model of relative density was concluded. Accuracy of model checked by relating predicted value to experimental value of relative density, where 85% sample size lies within range of ±5% (95% confidence interval) with reference to 1:1 line and all with in ±10. The relative density correlations described above will be useful for field design specification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Multiple linear regression

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Enhancing Road Safety and User Experience through Intelligent Tollgate Systems and Vehicular Communication Networks 5

Enhancing Road Safety and User Experience through Intelligen...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Jebaraj, Bharath Singh Nasreen, M.Ayeesha Sushmitha, S. Azarudeen, A. Subha, T.d. Vivekrabinson, V. Kalasalingam Academy of Research and Educatuion Department of Computer Science and Engineeing Sriviliputhur India R.M.D. Engineering College Department of Electronics and Communication Engineering Thiruvallur India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India R.M.K. Engineering College Thiruvallur Department of Electronics and Communication Engineering Thiruvallur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350367171

The automotive industry's growth, fueled by technology and job opportunities, has heightened global automobile sales. despite this progress, road safety remains a concern, with numerous lives lost in car accidents annually. To address this, Vehicle-to-Vehicle (V2V) communication emerges, enhancing road safety through real-time information exchange. Roadside Communication Units (RCUs) provide vital services, offering traffic updates, weather notifications, and immediate alerts about incidents. Intelligent tollgate systems in vehicles streamline transactions, and speed control systems ensure safer driving. leveraging Zigbee technology enables seamless communication with toll plazas, improving traffic flow. This study aims to enhance road safety through intelligent tollgate systems, V2V communication, and innovative technologies, emphasizing their potential to optimize traffic flow and provide a more comfortable driving experience. Identified are future research directions and development opportunities in this dynamic field. © 2024 IEEE.

关键词： Zigbee

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Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network data

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SN computer Science 2025年第2期6卷 1-23页

作者： Maruthupandi, J. Sivakumar, S. Kumar, V. Senthil Srikaanth, P. Balaji Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Trichy India Department of Networks and Communication SRM Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The degree of Correlation Network Model (dCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based deep Analyzing Network (SA-dAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to S

关键词： Classification degree of correlation network model (dCNM) Heap based optimization (HbO) Opinion mining Self-attention based deep analyzing network (SA-dAN) Semantic analysis Social data

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A New Wave in CyberSecurity: democratising the Future Networking landscape 1

A New Wave in CyberSecurity: Democratising the Future Networ...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Yogesh, M.J. Gowri Shankar, B.S. Shivakumar, Shreyas Jamanani, Sharan The National Institute of Engineering Department of Computer Science and Engineering Mysore India Vidyavardhaka College of Engineering Department of Information Science and Engineering Mysore India K.L.S Gogte Institute of Technology Computer Science India

ISBN: (纸本)9798350323795

Internet use has grown ubiquitous worldwide. Online crime has grown alongside Internet activity. Cybersecurity has evolved rapidly to keep up with cyberspace's rapid changes. Cyber security refers to a nation's or company's online protections. The term 'cyber security' was barely known by the general public two decades ago. Cybersecurity is an issue that impacts not just individuals but also businesses and governments. Everything is now digital, and cybernetics makes use of tools from the cloud, mobile devices, and the Internet of Things. The issues of confidentiality, safety, and restitution all arise when discussing cyber attacks. To protect networks, computers, programmes, and data from being attacked, damaged, or accessed without permission, cyber security has been developed. With an eye on the future of next-generation networks, this article provides a high-level review of several ANN and dl techniques utilized for Cybersecurity and CyberSecurity Capacity Building(CCB) strategies. © 2023 IEEE.

关键词： Reinforcement learning

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Stakeholders Sentiment Analysis using Twitter data on NEP 2020: A Case Study Approach 7

Stakeholders Sentiment Analysis using Twitter Data on NEP 20...

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Arlimatti, Shivaleela Elbreiki, Walid Hassan, Suhaidi Mane, Suresh d. Rathod, Ganesh Dept of Computer Science & Engineering Tatyasaheb Kore Institute of Engineering & Technology Warana University Warananagar Kolhapur India Dept. of Computer Science College of Computer Technology Banghazi Libya Internetworks Research Laborotary School of Computing University Utara Malaysia Sintok Malaysia Dr. D. Y. Patil Pratisthan’s College of Engineering Salokhenagar Kolhapur416007 India Dept of Computer Science & Engineering Dr. D. Y. Patil Pratisthan’s College of Engineering Salokhenagar Kolhapur India

ISBN: (纸本)9798331519513

The National Education Policy 2020 (NEP2020) is a comprehensive framework aimed at transforming the education system in India. This paper analyzes NEP2020 and its details envisioned in promoting educational excellence and addressing the needs of a rapidly changing world. The educated elite have access to the information on salient features and hence are in a position to give their free and fair opinion. Thus, this work is based on twitter wherein it utilizes a dataset of 18000 tweets, focusing on discussions and opinions related to the NEP 2020. The tweets are preprocessed and then labeled with sentiment categories such as positive, negative, or neutral. A decision tree classifier is trained on the preprocessed and labeled dataset to predict the sentiment of new tweets. The performance of the classifier is assessed using accuracy, recall, F1 score, and precision metrics, providing insights into the effectiveness of the sentiment analysis approach. The decision tree classifier achieves promising performance in accurately predicting sentiment categories. It is observed that over 55% of tweets are positive towards the policy, 17% are negative and 28% are neutral towards the policy with the study being 92% accurate. The findings of this work can be used by policymakers and education stakeholders in understanding public sentiment and making informed decisions regarding the NEP 2020. © 2024 IEEE.

关键词： Tweets

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Automated Process for Retinal Image Segmentation and Classification Via deep learning based CNN Model 6

Automated Process for Retinal Image Segmentation and Classif...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Abitha Kumari, d. Rama lakshmi, S. Revathy, R. Sundararajan, S. Santhana Krishnan, R. lakshmi Narayanan, K. Department of Computer Science and Engineering RMK Engineering College Thiruvallur India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi Tirunelveli India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Mechanical Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India

ISBN: (纸本)9798350398496

Retinal vessel segmentation is a critical problem for the identification of a variety of retinal diseases, and manual segmentation is a time-consuming process. Automatic segmentation of such vessels might help in eye disease diagnosis. Because various studies on the automated mechanism for retinal illness were conducted, there were certain drawbacks such as lesser accuracy, a lower prediction rate, and so on. For the classification and diagnosis of diabetes disease in retinal images, an efficient convolutional neural network (CNN) based classification algorithm is used. The input dataset is preprocessed using the extraction of the green channel, picture enhancement using a histogram-based technique, and noise reduction. The vessel is extracted using a thresholding method. The watershed algorithm is then used to segment the preprocessed picture. The features are retrieved from the segmented picture by the Grey level co-occurrence matrix (GlCM) approach, then selected using the principal component analysis (PCA) algorithm. The anomaly is found when the specified characteristics are categorized. Finally, a performance analysis is done and related to current methodologies in order to demonstrate the effectiveness of the suggested mechanism. For performance assessment, the three publicly accessible databases dRIVE, STARE, and CHASE dB1 are employed. As a consequence of the outcomes, it was learned that the proposed approach outperforms other current processes. © 2023 IEEE.

关键词： Image classification

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Continuous Gesture Recognition in Human-computer Interaction through Real-Time Machine learning Models

Continuous Gesture Recognition in Human-Computer Interaction...

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2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Pushparani, S. Muthukrishnan, A. Poongavanam, N. Kumar, K. Sampath Arun, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600078 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India Amet Deemed to be University Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

This paper focuses on the realisation of gesture recognition for permanent interaction with the human-computer interface using deep learning. The study focuses on the fact that the necessary gestures are dynamic and continuous, and the approach is based on the architecture that is CNN-lSTM hybrid for spatial features and temporal sequence modeling. The training and evaluation of the system are conducted on gesture datasets which are open source while real-time implementation is done on the edge devices by using the TensorRT. This proves its efficiency and promptness and the performance indicators are provided with regard to various scenarios. Research on the users showed positive responses concerning the unit and the degree of engagement the users had with the system. Nevertheless, the issues of generalization and increasing the computational speed are the main concerns for further development However, the presented work offers promising perspectives for the improvement of the gesture-based interfaces in the applications from virtual reality to assistive technology. In future research, we will improve the architecture proposed in the paper, add new features, make the system open to more kinds of data modalities, and enable it to learn more gestures and contexts for interactions in general. © 2024 IEEE.

关键词： Gesture recognition

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Enhancing Enterprise data Management with Secure and Scalable Cloud Storage Solutions 2

Enhancing Enterprise Data Management with Secure and Scalabl...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Kanna, Rajesh lakshmi, d. Muneeshwari, P. Valantina, G. Mary Suguna, R. Department of Petroleum Engineering AMET Deemed to be University Chennai India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798350383867

Today it has become extremely critical for the business to utilize and safeguard the data(Enterprise data) in a proper way in order to achieve the organizational success which is in accordance with the digital environment. The objective of this paper is to analyze the paradigm change in the patterns from relying mainly on secure and scalable cloud storage solutions to handle evolving challenges encountered in data management. The shift from old stands on site in console to a cloud based storage system gives enterprises unlimited capacity not only is that but they also can scale accordingly and get the best security features. No sentiment was spared by the introductory abstract, which discussed the advantages to the end users of cloud storage, from its flexibility and accessibility to being cheaper than preserving data in conventional storage modes. On the other hand, abstract attracts the security in cloud storage, including encryption, access controls, and compliance certificates that help protect the information from a theft and other risks. The other topic that has been touched upon is scalability, which demonstrates how the cloud makes enterprises to be very adaptable to current storage requirements and enabling growth of storage that is astronomical in magnitude. Consequently, the poem abstract illustrates the essential functions of cloud storage as the technology that particularly empowers innovation through data science and use of analytics and artificial intelligence. This article demonstrates the need for organizations to implement a cloud standard purpose made for the storage of data and the harm of business in the digital age as a strategic priority amidst data management activities. © 2024 IEEE.

关键词： Cloud storage

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leveraging Machine learning for Real-time Anomaly detection and Self-Repair in IoT devices 2

Leveraging Machine Learning for Real-time Anomaly Detection ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Ali, S. Ahamed Sujatha, d. Michael, Raj.T.F. Ramesh, G. Agoramoorthy, Moorthy Computer Science and Engineering Easwari Engineering College Ramapuram India St. Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai India Simats Department of Computer Applications Sclas TN Chennai India K.L.N. College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369960

The Internet of Things (IoT) gadgets have completely transformed a number of different businesses by producing enormous amounts of data. A machine learning framework for real-time Internet of Things anomaly detection and self-repair is presented in this research for the first time. Incorporating data preparation, anomaly detection, and selfrepair are all components of the framework. Through the use of data preparation module normalization and dimensionality reduction, Internet of Things data analysis can be improved. Through the utilization of deep neural networks, support vector machines, and several other machine learning models that have been trained on previous data, abnormal behavior can be identified. The reliability of ensemble approaches is increased. The self-repair module is responsible for automatically repairing abnormalities by resetting the settings of the device, upgrading the firmware, and applying patches. device disruptions are reduced to a minimum by validation. Within the context of smart home and industrial automation tests, events were identified with a high degree of precision and a low percentage of false positives. Because it automatically fixes problems, the self-repair module makes the Internet of Things ecosystem better. © 2023 IEEE.

关键词： Anomaly detection Internet of Things (IoT) Machine learning Self-Repair

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