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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Nishitha, P. Sarika, T. Vijayakumari, E.N. Vuyyuru, Asha Silparaj, M. Mohanty, Jnyana Ranjan Mlr Institute of Technology Department of Assistant Computer Science and Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science Engineering Hyderabad India Hyderabad India Hyderabad501510 India Kalinga Institute of Industrial Technology University India

ISBN: (纸本)9798350370188

Text summarization involves creating a short, concise summary that captures the salient points of the source text. We'll demonstrate a synthesis process based on the use of trainable machine learning algorithms with a collection of features taken straight from the source text. There are two groups of these features: statistics-also referred to as extractive summarization-based on how frequently different textual components occur and linguistics also known as abstractive summarization - based on morphological and syntactic analysis of documents. In this paper, we show how BERT can be usefully applied in text summarization and provide a common framework for abstraction models. We also demonstrate that a fine-tuning method can further improve the quality of the generated summaries. © 2024 IEEE.

关键词： Syntactics

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Prediction of agricultural waste compost maturity using fast regions with convolutional neural network(R-CNN)

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Materials Today: Proceedings 2023年

作者： Sangeetha, J. Govindarajan, Priya Senior Assistant Professor Department of Computer Science & Engineering Srinivasa Ramanujan Centre SASTRA Deemed University Kumbakonam India. Assistant Professor Department of Computer Science AMRITA VISHWA VIDYAPEETHAM Deemed University Mysuru India.

In agriculture production, the enormous volume of wastage production is a major concern. Therefore, composting a massive amount of thrash will transform from agricultural wastage to stabilized, disinfected, and pollution-less yields, allowing important nutrients to be retained and the fertility of soil to be enhanced. Carbon to Nitrogen proportion (C:N) or estimating seed germination index (GI) is the most well-known technique for deciding the compost development of agricultural throwaway, they are both tedious and hard to carry out. Thus, various powerful feature extraction calculations were utilized in the literature. These broadly utilized texture feature extraction strategies could obtain only single-level characteristics, however, the Faster R - CNN model can separate multilevel image attributes since it has a bunch of cascaded layers of convolution and activation functions that could make sense of color and texture features of composts at different levels. Thus, Fast Regions with Convolutional Neural Network(R-CNN) was acquainted in order to rapidly evaluate compost maturity by examining pictures of different composting phases. © 2023 Elsevier Ltd. All rights reserved.

关键词： Feature extraction

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Improving Robotic Arms Through Natural Language Processing, computer Vision, and Edge Computing

Improving Robotic Arms Through Natural Language Processing, ...

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Mechatronics, Control and Robotics (ICMCR), International Conference on

作者： Pascal Sikorski Kaleb Yu Lucy Billadeau Flavio Esposito Hadi AliAkbarpour Madi Babaias Undergraduate Research Assistant Computer Science Department Saint Louis University Saint Louis USA Undergraduate Research Assistant Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis USA Associate Professor Computer Science Department Saint Louis University Saint Louis USA Assistant Professor Computer Science Department Saint Louis University Saint Louis USA CBL Assistant Professor of Robotics Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis USA

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. Experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. For video demonstrations and source code, please refer to: https://***/EnhancedArmEdgeNLP.

关键词： computer vision Mechatronics Source coding Machine vision Large language models Prototypes Focusing Manipulators Natural language processing Edge computing

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Optimized Prognostic Models for Oral Cancer Survival using Feature Selection Methods

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Procedia computer science 2024年 235卷 1832-1840页

作者： Deepali Neelam Goel Padmavati University Institute of Engineering and Technology Panjab University Chandigarh 160014 India Assistant Professor Department of Computer Science Guru Nanak College Budhlada 151502 India Department of Computer Science Punjab Engineering College (deemed to be university) Chandigarh 160012 India

Accurately predicting survival time holds significant importance in various medical and scientific domains. This study proposes a novel survival prediction model for oral cancer using multi-omics data including clinical, DNA methylation, copy number alteration, and mRNA data. The proposed model incorporates four feature selection methods, namely LASSO, Elastic Net, Random Forest Regressor, and Recursive Feature Elimination (RFE), to improve the accuracy. The findings reveal that RFE outperforms the other methods, achieving the highest C-Index of 0.944. This is a significant improvement over previous models, which have a c-index of 0.694 using only clinical data and 0.916 using multi-omics data. In terms of precision, the Random Forest Regressor is the most precise with an MSE of 18.37, followed by RFE of 21.61. Notably, both RFE and Random Forest Regressor outperformed LASSO and Elastic Net in predictive accuracy. Additionally, the results highlight the consistency of RFE, as indicated by its low Median Absolute Error (MedAE) of 2.86. Random Forest Regressor also performed well in this regard, with a MedAE of 4.05. In contrast, Elastic Net exhibited a higher MedAE of 4.55, while LASSO showed more variability, with a MedAE of 8.58. These findings offer valuable insights into survival analysis and feature selection, helping researchers and practitioners select appropriate approaches for survival time prediction, and improving accuracy and reliability in critical applications.

关键词： Oral Cancer Survival Time Feature Selection Accuracy

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An efficacy analysis of data encryption architecture for cloud platform

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Procedia computer science 2023年 218卷 989-1002页

作者： Sheenam Malhotra Williamjeet Singh Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India

In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Google, Amazon, etc. Cloud system presents several benefits to users in terms of easy operations, low implementation, and maintenance expenses. However, significant risks are encountered in the data security procedures of cloud systems. Although the area is frequently being analyzed and reformed, the concern of cloud data protection and user reliability remains under uncertainty due to growing cyber-attack schemes as well as cloud storage system errors. To deal with this risk and contribute to the endeavor of providing optimal data security solutions in cloud data storage and retrieval system, this paper proposes a Symmetric Searchable Encryption influenced Machine Learning based cloud data encryption and retrieval model. The proposed model enhances data security and employs an effective keyword ranking approach by using an Artificial Neural Network. The comparative assessment of the proposed model against multiclass SVM and Naïve Bayes has established the better operational potentiality of the model. The effectiveness of the proposed work is justified by the association between high TPR and low FPR. Further, a low CCR of 0.6973 adds up to the success of the proposed work.

关键词： Cloud Computing Symmetric Searchable Encryption Machine Learning

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Detection and Prediction of Bond Degradation for Piezo Impedance-Based Structural Health Monitoring (PISHM) Using Hybrid Deep Learning Model 4th

Detection and Prediction of Bond Degradation for Piezo Imped...

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4th Structural Integrity Conference and Exhibition, SICE 2022

作者： Parida, Lukesh Moharana, Sumedha Giri, Sourav Kumar Research Scholar Department of Civil Engineering Shiv Nadar Institute of Eminence Dadri201314 India Postdoctoral Researcher Indian Institute of Technology Bombay Mumbai India Associate Professor Department of Civil Engineering Shiv Nadar Institute of Eminence Dadri201314 India Assistant Professor School of Computer Engineering KIIT Deemed to be University Odisha Patia Bhubaneswar756001 India

ISBN: (纸本)9789819763665

In past years, sensor-based health diagnostics have shown significant potential for electromechanical impedance (EMI) techniques with many practical implementations. In order to enhance the effectiveness of health monitoring systems, it is crucial to examine the bonding interface between the piezoelectric sensor (PZT) and the host structure. A promising approach to achieve this goal is the utilization of Sn–Ag bonding, which offers improved resistance to moisture, chemicals, and temperature compared to traditional epoxy adhesive bonding. The Sn–Ag bond layer presents a novel attachment method to the structural design, which may ultimately enhance the sensitivity of the health monitoring system. This study used EMI techniques to instrument a unique Sn–Ag alloy-based bond and compared it against an epoxy adhesive bond for better sensing and actuation. Later, both the bonds were cured, and the bond performances were analyzed through conductance and susceptance signatures. Moreover, three different bond layer damage conditions, i.e., incipient, moderate, and severe, were chosen to check the performance of the bond layer through admittance signatures. The EMI techniques rely on pristine data, which presents significant constraints when applied to pre-existing structures. Furthermore, the experimental results were used to predict baseline data through deep learning classifications, i.e., convolutional neural network (CNN), long short-term memory (LSTM), and CNN-LSTM hybrid model for all the bond layer damage conditions. The experimental results were compared against the chosen deep learning model for futuristic prediction. The efficiency of all the deep learning models is quantified using performance metrics like accuracy and R2 score. The present study demonstrates that the hybrid model yields superior performance compared to the other two models for predicting Sn–Ag and adhesive bond layer damage. The proposed framework aims to replace traditional optimization algorithm

关键词： Structural health monitoring

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A Comprehensive Review on Detection of Dacrocytes from Blood Smear Images using ML Algorithms

A Comprehensive Review on Detection of Dacrocytes from Blood...

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International Conference on Recent Trends in Advance Computing (ICRTAC)

作者： D. Kavitha B. Padmavathi Shakthi Priya S Sanjana N Sidesh S Department of Computer Science and Engineering Assistant Professor of Easwari Engineering College Chennai India Department of Computer Science and Engineering Student of Easwari Engineering College Chennai India

A nation’s capacity to develop is greatly impacted by the prevalence of anemia, a major public health issue. Despite this, it is a sickness that many developing countries choose to overlook. A million people die each year from iron deficiency, which is the underlying cause of anemia in at least half of those cases. Approximately 75% of these fatalities take place in Southeast Asia and Africa. One of the leading causes of death on a global scale is iron deficiency anemia (IDA). The use of machine learning techniques for the prediction of anemia was the focus of this work. Light Gradient Boosting Machines, Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, and K-Nearest Neighbors were the five machine learning models studied in the study. The voting classifier method is used to merge various models in order to improve the accuracy of predictions. The need of accurate sickness forecasting by medical practitioners is highlighted by the study. Being able to predict when anemia may occur is crucial for effective prevention and treatment of the condition. This study proves that specific machine learning algorithms can improve disease prevention and treatment by predicting the occurrence of anemia. Using advanced algorithms and data processing techniques can help doctors make better predictions and decisions, which in turn improves patient outcomes. According to the results, the vote classifier has a 99.95% accuracy rate.

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Assessing CNN’s Performance with Multiple Optimization Functions for Credit Card Fraud Detection

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Procedia computer science 2024年 235卷 2035-2042页

作者： Chandana Gouri Tekkali Karthika Natarajan Research Scholar School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Assistant Professor School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India

In recent days, credit card fraud has emerged as a significant challenge for researchers in the field of detection and prevention. Tackling this challenge holds substantial benefits for both public and private organizations, as it directly impacts economic statistics. Our proposed model introduces crucial advancements to address this issue in real-time scenarios. To achieve this, we harnessed the power of Deep Convolutional Neural Networks (DeepConvNet) in conjunction with various optimization techniques. Optimization algorithms encompass a group of mathematical and computational techniques employed to discover the most suitable solution or set of solutions for a given problem. The primary goal of these algorithms is to optimize, either by maximizing or minimizing, an objective function while ensuring compliance with specific constraints. In this research work, we provide a comparison of highly effective and validated optimization techniques: Stochastic Gradient Descent (Sgd), Adaptive Gradient (Adagrad), Adaptive Moment Estimation (Adam), and Root Mean Squared Propagation (Rmsprop). These optimization algorithms are applied to the Deep Convolutional Neural Network (DeepConvNet) in the subject of our specific problem statement, which involves credit card fraud detection (CCFD). After careful consideration of the problem’s nature, objective function characteristics, and computational aspects, we fnd that all four algorithms are suitable for our CCFD task. However, based on experimental results, it is evident that Rmsprop outperforms others, leading to a remarkable 99.93% in accuracy.

关键词： Optimization Algorithms Objective functions Convolutional Neural Network Credit Card Fraud Detection

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The necessity of machine learning in clinical emergency medicine;a narrative review of the current literature

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Materials Today: Proceedings 2022年 56卷 2075-2081页

作者： Rajendran, Ratchana Singirikonda, Bhagyalaxmi Navpreet Jain, Neetu Naved, Mohd Khaja Mohiddin, Md. Assistant Professor - Finance International School of Management Excellence Bengaluru India Lecturer in Computer Science Computer Science and Applications Government Degree College for Women Mahatma Gandhi University India Bk Birla College of Arts Commerce and Science Kalyan India Institute of Management and Research India Department of Business Analytics Jagannath University Delhi-NCR India Senior Assistant Professor Bhilai Institute of Technology Chhattisgarh Raipur India

Clinical databases may be categorized by medical experts as massive records that consist of massive portions of facts. These facts can include medical health sufferers and their scientific conditions. Analyzing the quantitative and qualitative medical records further to produce relationships amongst a massive variety of samples requires the use of machine learning strategies. These strategies will unveil hidden scientific know-how in phrases of correlation and affiliation of reputedly impartial variables. This review intends to focus on the use of a predictive set of rules for the prediction of trauma sufferers on admission to the health center if the medical industry wants to are expecting the vital remedy for sufferers and furnished the vital measures for the trauma sufferers who are earlier than getting into the crucial situation. Managing large data sets is a challenge for most medical systems that involve Artificial Intelligence. This study offers an overview of machine learning applications in medical medication, and specifically the ability to respond to emergencies. The relevant, recently posted research on machine learning and scientific records with a focal went through various investigation procedures. The goal of this process was to address professionals and the challenge of such approaches in clinical medicine. The effects of this look may be utilized in the prediction of trauma patient's reputations six hours after admission to the health center. © 2021

关键词： Machine learning

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A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and computer Vision

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Procedia computer science 2024年 233卷 343-352页

作者： Senthil G. A R. Prabha Arun M A. Sahaya Anselin Nisha S. Prabu Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Assistant Professor Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Professor Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Assistant Professor Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. The Geographic Information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. This provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. This research study focuses on the use of Predictive Data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. The proposed model makes use of a dataset that includes specific land subsidence circumstances as input. The prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). The proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction.

关键词： Land Subsidence Predictive Data Analysis (PDA) Geographic Information System Machine Learning computer Cision Susceptibility Mapping Stochastic Gradient Descent (SGD) Generative Adversarial Network (GAN) Long Short-Term Memory (LSTM) K-Nearest Neighbor (KNN) Artificial Neural Network (ANN) Google Earth Platform

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