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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Rani, K. Mary Sudha Prasad, V. Kamakshi Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence and Machine Learning Hyderabad India Jawaharlal Nehru Technological University Hyderabad Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331518943

Lung cancer remains a critical global health challenge, necessitating advanced predictive models to enhance diagnostic and prognostic accuracy. This study employs natural language processing (NLP) and deep learning to advance personalized lung cancer prognosis and treatment response prediction. Using a synthetic dataset, our novel neural network model integrates clinical data with patient narratives to extract meaningful insights, correlating patient transcripts with existing datasets. The model interprets lung cancer histology types, gene sets, and genetic mutations, achieving 85% accuracy in predicting treatment responses, with a sensitivity of 0.88 and specificity of 0.83. By merging clinical and transcript-based data, this research provides clinicians with a reliable tool to support personalized medicine and informed decision-making. The results highlight the potential of AI-driven approaches to improve disease-gene association analysis, paving the way for innovative computational methodologies in lung cancer research. © 2024 IEEE.

关键词： Lung cancer

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics 23

A Comparison of Prompt Engineering Techniques for Task Plann...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Bode, Jonas Pätzold, Bastian Memmesheimer, Raphael Behnke, Sven Computer Science Institute Vi - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Autonomous Intelligent Systems group Germany

ISBN: (纸本)9798350373578

Recent advances in Large Language Models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt-Engineering. © 2024 IEEE.

关键词： Human robot interaction

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Detection of Medicinal Plants Using machine learning 12th

Detection of Medicinal Plants Using Machine Learning

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Meta-Auto-Decoder:a Meta-learning-Based Reduced Order Model for Solving Parametric Partial Differential Equations

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Communications on Applied Mathematics and Computation 2024年第2期6卷 1096-1130页

作者： Zhanhong Ye Xiang Huang Hongsheng Liu Bin Dong Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230027AnhuiChina Central Software Institute Huawei Technologies Co.LtdHangzhou 310007ZhejiangChina Center for Machine Learning Research Peking UniversityBeijing 100871China

Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational domains,*** reduced order modeling techniques accelerate the solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the ofline *** methods often need a predefined mesh as well as a series of precomputed solution snapshots,and may struggle to balance between the efficiency and accuracy due to the limitation of the linear *** the nonlinear representation of neural networks(NNs),we propose the Meta-Auto-Decoder(MAD)to construct a nonlinear trial manifold,whose best possible performance is measured theoretically by the decoder *** on the meta-learning concept,the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training *** adaptation to new(possibly heterogeneous)PDE parameters is enabled by searching on this trial manifold,and optionally fine-tuning the trial manifold at the same *** numerical experiments show that the MAD method exhibits a faster convergence speed without losing the accuracy than other deep learning-based methods.

关键词： Parametric partial differential equations(PDEs) Meta-learning Reduced order modeling Neural networks(NNs) Auto-decoder

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Comparative Assessment of machine learning Models for Predicting Glucose Intolerance Risk

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SN computer science 2024年第7期5卷 894页

作者： Kumar, B. P. Pradeep Manoj, H.M. Department of Computer Science and Design Atria Institute of Technology Affiliated to VTU Karnataka Bangalore India Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Affiliated to VTU Karnataka Bangalore India

This study focuses on predictive analytics and healthcare, specifically the prediction of Glucose Intolerance, a chronic metabolic disease with significant global health implications. The research aims to develop efficient tools for risk assessment and early identification, offering medical practitioners reliable instruments for identifying high-risk patients and implementing preventive measures in a timely manner. A significant challenge in managing Glucose Intolerance is the absence of effective and precise prediction models. Traditional risk assessment techniques often fail to capture the multifaceted nature of Glucose Intolerance development, leading to delayed treatments and suboptimal patient outcomes. To address this issue, we conducted a comprehensive study utilizing various machine learning algorithms, including Decision Trees, Random Forest, Gradient Boosting, CatBoost, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression, and a Voting Classifier, to predict Glucose Intolerance. The primary objective was to identify the most effective combination of features and models for accurate predictions. Key factors considered included patient demographics, lifestyle characteristics, medical history, and genetic susceptibility, which were used to build robust and personalized prediction models. We conducted a comparative analysis of the machine learning models' performance based on cross-validated accuracy with test-train splits and folds: 0.2, 6;0.05, 4;0.1, 2;0.2, 6;and 0.05, 2. The results showed that Random Forest achieved test accuracies of 74%, 76%, 82%, 75%, and 79%;KNN achieved 70%, 72%, 74%, 75%, and 72%;SVC achieved 74%, 72%, 77%, 77%, and 72%;Logistic Regression achieved 73%, 76%, 79%, 73%, and 76%;Gradient Boosting achieved 72%, 79%, 75%, 73%, and 79%;XGBoost achieved 72%, 79%, 77%, 74%, and 79%;CatBoost achieved 74%, 76%, 79%, 75%, and 76%;Decision Tree achieved 60%, 83%, 74%, 70%, and 79%;and Voting Classifier achieved 74%, 7

关键词： Algorithmic approaches Chronic disease Data-driven healthcare Early detection Glucose Intolerance Healthcare analytics machine learning Personalized medicine Prediction Predictive modelling Risk stratification

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Hybrid Design for Privacy Preserved Image Representation in a Cloud Environment 12th

Hybrid Design for Privacy Preserved Image Representation in ...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Vijay, K. Sorna Shanthi, D. Jaeyalakshmi, M. Vignesh, P. Yuvaraja, M. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The digital era has made seamless sharing and keeping of media such as images on cloud platforms an integral part of our lives. Still, there is a big issue about user privacy and data security in these repositories. We have thus created a very strong system that is able to do image encryption and decryption so as to protect the privacy of users while at the same time making their pictures accessible. Our system uses hybrid encryption where we try to prevent even cloud service providers from accessing the original files hence ensuring that only the owner who has been authenticated through password based authentication can decrypt or access them. In this way, it allows maintaining high privacy standards without sacrificing user convenience. It starts with preparing photos for encryption so that they are kept safe during transmission and storage. When needed by a user, images are decrypted by our system and become available only to authorized users. Consequently, users can comfortably leave their private photo albums on cloud storage systems knowing too well that their friends cannot access them thereby increasing trust in using cloud services for storing sensitive information. The aim of this work is to increase user privacy and data security in cloud repositories through employing this encryption technique. The users are guaranteed that their private details are not exposed to others enabling them to use cloud storage efficiently without interfering with their privacy at all. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Recognition and Classification of Apple and Sugarcane Plant Leaf Diseases using SVM with DAE Models 2

Recognition and Classification of Apple and Sugarcane Plant ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sunil Kumar, V. Jyothi, A.P. Sumanth, S. Nagamani, H.S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Peenya Bengaluru India Department of Computer Science and Computer Applications Kolar India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350382952

For this leaf disease study, apple and sugarcane plants are identified and classified using a mix of character features and in-depth automated coding. Farm sugar cane leaves were copied for the online apple Plant Village Dataset. Pre-process images to reduce size and improve HLG filters. Image segmentation using DWT and GLCM feature extraction follows. Like the Low-Low (LL) band, DWT images produce 4-orthogonal subbands. Deep Auto Encoder and SVM algorithms retrieved subgroup functions and identified diseases from training data. If classification findings are ignored, the division stage receives data with HL, LH, and HH bands to explain approach consistency. Deep Auto encoder classifies illness using training data after extracting sub-band features. The Auto Encoder and SVM classifier demonstrated that the proposed model outperformed alternatives in LL band. Other subbands performed worse than the LL band. This study found that LL sub band data provided fine image data for better sickness categorization than HH, HL, and LH sub band data. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Early Melanoma Detection and Classification Using CNN and Confusion Matrix Analysis

Early Melanoma Detection and Classification Using CNN and Co...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dinesh Kumar, K. Deepthi, K. Jaya Saravanakumar, S. Balamurugan, S. Govindharaj, I. Reddeppa, Pulikandla Akshay Vel Tech University Department of Computer Science and Engineering Chennai India Learning Mohan Babu University Department of Artificial Intelligence and Machine Tirupati India Paavai Engineering College Department of Information Technology Namakkal India Bengaluru India Learning Vel Tech University Department of Artificial Intelligence and Machine Chennai India

ISBN: (纸本)9798331510022

This article brings up a new method for detecting and classifying skin cancers using Convolutional Neural Networks (CNN) and Confusion Matrix analysis. The main work of this work evolves around detection of Melanoma and Non Melanoma cells. The large dataset on skin cancers is used to teach the CNN model to accurately classify different types of skin lesions into different malignant and non-cancerous groups. The integration of Confusion Matrix, in this case, allows for accurate identification of the classification errors made by the model as well as possible areas for improvement hence making it possible to comprehensively evaluate how well the model performs. Therefore, early recognition is indispensable to successful treatment as skin cancer is a typical yet fatal condition. Results shows that the suggested proposed model achieves 95.5% accuracy and performance in comparison with other methods found in literature. Additionally, Modified DenseNet201 model has a sensitivity of 95.96% as well as a specificity of 98.03%. © 2024 IEEE.

关键词： Lung cancer

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Avoidance of Ship Collision Using Deep Reinforcement learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces 2

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Adnan, Myasar Mundher Sunil Kumar, V. Srinivas, T. Aditya Sai Aluvala, Srinivas Rajalakshmi Engineering College Artificial Intelligence & Machine Learning Department Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering Sr University Warangal India

ISBN: (纸本)9798350373110

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values. © 2024 IEEE.

关键词： Deep reinforcement learning

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Life Expectancy Prediction using machine learning Technique - KNN 3

Life Expectancy Prediction using Machine Learning Technique ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Narooka, Preeti Vashistha, Aarushi Mitra, Priyanka Derashri, Gulisha School of Computer Science Engineering Manipal University Department of Artificial Intelligence and Machine Learning Rajasthan Jaipur303007 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur303905 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350384598

In the modern world, life expectancy turns out to be the most valuable factor in determining population's health. Today, it is more crucial to predict the future because life is more precarious due to a variety of circumstances, including social, economic, environmental, educational, lifestyle, and health conditions. The machine learning algorithm KNN for life expectancy is presented in this work. The dataset gathered for this study is separated into a training set (70%) and a test set (30%), and the forecasts precision is examined. To avoid misclassification, KNN needs a larger sample size, and the best K are chosen more in accordance with cross-validation experience than with strong statistical theory. © 2024 IEEE.

关键词： Adversarial machine learning

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