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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Sivakumar, S. Dharani, U. Hephzibah, Dineep Srm Institute Of Science And Technology Department Of Computer Science And Engineering Tiruchirapalli India Srm Institute Of Science And Technology Dept. Of Comp. Sci. And Engineering Specialization In Artificial Intelligence And Machine Learning Tiruchirapalli India

ISBN: (纸本)9798350352689

The analysis of soil nutrients and water levels holds utmost significance in the realms of agriculture. Determining the soil type and identifying the crop suitable for that specific soil are critical factors in optimizing agricultural yield. These considerations play a pivotal role in precision agriculture, ensuring that the chosen crops align with the soil's characteristics. This strategic alignment enhances the efficiency of cultivation practices, promotes sustainable agricultural methods, and ultimately contributes to increased crop yields. In this research paper, we systematically identify key soil properties, including NPK concentrations, temperature, humidity, and soil pH. The collected dataset, encompassing these crucial soil attributes, is utilized to train machine learning models. The primary goal is to forecast the soil type utilizing the gathered data, and subsequently, undertake further analyses, to ascertain the relevant crop types that are most suitable for the identified soil characteristics. This comprehensive approach involves the integration of data-driven insights and machine learning techniques to enhance our understanding of soil composition and facilitate informed decision-making in agriculture. © 2024 IEEE.

关键词： Soil testing

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Unveiling Customer Segmentation Patterns in Credit Card data using K-Means Clustering: A machine learning Approach

Unveiling Customer Segmentation Patterns in Credit Card Data...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Priyadarshni, Sakshi Fathima, Rakshan Urolagin, Siddhaling Bongale, Anupkumar M. Dharrao, Deepak Sudhakar Birla Institute of Technology and Science Dept. of Computer Science Pilani Dubai United Arab Emirates Symbiosis Institute of Technology Dept. of Artificial Intelligence and Machine Learning Pune India Symbiosis Institute of Technology Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350393415

Customer segmentation plays a crucial role in business development by providing deep insights into customer behaviors and preferences, facilitating effective and personalized marketing strategies. However, traditional segmentation methods often fall short of accurately analyzing large and complex datasets. In recent years, the application of machine learning techniques has transformed the field of customer segmentation, enabling automated analysis, enhanced accuracy, and the discovery of intricate patterns and key trends. This research paper provides a detailed review of the application of K-Means clustering in credit card companies for effective customer segmentation. It explores the benefits, challenges, and practical considerations of utilizing K-Means clustering, along with real-world case studies highlighting trends and potential advancements for using K-Means clustering in credit card customer segmentation. © 2023 IEEE.

关键词： K-means clustering

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Natural Language Processing based Automated Essay Scoring with Parameter-Efficient Transformer Approach 6

Natural Language Processing based Automated Essay Scoring wi...

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Sethi, Angad Singh, Kavinder Delhi Technological University Machine Learning Research Lab Dept. of Computer Science Delhi India

ISBN: (纸本)9781665410281

Existing automated scoring models implement layers of traditional recurrent neural networks to achieve reasonable performance. However, the models provide limited performance due to the limited capacity to encode long-term dependencies. The paper proposed a novel architecture incorporating pioneering language models of the natural language processing community. We leverage pre-trained language models and integrate it with adapter modules, which use a bottle-neck architecture to reduce the number of trainable parameters while delivering excellent performance. We also propose a model by re-purposing the bidirectional attention flow model to detect adversarial essays. The model we put forward achieves state-of-the-art performance on most essay prompts in the Automated Student Assessment Prize data set. We outline the previous methods employed to attempt this task, and show how our model outperforms them. © 2022 IEEE.

关键词： Bottles

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Rotation-Invariant Self-Supervised Representation learning for Object Detection in Autonomous Driving 2

Rotation-Invariant Self-Supervised Representation Learning f...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Marathe, Aboli Roy, Sidharth Singh, Devansh Walambe, Rahee Kotecha, Ketan Machine Learning Dept. Carnegie Mellon University Pittsburgh United States Dept. of Computer and Information Science University of Pennsylvania Philadelphia United States Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International University Pune India

ISBN: (纸本)9798331504106

This paper describes a self supervised representation learning approach that can perform robust object detection in out-of-distribution rotated images for autonomous driving task. Keeping in mind the limitations of vehicle sensors we incorporate self-supervised rotation invariance for correcting rotational corruptions. We present a comprehensive analysis of observed label shifts, validate the results using holdout sets and provide explainability for the performance uisng GradCAM appaorach. We demonstrate results on real-world adversarial examples: (Autonomous Scenes - DAWN dataset) that significantly outperform common methods. The proposed framework outperforms the rotation-affected benchmark by 14.73 points of mAP for the OD task in autonomous driving applications. ©2024 IEEE.

关键词： Self-supervised learning

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Transforming Text to Video: Leveraging Advanced Generative AI Techniques

Transforming Text to Video: Leveraging Advanced Generative A...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： P Indira Priya M Manju R Anitha R Harsavardhini S Sri Harini Dept. of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Dept. of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

This comprehensive evaluation paper delves into the realm of textual content-to-video generation using artificial intelligence. The authors present an inclusive analysis of key reference papers: VGEN - Text to Video Generation Using AI, MAGVIT, STF, DirecT2V, Text2Video-Zero, Tune-A-Video, and four supplementary papers. These manuscripts collectively address various challenges and propose groundbreaking methodologies for producing high-quality videos from textual prompts. VGEN aims to synthesize videos from textual descriptions using pioneering approaches inspired by the strategic tokenization and embedding techniques employed in MAGVIT. STF introduces a cutting-edge 3D generative model specifically designed for text-to-video generation, while DirecT2V focuses on developing lightweight video diffusion models that operate within a low-dimensional latent space. Text2Video-Zero explores zero-shot video generation using diffusion models, and Tune-A-Video introduces a one-shot tuning method that leverages image diffusion models to generate videos. The additional papers contribute to advancements in latent diffusion models, video LDMs, and spatio-temporal attention mechanisms. By incorporating these techniques, the project aims to generate high-fidelity, long-duration videos with content consistency and motion coherence. Code and models are provided for reproducibility, encouraging collaboration in the field. Extensive qualitative and quantitative evaluations demonstrate the effectiveness and versatility of the approaches in producing realistic and diverse videos aligned with textual descriptions. This comprehensive review paper serves as a guide to current strategies in text-to-video technology, offering insights and directions for future research in this exciting field.

关键词： Translation Three-dimensional displays Statistical analysis Streaming media Diffusion models Aerodynamics Reproducibility of results Tokenization Text to video Tuning

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Biomedical Interventions in Precision Medicine for Diabetics 3

Biomedical Interventions in Precision Medicine for Diabetics

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Madhumitha, A. Dhananjayan, V. Bannari Amman Institute of Technology Dept. of Computer Science and Bussiness System Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Dept. of Artificial Intelligence and Machine Learning Tamil Nadu Sathyamangalam India

ISBN: (纸本)9798350370867

Precision medicine is the need of the hour strategy that deals with precision from diagnosis to treatment. There is a growing need for continuous monitoring of blood glucose levels and an effective algorithm to determine the right insulin dosages to achieve precision medicine for diabetes. This work intends to review the impact and importance of biomedical interventions in precision medicine for diabetes. It addresses the barriers that persist in practicing precision medicine for diabetes by strengthening the techniques of precision diagnosis and prognosis, precision therapeutics, precision treatments, and precision monitoring using predictive algorithms. The review emphasizes the significance of biomedical interventions in advancing the future directions of precision medicine for diabetes, including the integration of clinical trials with large datasets and continuous evaluation and monitoring of patients through regular measurements of blood glucose levels. By focusing on these biomedical interventions, this review aims to provide insights into the role of precision medicine in diabetes and its potential for improving patient outcomes. © 2023 IEEE.

关键词： Diagnosis

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Decision-Focused Evaluation of Worst-Case Distribution Shift

arXiv

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arXiv 2024年

作者： Ren, Kevin Byun, Yewon Wilder, Bryan Statistics and Data Science Dept. Carnegie Mellon University PittsburghPA United States Machine Learning Dept. Carnegie Mellon University PittsburghPA United States

Distribution shift is a key challenge for predictive models in practice, creating the need to identify potentially harmful shifts in advance of deployment. Existing work typically defines these worst-case shifts as ones that most degrade the individual-level accuracy of the model. However, when models are used to make a downstream population-level decision like the allocation of a scarce resource, individual-level accuracy may be a poor proxy for performance on the task at hand. We introduce a novel framework that employs a hierarchical model structure to identify worst-case distribution shifts in predictive resource allocation settings by capturing shifts both within and across instances of the decision problem. This task is more difficult than in standard distribution shift settings due to combinatorial interactions, where decisions depend on the joint presence of individuals in the allocation task. We show that the problem can be reformulated as a submodular optimization problem, enabling efficient approximations of worst-case loss. Applying our framework to real data, we find empirical evidence that worst-case shifts identified by one metric often significantly diverge from worst-case distributions identified by other metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Hierarchical systems

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Indian Food Segmentation and Calorie Estimation Using CatBoost and Masked Convolutional Neural Networks

Indian Food Segmentation and Calorie Estimation Using CatBoo...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Acharya, Nabin Singh, Ayush Kumar Dwivedi, Anshuman Kumar Jayanthi, M.G. Kannadaguli, Prashant Cambridge Institute of Technology Dept. of Information Science and Engineering Bengaluru India Cambridge Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bengaluru India Cambridge Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

ISBN: (纸本)9798350300826

Accurate food segmentation and calorie estimation play a pivotal role in effective dietary monitoring and health management. In this paper, after extensive experimentation on a diverse Indian food dataset of 20 classes collected by us, we present two separate innovative models designed to address these tasks with exceptional precision. The first model employs a machine learning approach by incorporating the CatBoost algorithm, while the second model leverages a deep learning technique utilizing the U-Net architecture for high-quality image segmentation. Additionally, we merge the U-Net outcomes with a convolutional neural network (CNN) to enhance the deep learning-based classification. Our models effectively handle categorical features and address imbalanced data, resulting in significantly improved accuracy in food item delineation within images. This advancement enables a more reliable and comprehensive analysis of dietary patterns. We demonstrate the effectiveness and robustness of our proposed models. Comparative evaluations against state-of-the-art methods verify exceptional performance in terms of segmentation accuracy, precision in food classification, and estimation of calorie intake. Notably, our models are specifically tailored for food images and do not take ingredients or any other information into consideration. In comparison CNN model is performs better than CatBoost, achieving an accuracy of 78%. © 2023 IEEE.

关键词： Convolutional neural networks

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Lung Nodule Segmentation and Classification Using Conv-Unet Based Deep learning

Lung Nodule Segmentation and Classification Using Conv-Unet ...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Akash, V. Monish Sai, R. Amith, K.B. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bangalore India Cambridge Institute of Technology Dept. of Information Science and Engineering Bangalore India Cambridge Institute of Technology Dept. of Computer Science and Engineering Bangalore India Dhaarini Academy of Technical Education Bangalore India

ISBN: (纸本)9798350300826

This paper proposes a Convolutional U-Net architecture, a variation of the standard U-Net architecture for the segmentation of lung nodules and classification using Deep learning on Computerized Tomography (CT) scans. The Primary steps employed are Preprocessing, Segmentation and Classification of nodules. In the preprocessing step, the lung region is segmented using techniques such as normalization, median filtering, Kmeans clustering, morphological and thresholding operations to extract lung Region of Interest (ROI) and nodule masks. The Conv-Unet design adds more convolutional layers to the standard U -Net architecture to help capture complicated patterns and boundaries of lung nodules for more accurate segmentation. Categorization of the segmented lung nodules is done using a CNN network on the LIDC-IDRI, and LUNA16 dataset. Overall, this model achieves a dice score of 62% and classification accuracy of 82% displaying appropriate performance in comparison with other variations of the U-Net architecture. © 2023 IEEE.

关键词： Convolution

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