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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Joon, Rakesh Kumar Anitha, R. Selvan, Saravana Latha Mageshwari, P.S. Gayathri Priya, S. Malathi, K. Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana Kablana124104 India S.A. Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai600077 India School of Professional Engineering Manukau Institute of Technology Auckland2104 New Zealand Tamil Nadu Kavaraipettai601206 India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai601206 India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

Wireless Sensor Networks (WSNs) are essential in the collection of real time data across different fields, including environmental monitoring and process control. However, due to the bounded amount of energy in sensor nodes, network lifetime becomes an issue, which hinders performance and reliability. Thus, optimization of WSN performance in an attempt to minimize energy consumption is essential for reliable data transmission. In conventional routing and clustering models, network load distribution is not achieved adequately, resulting in a more significant loading of nodes and faster exhaustion of energy. To solve these problems, it is necessary to carefully design load distribution and path finding algorithms to support long-term WSN operation. The objective of this research is to design a framework-Ski-CDiCo-BO-that integrates Skill Optimization for clustering, and ensuring an energy-efficient paths while reducing redundant transmissions on nodes and conserving node energies, and uses the Causal Dilated Cosine Architecture for sophisticated load balancing, and Bowerbird Optimization for improving the parameterization of neural networks. Analysis reveals that the presented Ski-CDiCo-BO framework provides improved performance of various parameters including data delivery success rate of above 99.8% and network lifetime gain by up to 99.5%. Also, load distribution accuracy remains above 99.3%, which indicates that the framework is capable of avoiding node overload and end-to-end delay improved upon by 99.6%, indicating that the solution maintains high parameters even in dynamically changing conditions. In conclusion, Ski-CDiCo-BO maximizes WSN efficiency by implementing cutting-edge clustering, load balancing, and optimization techniques, setting new standards for energy-conscious and reliable sensor network performance across diverse applications. © 2025 IEEE.

关键词： Sensor nodes

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Conversational Symptom Checker Chatbot for Disease Prediction and Suggesting Nearby Hospitals using machine learning and Location Services

Conversational Symptom Checker Chatbot for Disease Predictio...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Rushikesh Burle Swapnil Gundewar Sanskruti Gaurkhede Bhushan Fulkar Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Computer Science And Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

A chatbot, sometimes known as a conversational agent, is software that uses natural language to interact with people. Travelling to and from a hospital for treatment takes a lot of time for people with hectic schedules, which makes many of them disregard their health issues. People frequently postpone going to the hospital for small problems that might develop into serious illnesses. For these people, interacting with chatbots to get helpful medical information is a cost-effective, time-efficient, and convenient option. This paper suggests a framework for a machine learning-based chatbot that combines natural language, machine learning, and statistical analysis. gaining knowledge of the technologies in a Spark cluster to help create an intelligent chatbot. In this work, we conducted a thorough analysis of current literature, looking at a large number of chatbot-related papers over the last five years. Next, we introduced many related tasks to our chatbot, which uses user symptoms to predict diseases and, depending on the user’s location, recommends hospitals in the area. Users will be able to easily engage with the chatbot using the system’s online interface.

关键词： Recurrent neural networks Hospitals Statistical analysis machine learning Chatbots Tokenization Sparks Medical diagnostic imaging Long short term memory Diseases

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NC-ALG: Graph-Based Active learning Under Noisy Crowd

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

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International Conference on data Engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks Labeling Noise measurement

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Ai-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management

Ai-Driven Automated Tool for Abdominal CT Body Composition A...

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IEEE International Symposium on Biomedical Imaging

作者： Xinyu Nan Meng He Zifan Chen Bin Dong Lei Tang Li Zhang Center for Data Science Peking University China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education) Peking University Cancer Hospital and Institute Beijing China Beijing International Center for Mathematical Research (BICMR) Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. Research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal tissue composition are time-consuming and costly, limiting clinical research scalability. To address these challenges, we developed an AI-driven tool for automated analysis of abdominal CT scans to effectively identify and segment muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view localization model and a high-precision 2D nnUNet-based segmentation model, demonstrating a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation. Furthermore, it features an interactive interface that allows clinicians to refine the segmentation results, ensuring high-quality outcomes effectively. Our tool offers a standardized method for effectively extracting critical abdominal tissues, potentially enhancing the management and treatment for gastrointestinal cancers. The code is available at https://***/NanXinyu/***.

关键词： Location awareness Analytical models Accuracy Computed tomography Scalability Manuals Muscles Gastrointestinal tract Fats Cancer

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Smart Telemedicine Using IoT by Integrating 5G and Block-Chain Techniques

Smart Telemedicine Using IoT by Integrating 5G and Block-Cha...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Shailee Lohmor Choudhary Rinku Sharma Dixit Deepak Das K. Ranjith Singh V. Dinesh Babu Department of Artificial Intelligence & Machine Learning - Data Science New Delhi Institute of Management New Delhi India Agricultural Engineering Karunya Institute of Science and Technology Coimbatore India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Information Technology Karpagam Institute of Technology Coimbatore India

There are many medical applications for the Internet of Health Things (IoHT). In order to offer patients with more intelligent and effective health diagnoses, contemporary IoHT incorporates wellness items such sensors with continuously monitored medical equipment for the evaluation and handling of a medical chart. Using the combination of 5G networks and ethereum technology, we presented the IoT using a fog medical decision assistance system in this study for the forecast and monitoring of illness along with its severities. Due to its visibility, a frame provides a method for keeping and exchanging knowledge which is safe. Ethereum has various uses in the medical industry and therefore can enhance monitoring equipment, clinical study storage systems, mobile medical software, exchanging and keeping of digital media files, and insurer details stockpiling. The suggested structure will assemble a cloud provider will hold client information from the health equipment that's going to be connected to the individual, along with any pertinent health records. The use of 5G with cryptocurrency technologies allows the reliable transfer of patient information at high transmission rates with short reaction times. In addition, a Deep Neural (NN) classification is employed to forecast illnesses as well as the degree of underlying severity. The suggested model is tested using a variety of classifiers. In order to determine which classification is appropriate for the information, the effectiveness of various classifier is compared by contrasting the results. The NN improved a 98.98 average accuracy. This NN is additionally trained on the information such so this can forecast the outcome of the information category that carries no labels. A taught Naive Bayes is more accurate than conventional classifications at making predictions and effectively displaying the findings.

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Exploring Deep Neural Networks for Accurate Diabetic Retinopathy Prediction

Exploring Deep Neural Networks for Accurate Diabetic Retinop...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： T. Kujani P. Arivubrakan K. Raju P. Mani Sankar B. Surekha Reddy E. Bhavana Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Artificial Intelligence and Data Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Diabetic Retinopathy (DR) emerges as a serious complication associated with diabetes mellitus, causing damage to retinal blood vessels, and resulting in vision loss. The critical need for early detection and effective management to curb the progression towards vision impairment and potential blindness underscores the significance of this issue. To address these challenges, have propose a novel deep learning system utilizing a Densely connected Convolutional Network (DenseNet-201) for the efficient detection of DR. This model is specifically trained to classify fundus images into six distinct categories: no DR, mild, moderate, and severe, proliferative DR, and hard exudates. The datasets utilized in this study include the publicly available Diabetic Retinopathy Detection 2015 and Aptos 2019 blindness detection datasets from Kaggle. The proposed system comprises multiple stages, including data Collection, Preprocessing, Augmentation, and Modeling. During the preprocessing phase, normalization techniques are to eliminate biases or distortions present in the fundus images. Furthermore, the augmentation of data is employed to enhance the diversity and quantity of the training dataset. In the modeling stage, have designed and implemented the deep learning model, with the robust and high-performing DenseNet-201 architecture selected as the model’s backbone. The model exhibits a significant accuracy of 87.7% in its classification task. Additionally, have introduced a Deep Neural Network (DNN) model, achieving an impressive accuracy rate of 95.8%. These results underscore the effectiveness of our proposed system in accurately and efficiently detecting DR. The primary objective of this research is to develop a reliable and automated system for Diabetic Retinopathy detection, with the overarching goal of improving the quality of life for individuals affected by diabetes.

关键词： Deep learning Training Diabetic retinopathy Transfer learning Visual impairment Blindness Robustness

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Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond

The Journal of Machine Learning Research

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The Journal of machine learning Research 2023年第1期24卷 1339-1349页

作者： Anna Hedström Leander Weber Dilyara Bareeva Daniel Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne Understandable Machine Intelligence Lab TU Berlin Berlin Germany Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin Germany Department of Computer Science University of Potsdam Potsdam Germany Department of Electrical Engineering and Computer Science TU Berlin and Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin Germany Understandable Machine Intelligence Lab TU Berlin Berlin Germany and BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin Germany

The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus--a comprehensive, evaluation toolkit in Python that includes a growing, well-organised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://***/understandable-machine-intelligence-lab/Quantus/).

关键词： explainability responsible AI reproducibility open source Python

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Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems

arXiv

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arXiv 2024年

作者： González, Tomás Guzmán, Cristóbal Paquette, Courtney Machine Learning Department School of Computer Science Carnegie Mellon University United States Institute for Mathematical and Computational Engineering Faculty of Mathematics School of Engineering Pontificia Universidad Católica de Chile Chile Department of Mathematics and Statistics McGill University Google Deepmind Canada

We study the problem of differentially-private (DP) stochastic (convex-concave) saddle-points in the polyhedral setting. We propose (Ε, δ)-DP algorithms based on stochastic mirror descent that attain nearly dimension-independent convergence rates for the expected duality gap, a type of guarantee that was known before only for bilinear objectives. For convex-concave and first-order-smooth stochastic objectives, our algorithms attain a rate of (Formula Presented), where d is the dimension of the problem and n the dataset size. Under an additional second-order-smoothness assumption, we improve the rate on the expected gap to (Formula Presented). Under this additional assumption, we also show, by using bias-reduced gradient estimators, that the duality gap is bounded by (Formula Presented) with constant success probability. This result provides evidence of the near-optimality of the approach. Finally, we show that combining our methods with acceleration techniques from online learning leads to the first algorithm for DP Stochastic Convex Optimization in the polyhedral setting that is not based on Frank-Wolfe methods. For convex and first-order-smooth stochastic objectives, our algorithms attain an excess risk of (Formula Presented), and when additionally assuming second-order-smoothness, we improve the rate to plog(d)/n + log(d)/√nΕ. Instrumental to all of these results are various extensions of the classical Maurey Sparsification Lemma, which may be of independent interest. © 2024, CC BY.

关键词： Mirrors

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Does the Definition of Difficulty Matter? Scoring Functions and their Role for Curriculum learning

arXiv

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arXiv 2024年

作者： Rampp, Simon Milling, Manuel Triantafyllopoulos, Andreas Schuller, Björn W. CHI Chair of Health Informatics Technical University of Munich Munich Germany MCML Munich Center for Machine Learning Munich Germany GLAM Group on Language Audio & Music Imperial College London United Kingdom MDSI Munich Data Science Institute Munich Germany

Curriculum learning (CL) describes a machine learning training strategy in which samples are gradually introduced into the training process based on their difficulty. Despite a partially contradictory body of evidence in the literature, CL finds popularity in deep learning research due to its promise of leveraging human-inspired curricula to achieve higher model performance. Yet, the subjectivity and biases that follow any necessary definition of difficulty, especially for those found in orderings derived from models or training statistics, have rarely been investigated. To shed more light on the underlying unanswered questions, we conduct an extensive study on the robustness and similarity of the most common scoring functions for sample difficulty estimation, as well as their potential benefits in CL, using the popular benchmark dataset CIFAR-10 and the acoustic scene classification task from the DCASE2020 challenge as representatives of computer vision and computer audition, respectively. We report a strong dependence of scoring functions on the training setting, including randomness, which can partly be mitigated through ensemble scoring. While we do not find a general advantage of CL over uniform sampling, we observe that the ordering in which data is presented for CL-based training plays an important role in model performance. Furthermore, we find that the robustness of scoring functions across random seeds positively correlates with CL performance. Finally, we uncover that models trained with different CL strategies complement each other by boosting predictive power through late fusion, likely due to differences in the learnt concepts. Alongside our findings, we release the aucurriculum toolkit (https://***/autrainer/aucurriculum), implementing sample difficulty and CL-based training in a modular fashion. © 2024, CC BY-NC-SA.

关键词： Contrastive learning

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Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set

arXiv

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arXiv 2025年

作者： Dai, Gordon Ravishankar, Pavan Yuan, Rachel Neill, Daniel B. Black, Emily Machine Learning for Good Laboratory Department of Computer Science NYU Courant Institute of Mathematical Sciences New York University United States Robert F. Wagner Graduate School of Public Service New York University United States Center for Urban Science and Progress Tandon School of Engineering New York University United States Center for Data Science New York University United States Department of Computer Science and Engineering Tandon School of Engineering New York University United States

When selecting a model from a set of equally performant models, how much unfairness can you really reduce? Is it important to be intentional about fairness when choosing among this set, or is arbitrarily choosing among the set of "good" models good enough? Recent work has highlighted that the phenomenon of model multiplicity—where multiple models with nearly identical predictive accuracy exist for the same task—has both positive and negative implications for fairness, from strengthening the enforcement of civil rights law in AI systems to showcasing arbitrariness in AI decision-making. Despite the enormous implications of model multiplicity, there is little work that explores the properties of sets of equally accurate models, or Rashomon sets, in general. In this paper, we present five main theoretical and methodological contributions which help us to understand the relatively unexplored properties of the Rashomon set, in particular with regards to fairness. Our contributions include methods for efficiently sampling models from this set and techniques for identifying the fairest models according to key fairness metrics such as statistical parity. We also derive the probability that an individual’s prediction will be flipped within the Rashomon set, as well as expressions for the set’s size and the distribution of error tolerance used across models. These results lead to policy-relevant takeaways, such as the importance of intentionally looking for fair models within the Rashomon set, and understanding which individuals or groups may be more susceptible to arbitrary decisions. © 2025, CC BY.

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