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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Yesankar, Prajyot Puri, Chetan Barahate, Aditya Gote, Pradanywant M. Hajbe, Janhavi Pawar, Adesh Department of Computer Science & Design Maharashtra Wardha442001 India Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

machine learning (ML) has been a main force behind important breakthroughs in patient tracking, personalized medicine, medical tests, and operating efficiency in the healthcare business in recent years. machine learning algorithms provide unique insights into early disease identification, picture analysis, and prediction analytics, improving diagnosis accuracy and treatment results. These programs are able to examine big and complex information. By predicting treatment effectiveness and genetic risks, machine learning (ML) allows personalized medicine by allowing customized care through tailored therapy approaches. Furthermore, preventative health management is backed by ML-driven prediction analytics and online patient tracking, especially for chronic illnesses. Operationally, machine learning increases process efficiency and cuts costs, allowing for data- driven decision-making and individualized patient care. However, there are important ethics issues with machine learning acceptance in the healthcare business as well, such as data protection, computer bias, and legal problems. This study offers a full analysis of the state-of-the-art machine learning applications in healthcare, stressing their revolutionary potential while discussing the ethics issues and hurdles that must be addressed to ensure execution that is both responsible and fair. © 2025 IEEE.

关键词： Personalized medicine

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PAC-Bayes-Chernoff bounds for unbounded losses 38

PAC-Bayes-Chernoff bounds for unbounded losses

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Casado, Ioar Ortega, Luis A. Pérez, Aritz Masegosa, Andrés R. Spain Machine Learning Group Computer Science Dept. EPS Universidad Autónoma de Madrid Spain Department of Computer Science Aalborg University Denmark

We introduce a new PAC-Bayes oracle bound for unbounded losses that extends Cramér-Chernoff bounds to the PAC-Bayesian setting. The proof technique relies on controlling the tails of certain random variables involving the Cramér transform of the loss. Our approach naturally leverages properties of Cramér-Chernoff bounds, such as exact optimization of the free parameter in many PAC-Bayes bounds. We highlight several applications of the main theorem. Firstly, we show that our bound recovers and generalizes previous results. Additionally, our approach allows working with richer assumptions that result in more informative and potentially tighter bounds. In this direction, we provide a general bound under a new model-dependent assumption from which we obtain bounds based on parameter norms and log-Sobolev inequalities. Notably, many of these bounds can be minimized to obtain distributions beyond the Gibbs posterior and provide novel theoretical coverage to existing regularization techniques. © 2024 Neural information processing systems foundation. All rights reserved.

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A Survey on Graph Neural Networks and its Applications in Various Domains

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SN computer science 2025年第1期6卷 1-12页

作者： Murgod, Tejaswini R. Reddy, P. Srihith Gaddam, Shamitha Sundaram, S. Meenakshi Anitha, C. Department of Artificial Intelligence & Machine Learning BNM Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering NITTE Meenakshi Institute of Technology Karnataka Bengaluru India

Graph Neural Networks (GNNs) are neural models that use message transmission between graph nodes to represent the dependency of graphs. Variants of Graph Neural Networks (GNNs), such as graph recurrent networks (GRN), graph attention networks (GAT), and graph convolutional networks (GCN), have shown remarkable results on a variety of deep learning tasks in recent years. In this study, we offer a generic design pipeline for GNN models, go over the variations of each part, classify the applications in an organized manner, and suggest four outstanding research issues. Dealing with graph data, which provides extensive connection information among pieces, is necessary for many learning tasks. A model that learns from graph inputs is required for modelling physics systems, learning molecular fingerprints, predicting protein interfaces, and identifying illnesses. Reasoning on extracted structures (such as the dependency trees of sentences and the scene graphs of photos) is an important research issue that also requires graph reasoning models in other domains, such as learning from non-structural data like texts and images. Graph Neural Networks (GNNs) are primarily designed for dealing with graph-structured data, where relationships between entities are modeled as edges in a graph. While GNNs are not traditionally applied to image classification problems, researchers have explored ways to leverage graph-based structures to enhance the performance of Convolutional Neural Networks (CNNs) in certain scenario. GNN have been increasingly applied to Natural Language Processing (NLP) tasks, leveraging their ability to model structured data and capture relationships between elements in a graph. GNN are also applied for traffic related problems particularly in modeling and optimizing traffic flow, analyzing transportation networks, and addressing congestion issues. GNN can be used for traffic flow prediction, dynamic routing & navigation, Anomaly detection, public transport network

关键词： computer vision Graph neural networks Intrusion detection Natural language processing Neural networks Traffic control

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Identification of Infectious Disease Like COVID-19 Gene Biomarkers Using a Clear Artificial Intelligence Approach

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SN computer science 2025年第4期6卷 1-13页

作者： Dubey, Shivendra Verma, Dinesh Kumar Kumar, Mahesh Department of Artificial Intelligence and Machine Learning Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Jaypee University of Engineering and Technology MP Guna India

This study offers an approach that uses SARS-COV-2 mNGS (meta-genomic next-generation sequencing) samples to apply XAI (explainable artificial intelligence) methodologies derived from the use of machine learning methods. The research's data set contains 15,979 expressions of genes from 234 infected persons, of whom 39.68% (93) had SARS-COV-2 that was positive and 60.29% (141) had SARS-COV-2 that was negatives. The SARS-COV-2 related gene was chosen using the LASSO (least absolute shrinkage and selection operator) technique. The class imbalanced issue has been resolved using the SVM-SMOTE (Support Vector machine - Synthetic Minority Oversampling Technique) approach. To identify SARS-COV-2 related biomarkers possibilities and increase the comprehensibility of the final approach, an explainable strategy that utilized SHAP (SHAPley Additive exPlanations) as well as LIME (local interpretable model-agnostic explanations) approaches were used. The XGBoost (92.98% accuracy) approach performs better for the identification of infectious diseases like COVID-19 than the LR (91.17% accuracy), SVM (87.68% accuracy), and RF (91.18% accuracy) models. The three most significant genes connected to COVID-19 by the SHAP method were FAM83A, LGR6, and IFI27. The LIME findings demonstrated that the likelihood of a positive class was particularly influenced by the elevated degree of IFI27 expression of genes. Infectious diseases like COVID-19 could be accurately predicted using the suggested framework (XGBoost). The findings demonstrate that SHAP and LIME, along with the exploit of ML methods, it could be clarify the biological marker prognosis for infectious disease and give health center a clear knowledge about how potential risk factors affect the forecasts made by the model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomarker Gene LIME SARS SHAP

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Kontho: An AI-Driven Smart Glove for Enhanced Sign Language Communication 2nd

Kontho: An AI-Driven Smart Glove for Enhanced Sign Language...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Dasgupta, Dyuti Datta, Soumyajit Pramanik, Sagnik Dey, Rohit Kumar Nandigrami, Jeet Ghosh, Debaditya Department of Artificial Intelligence and Machine Learning Heritage Institute of Technology Kolkata India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9783031845420

In India, over 63 million individuals are affected by hearing loss, yet less than 350 certified sign language interpreters are available, thus leading to communication barriers and significant social and economic exclusion. This paper introduces Kontho, an ergonomic, AI-driven, multi-sensor glove integrated with a mobile app for real-time sign language communication via American Sign Language (ASL). Utilizing flex sensors, accelerometers, gyroscopes and EMG sensors, Kontho captures gestures and muscle activity which is processed through a combination of ensemble models and Large Language Models (LLMs) to enhance communication by predicting and completing incomplete gestures. Evaluated on a dataset of 23 ASL gestures at over 3867 data points with an accuracy of 99.61%, Kontho offers a scalable solution to foster social inclusion and address the economic isolation of hearing and speech-impaired individuals, enabling their active participation in the society. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signaling

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DP-RAE: A Dual-Phase Merging Reversible Adversarial Example for Image Privacy Protection 24

DP-RAE: A Dual-Phase Merging Reversible Adversarial Example ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Jiajie Du, Xia Zhou, Jizhe Pun, Chi-Man Xu, Qizhen Liu, Xiaoyuan School of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Computer Science Engineering Research Center of Machine Learning and Industry Intelligence Sichuan University Chengdu China Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798400706868

In digital security, Reversible Adversarial Examples (RAE) blend adversarial attacks with Reversible Data Hiding (RDH) within images to thwart unauthorized access. Traditional RAE methods, however, compromise attack efficiency for the sake of perturbation concealment, diminishing the protective capacity of valuable perturbations and limiting applications to white-box scenarios. This paper proposes a novel Dual-Phase merging Reversible Adversarial Example (DP-RAE) generation framework, combining a heuristic black-box attack and RDH with Grayscale Invariance (RDH-GI) technology. This dual strategy not only evaluates and harnesses the adversarial potential of past perturbations more effectively but also guarantees flawless embedding of perturbation information and complete recovery of the original image. Experimental validation reveals our method's superiority, secured an impressive 96.9% success rate and 100% recovery rate in compromising black-box models. In particular, it achieved a 90% misdirection rate against commercial models under a constrained number of queries. This marks the first successful attempt at targeted black-box reversible adversarial attacks for commercial recognition models. This achievement highlights our framework's capability to enhance security measures without sacrificing attack performance. Moreover, our attack framework is flexible, allowing the interchangeable use of different attack and RDH modules to meet advanced technological requirements. © 2024 ACM.

关键词： Differential privacy

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Automated Cleaning Solutions: Robotics Innovation in Homecare 4

Automated Cleaning Solutions: Robotics Innovation in Homecar...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Sujatha, E. Devi, S.Renuka Divya, M. Raju, D. Naveen Department of Computer Science and Engineering Chennai India Department of Artificial Intelligence and Machine Learning Chennai India Department of Computer Science and Engineering Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350364729

A room cleaning robot, often known as a vacuum robot, is a clever appliance designed to simplify household cleaning tasks. It is equipped with sensors and brushes that allow it to navigate rooms, detect obstacles, and collect dirt and debris from floors. These robots can be programmed to operate autonomously, following predefined cleaning patterns or avoiding specific areas. They are also commonly controlled remotely through smartphone apps or voice commands, making them user- friendly and convenient. Room cleaning robots are known for their efficiency in reaching tight spaces and cleaning under furniture, which can be challenging for traditional vacuum cleaners. They often use rechargeable batteries, returning to their charging docks when the battery is low, ensuring uninterrupted cleaning They have become popular for their ability to maintain clean floors in homes and businesses without continuous human supervision. Additionally, their compact and sleek designs make them fit seamlessly into modern living spaces. As technology advances, these robots continue to evolve, offering improved cleaning performance and smarter features. © 2024 IEEE.

关键词： Vacuum cleaners

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Advanced Energy Consumption Forecasting in Smart Grids Using Quantum Boltzmann machines 2

Advanced Energy Consumption Forecasting in Smart Grids Using...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Velayutham, P. Balaji, V.S. Krissh Shankaran, N. Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

Smart grid systems are seen as the next big thing to energy distribution and management with more efficiency, stability, and sustainability in power distribution and energy management systems. It is challenging to predict the amount of energy consumption in smart grid system because of its randomness resulting from various factors such as consumer's behavior, weather conditions. This is mainly because traditional forecasting techniques are usually inadequate in capturing these complexities and as a result resources are poorly allocated and organizational processes become inefficient. The work provides the energy consumption forecasting in smart grids through the application of Quantum Boltzmann machine(QBM) in the smart grid utilizing historical data. Some of the variables incorporated in the data are reaction time, power balance, and price elasticity coefficients customized through smart meters. Once raw data is preprocessed, QBMs are developed to establish an empirical probability model that will predict actual future energy utilization characteristics. The QBM is tested against the actual consumption data and focuses on the visualization of energy profile and identification of points like local extremes. © 2024 IEEE.

关键词： Smart power grids

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Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions

Advancing Social Intelligence in AI Agents: Technical Challe...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mathur, Leena Liang, Paul Pu Morency, Louis-Philippe Language Technologies Institute School of Computer Science Carnegie Mellon University United States Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9798891761643

Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal that involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (human or artificial).Progress towards Social-AI has accelerated in the past decade across several computing communities, including natural language processing, machine learning, robotics, human-machine interaction, computer vision, and *** language processing, in particular, has been prominent in Social-AI research, as language plays a key role in constructing the social *** this position paper, we identify a set of underlying technical challenges and open questions for researchers across computing communities to advance *** anchor our discussion in the context of social intelligence concepts and prior progress in Social-AI research. © 2024 Association for Computational Linguistics.

关键词： Adversarial machine learning

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Innovative Solutions for Cervical Cancer: Deep learning Models for Accurate Stage Prediction 2

Innovative Solutions for Cervical Cancer: Deep Learning Mode...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Yadav, Roshan Kumar Kumar, Divyam Kadam, Kalyani Devi, Seeta Gangarde, Rupali Department of Artificial Intelligence and Machine Learning Pune India Department of Obstetrics and Gynecological Nursing Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350383652

Cervical cancer, caused by the skin infection of the Human papillomavirus (HPV), ranked as the fourth most prevalent cancer globally, has profound and sometimes fatal implications for women. With approximately 500,000 new cases reported annually, the disease significantly impacts women's overall well-being. Ongoing efforts by researchers focus on developing a more cost-effective solution than regular HPV testing to manage and control this widespread health concern. Our research included an innovative framework for identifying and classifying stages of cervical cancer by processing the images of the tumours. To optimize the performance of our model, we developed a unique approach for selecting the best image data points by calculating a score, which is defined as the product of focus, sharpness, and resolution. Additionally, our study distinguishes itself by integrating three diverse feature extraction techniques-Local Binary Pattern (LBP), ResNet, and VGGNet-an inclusive strategy not commonly observed in similar projects. We even added deep convolutional architectures and machine learning techniques, including Random Forest, SVM, Decision Tree, XGBoost, a basic Neural Network Classifier, and a Multi-layered Perceptron (MLP). This hybrid approach allowed for an intrinsic comparison, identifying the most effective strategy among these. In contrast to current state-of-the-art methods, our approach yielded around 75% accuracy and 81% AUC score, which is greater than the existing studies on such dataset. © 2024 IEEE.

关键词： Contrastive learning

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