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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wang, Xiaofan Liu, Zheng Sun, Dongming Zhu, Junjie Yao, Jie Xi'an university of technology College of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350356328

The diffusion of toxic gas caused by the leakage of stored high-pressure chemical gases will cause significant harm to the environment and residents. Therefore, it is crucial to trace the source of leakage and predict the dispersion range of leakage accidents quickly. When using diffusion models to predict poisonous gas leaks, errors are accumulated due to the influence of meteorological, terrain and other factors, which affects the prediction accuracy, Therefore, sensors need to be deployed to use the data captured as observation value to correct models. However, the high cost of arranging and maintaining toxic gas sensors makes it difficult to place a large number of them, so how to place a limited number of sensors in the optimal location is a hot research issue. In this paper, we propose a sensor optimization layout strategy based on the gradient of mutual information, which utilizes the mutual information between the concentration of poisonous gas and the release source at the detection location as an indicator, and avoids traversing the search to improve the search efficiency through the gradient-based sequence forward selection search strategy. The time complexity is reduced from O(n2) to O(n),and at the same time, comparative experiments are conducted with other optimization algorithms, which show that the gradient-based forward selection search strategy is similar to other optimization algorithms in terms of time spent, and is more accurate in terms of convergence than other optimization algorithms © 2024 IEEE.

关键词： Information leakage

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Multi-Modal AI/ML Integration for Precision Glaucoma Detection: A Comprehensive Analysis using Optical Coherence Tomography, Fundus Imaging, RNFL, and Vessel Density 2

Multi-Modal AI/ML Integration for Precision Glaucoma Detecti...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Raghunathan, T. Mishra, Aditya Mahur, Akhilesh Kumar Balaji, B. Sri Krishna College of Technology Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350349221

Glaucoma, a prevailing cause of irreversible blindness, necessitates early and accurate detection for optimal management. This paper introduces a novel approach integrating AI and ML to elevate glaucoma detection precision. Leveraging SD-OCT, fundus photography, RNFL, and vessel density, the proposed system embodies a holistic analysis. Preliminary results underscore the efficacy of this multimodal system, positioning it as a frontrunner in revolutionizing ocular health *** conventional methods for glaucoma diagnosis are often limited by their reliance on single-modal imaging, prompting a paradigm shift towards comprehensive multi-modal integration. By fusing the capabilities of SDOCT, fundus photography, RNFL, and vessel density, our approach addresses the shortcomings of individual modalities, providing a more nuanced understanding of ocular health. The innovative combination of these imaging techniques, coupled with advanced AI and ML algorithms, establishes a robust foundation for glaucoma detection, heralding a new era in ocular health diagnostics. The suggested multi-modal system not only demonstrates superior accuracy but also showcases the potential for early detection and intervention. The intricate interplay of algorithms, illustrated in detailed block diagrams, underscores the synergy achieved by integrating diverse modalities. This paper presents a pioneering step towards a more comprehensive and accurate approach to glaucoma detection, opening avenues for further advancements at the intersection of AI, ML, and ophthalmic imaging. © 2024 IEEE.

关键词： Machine learning

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Prediction of Insurance Claims for Health Sector using Machine Learning Techniques 3

Prediction of Insurance Claims for Health Sector using Machi...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pragatheeswari, E. Kavitha, M.N. Saranya, S.S. Dhanushree, D. Jasmitha, S. Abiradhie, P.V. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science and Design Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's lives. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their medical expenses. The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Each record in the dataset represents an individual’s health insurance charges along with corresponding demographic and health-related characteristics. In this paper, Machine Learning techniques are used to group the claim amount that has been adopted by the individuals based on the hospitalized expenditure and insurance charges paid by the individuals. In the health sector, machine learning techniques are used to forecast insurance claims by building predictive models from historical data on claims, patient demographics, treatment types, and results. By predicting future claims based on trends seen in the data, these models help insurance firms better manage risk and allocate resources. In case of a fresher to this concept, it feels challenging to understand whereas generating as a pattern can be more flexible to acquire knowledge about how much amount has been claimed. © 2025 IEEE.

关键词： Electronic health record

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HARANet: a skin lesion image segmentation network based on hybrid reverse attention 3

HARANet: a skin lesion image segmentation network based on h...

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3rd International Conference on Electronic Information engineering, Big Data, and computer technology, EIBDCT 2024

作者： Zeng, Ziqin Long, Jianwu College of Computer Science and Engineering Chongqing University of Technology 400054 China

ISBN: (纸本)9781510680449

Segmentation plays a crucial role in medical image analysis, enabling the identification and localization of diseases, monitoring morphological changes, and extracting discriminative features for further diagnosis. Skin cancer, a widespread global malignancy, emphasizes the importance of early diagnosis in completely eliminating malignant tumors from the body. We propose a skin lesion segmentation network based on a hybrid reverse attention mechanism to address the issues of significant individual differences, blurred edges, and inconsistent scales between diseased and normal skin in dermoscopic images. The network combines spatial attention, which combines multidimensional feature maps and adaptively adjusts the target region of the network, with channel attention that follows the self-attention mechanism to obtain clear segmentation boundaries. By utilizing cascaded reverse attention modules and employing an erasing strategy, we guide the model to actively focus on important regions. When compared with seven existing segmentation networks on three open-source datasets, our approach achieves the best performance in three out of five evaluation metrics. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image segmentation

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A Machine Learning and Deep Learning based Approach to Generate a Speech Emotion Recognition System 18

A Machine Learning and Deep Learning based Approach to Gener...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sumera Vaidehi, K. Nisha, Qamar Stanley College of Engineering and Technology for Women Department of Artificial Intelligence & Data Science and Computer Engineering Hyderabad India Stanley College of Engineering and Technology for Women Department. of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The most common and general medium via which we humans convey or communicate our thoughts, emotions, feelings or ideas artlessly is by speech or articulation. Blending of this artless way of speech with the technological advancements of AI, has given rise to the importance of building emotion recognition systems from speech today. Even more, the speech/articulation emotion recognition system presented here is also to contribute in and facilitate various emerging applications of today like, in detecting persons' physiological state (as in lie detectors), also be used in forensics, medicine. The proposed work identifies/associates an appropriate label/emotion for the respective emotion from speech presented in the form of an audio file (.wav format). About 4240 audio samples are taken. 1440, 2800 samples from RAVDESS and TESS datasets are considered respectively. After this process of Data collection, features are separately extracted for each audio dataset mentioned above. Energy, pitch, ZCR, co-efficient of Mel frequency ceptrum (MFCC) are some of the features considered in this study. Furthermore, clubbing and merging of 2 datasets is performed resulting in a total of 4240 rows and 24 columns (features/characteristics including 1class label) of audio samples. The resulting 4240 samples of feature dataset is split/bifurcated into training and testing set by considering 3 different possibilities/instances viz;60%-40% ratio, 70%-30% ratio, 80%-30% ratio. The models namely CNN, Random forest and Support Vector Machine are trained to classify the dataset into 8 different emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). An attempt to implement the models using two very essential disciplines of AI i.e. Machine Learning and Deep Learning is made here. The accuracy or results are depicted by generating confusion matrices on test data for CNN, RF and SVM models (Each model is trained and test across 3 different ratios viz;60%-40%, 70%-30%, 80%-20%). C

关键词： Classification CNN Confusion matrices Extraction of features MFCC RF Speech/articulation Emotion Recognition SVM

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Blockchain - Enhanced Secure and Efficient IPFS - based EHR Management: A Decentralized Approach 4

Blockchain - Enhanced Secure and Efficient IPFS - based EHR ...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Santhi, V. Santhosh, Diyaa Samyuktha, N. Sachitha, C. PSG College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353266

This paper introduces a novel healthcare system based on blockchain and IPFS technologies, designed to securely store and facilitate the sharing of patient health records. The system is structured into three layers: blockchain,IPFS, and the application. Within the blockchain layer, the primary function is to store and safeguard patient health records. This is achieved through the utilization of distributed ledger technology, ensuring the records' immutability and resistance to tampering. Meanwhile, the IPFS layer offers the infrastructure necessary for secure storage and retrieval of patient health records. Lastly, the application layer provides the user interface for accessing and managing patient health records, including functions like addition, updates, and deletions. This innovative system boasts numerous advantages compared to traditional healthcare systems. It offers enhanced security, as patient health records are stored in an encrypted format across a distributed network. Furthermore, it provides a more efficient means of securely sharing patient health records among healthcare providers. The system also improves transparency by recording all network accesses as transactions. While this system is still in its early developmental stages, its potential to transform the storage and sharing of healthcare records is promising. © 2024 IEEE.

关键词： Electronic health record

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AI Based Identification of Plant Health Issues 9

AI Based Identification of Plant Health Issues

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Saranya, T. Subbiah, S. Dhanaseelan, V. Velammal College of Engineering and Technology Department of Computer Science Madurai India

ISBN: (纸本)9798350377972

The plant diseases detection and classification are Critical in ensuring sustainable agricultural productivity and preventing substantial crop losses. Traditional methods are time consuming and error prone and requires expertise in the field and it is difficult for larger operations. The detection of diseases using models like ResNet, MobileNet and YOLO are the recent advancements in the Artificial Intelligence and Deep Learning, but it also faces Challenges such as dataset imbalances, computational demand and variability in conditions persists. This research is on a deep learning model called Efficientnet to automate plant health diagnostics. The study evaluates the model's performance detecting various plant diseases and comparing it to other deep learning techniques, the results of the study show how Efficientnet can outperform other traditional models by providing accurate, real time plant disease identification. © 2024 IEEE.

关键词： agriculture Convolution Neural Network deep learning Efficientnet image classification plant disease detection

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Classifier Retraining with Gaussian-distributed Prototypes for Class-Imbalanced Federated Medical Image Classification

Classifier Retraining with Gaussian-distributed Prototypes f...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pan, Haiwei Chen, Chunling Zhang, Kejia Li, Zhe Yu, Fengming Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350386226

Federated learning enables collaborative learning across distributed medical institutions without centralizing data. However, existing studies often overlook class imbalance in medical images, which can degrade model performance, especially for minority classes. In this paper, we propose FedCRGP, a method that uses Gaussian-distributed prototypes to address class imbalance in federated medical image classification. These prototypes incorporate class variance to enhance representation learning. Class-aware Gaussian Prototype Learning (CGPL) is introduced to improve intra-class similarity and reduce interclass similarity by aligning class features around the Gaussian-distributed prototypes. Local and global features sampled from these prototypes are fused to retrain the classifier, mitigating bias caused by class imbalance. Experiments on two medical image classification datasets demonstrate its superior performance, particularly in multi-class scenarios. © 2024 IEEE.

关键词： Federated learning

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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RFA-YOLO-POSE: A Fusion Algorithm for Pose Detection and Object Identification Amidst Complex Crowds 5

RFA-YOLO-POSE: A Fusion Algorithm for Pose Detection and Obj...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Zhang, Yuanjian Xue, Wenqi College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9798350385557

This research introduces an algorithm that merges pose detection with object detection, leveraging YOLO-Pose as a foundation. By changing the backbone network, our model is enhanced to capture more detailed features. We integrated the CBAM attention mechanism for a more refined perception of input feature maps in both channels and spatial dimensions, improving the model's performance in complex crowd environments. The approach of using multi-scale feature fusion allows the model to adapt its judgments based on different scale feature maps. In our training process, we adopted the SIoU loss function in place of the standard IoU loss function, facilitating more precise bounding box adjustments. Our experiments, conducted on the Pedestrian Attribute Recognition dataset, excluded images with fewer pedestrians and incorporated additional images of dense crowds. The experimental outcomes show that our method reached a 90.14% accuracy and an 87.55% recall rate in the dataset, outperforming the traditional YOLO-POSE by 21.56% in detection accuracy, affirming its effectiveness in dense crowd scenarios. The versatility of the model in handling various detection tasks makes it invaluable for applications in safety surveillance, public security, crowd analysis, and analysis of social events © 2024 IEEE.

关键词： Object detection

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