Journal of Ambient Intelligence and Humanized Computing最新文献_第7页

AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping 利用迁移学习和基于梯度的类激活映射进行人工智能龋齿检测

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-21 DOI: 10.1007/s12652-024-04795-x

Hardik Inani, Veerangi Mehta, Drashti Bhavsar, Rajeev Kumar Gupta, Arti Jain, Zahid Akhtar

{"title":"AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping","authors":"Hardik Inani, Veerangi Mehta, Drashti Bhavsar, Rajeev Kumar Gupta, Arti Jain, Zahid Akhtar","doi":"10.1007/s12652-024-04795-x","DOIUrl":"https://doi.org/10.1007/s12652-024-04795-x","url":null,"abstract":"Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140635975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Video emotional description with fact reinforcement and emotion awaking 带有事实强化和情感唤醒功能的视频情感描述

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-20 DOI: 10.1007/s12652-024-04779-x

Pengjie Tang, Hong Rao, Ai Zhang, Yunlan Tan

{"title":"Video emotional description with fact reinforcement and emotion awaking","authors":"Pengjie Tang, Hong Rao, Ai Zhang, Yunlan Tan","doi":"10.1007/s12652-024-04779-x","DOIUrl":"https://doi.org/10.1007/s12652-024-04779-x","url":null,"abstract":"Video description aims to translate the visual content in a video with appropriate natural language. Most of current works only focus on the description of factual content, paying insufficient attention to the emotions in the video. And the sentences always lack flexibility and vividness. In this work, a fact enhancement and emotion awakening based model is proposed to describe the video, making the sentence more attractive and colorful. The strategy of deep incremental leaning is employed to build a multi-layer sequential network firstly, and multi-stage training method is used to sufficiently optimize the model. Secondly, the modules of fact inspiration, fact reinforcement and emotion awakening are constructed layer by layer to discovery more facts and embed emotions naturally. The three modules are cumulatively trained to sufficiently mine the factual and emotional information. Two public datasets including EmVidCap-S and EmVidCap are employed to evaluate the proposed model. The experimental results show that the performance of the proposed model is superior to not only the baseline models, but also the other popular methods.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A path planning method based on deep reinforcement learning for crowd evacuation 基于深度强化学习的人群疏散路径规划方法

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-18 DOI: 10.1007/s12652-024-04787-x

Xiangdong Meng, Hong Liu, Wenhao Li

{"title":"A path planning method based on deep reinforcement learning for crowd evacuation","authors":"Xiangdong Meng, Hong Liu, Wenhao Li","doi":"10.1007/s12652-024-04787-x","DOIUrl":"https://doi.org/10.1007/s12652-024-04787-x","url":null,"abstract":"Deep reinforcement learning (DRL) is suitable for solving complex path-planning problems due to its excellent ability to make continuous decisions in a complex environment. However, the increase in the population size in the crowd evacuation path-planning problem causes a substantial computational burden for the algorithm, which leads to an unsatisfactory efficiency of the current DRL algorithm. This paper presents a path planning method based on DRL for crowd evacuation to solve the problem. First, we divide crowds into groups based on their relationship and distance from each other and select leaders from them. Next, we expand the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to propose an Optimized Multi-Agent Deep Deterministic Policy Gradient (OMADDPG) algorithm to obtain the global evacuation path. The OMADDPG algorithm uses the Cross-Entropy Method (CEM) to optimize policy and improve the neural network’s training efficiency by applying the Data Pruning (DP) algorithm. In addition, the social force model is improved, incorporating the relationship between individuals and psychological factors into the model. Finally, this paper combines the improved social force model and the OMADDPG algorithm. The OMADDPG algorithm transmits the path information to the leaders. Pedestrians in the environment are driven by the improved social force model to follow the leaders to complete the evacuation simulation. The method can use a leader to guide pedestrians safely arrive the exit and reduce evacuation time in different environments. The simulation results prove the efficiency of the path planning method.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secured COVID-19 CT image classification based on human-centric IoT and vision transformer 基于以人为本的物联网和视觉转换器的安全 COVID-19 CT 图像分类

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-18 DOI: 10.1007/s12652-024-04797-9

Dandan Xue, Jiechun Huang, Rui Zhou, Yonghang Tai, Jun Zhang

{"title":"Secured COVID-19 CT image classification based on human-centric IoT and vision transformer","authors":"Dandan Xue, Jiechun Huang, Rui Zhou, Yonghang Tai, Jun Zhang","doi":"10.1007/s12652-024-04797-9","DOIUrl":"https://doi.org/10.1007/s12652-024-04797-9","url":null,"abstract":"Security and privacy are fundamental to applications of medical internet of things (IoT). This article proposes a new computed tomography (CT) image three-classification prediction network, Re50-ViT (ResNet50 and Vision Transformer), which aims to improve the accuracy of traditional neural networks in screening patients with novel coronavirus infection pneumonia. To enhance network performance, the batch normalization layer is replaced with the group normalization layer for more stable activation normalization. The front-end utilizes ResNet50 for local feature extraction, and global information integration is achieved through the connection of a Class token and position embedding. Dropout layer is added to prevent overfitting and improve generalization. multiple transformer encoder layers are used to capture complex patterns and model label relationships within the CT images. The network integrates human-centric IoT and security measures to protect patient privacy and sensitive medical information. Experimental results compared to existing methods demonstrate the superiority of the Re50-ViT network. The Grad-CAM (gradient-weighted class activation mapping) technique provides intuitive visualization, highlighting the importance of specific regions in the CT images. The network shows effectiveness and reliability in detecting lung lesions, including COVID-19 and other pulmonary abnormalities. The integration of human-centric IoT and security considerations further enhances the clinical value of the network while ensuring the protection of patient data and privacy.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predict industry merger waves utilizing supply network information 利用供应网络信息预测行业兼并浪潮

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-17 DOI: 10.1007/s12652-024-04792-0

Yating Qu, Liqiang Wang, Qianru Qi, Li Pan, Shijun Liu

引用次数: 0

An English video teaching classroom attention evaluation model incorporating multimodal information 结合多模态信息的英语视频教学课堂注意力评价模型

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-17 DOI: 10.1007/s12652-024-04800-3

Qin Miao, Lemin Li, Dongming Wu

引用次数: 0

Intelligent fractional-order sliding mode control based maneuvering of an autonomous vehicle 基于智能分数阶滑动模式控制的自动驾驶汽车操纵系统

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-17 DOI: 10.1007/s12652-024-04770-6

Raghavendra M. Shet, Girish V. Lakhekar, Nalini C. Iyer

{"title":"Intelligent fractional-order sliding mode control based maneuvering of an autonomous vehicle","authors":"Raghavendra M. Shet, Girish V. Lakhekar, Nalini C. Iyer","doi":"10.1007/s12652-024-04770-6","DOIUrl":"https://doi.org/10.1007/s12652-024-04770-6","url":null,"abstract":"This article proposes a new intelligent trajectory tracking control law for the precise maneuvering of an autonomous vehicle in the presence of parametric uncertainties and external disturbances. The controller design includes a fuzzy sliding mode algorithm for smooth motion control subjected to steering saturation and curvature constraints. Along with the Salp Swarm Optimization technique, explored for optimal selection of surface coefficient in fractional order Proportional-Derivative type (P{D}^{alpha }) sliding manifold. The sliding variable on the surface approaches zero in a finite time. Further, the trajectory tracking control rule offers the stability of closed-loop tracking on the predetermined path and ensures finite time convergence to the sliding surface. In addition, to estimate the hitting gain in online mode, a supervisory fuzzy logic controller system is used. Therefore, it is not necessary to determine upper bounds on uncertainty in the dynamic parameters of autonomous vehicles. Lyapunov theory verifies the global asymptotic stability of the entire closed-loop control strategy. The major control issue is the input constraints arising primarily due to the capability of the steering actuating module, which causes significant deviation or vehicle instability. Consequently, it is desirable to design a robust adaptive stable controller, such as Adaptive Backstepping Control (ABC), even though it requires vehicle model information. Therefore, the proposed model-free intelligent sliding mode technique offers better tracking performance and vehicle stability in adverse conditions. Finally, the efficacy of the proposed control technique was confirmed through a comparative analysis based on numerical simulation using MATLAB/SIMULINK and experimental validation using Quanser’s self-driving car module. A quantitative study was conducted to elucidate the superior tracking performance of intelligent control over the traditional SMC and adaptive backstepping control methods.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140610065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wavelet scattering transform and deep features for automated classification and grading of dates fruit 用于枣果自动分类和分级的小波散射变换和深度特征

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-16 DOI: 10.1007/s12652-024-04786-y

Newlin Shebiah Russel, Arivazhagan Selvaraj

{"title":"Wavelet scattering transform and deep features for automated classification and grading of dates fruit","authors":"Newlin Shebiah Russel, Arivazhagan Selvaraj","doi":"10.1007/s12652-024-04786-y","DOIUrl":"https://doi.org/10.1007/s12652-024-04786-y","url":null,"abstract":"Date fruit, a vital agricultural product in the Middle East area, is harvested annually in millions of metric tons and is renowned for its abundant nutrients. With computer vision and machine learning techniques, automatic date fruit classification enables farmers and supermarkets to differentiate between various varieties and qualities of date fruits within their inventory. Date fruits have unique physical characteristics, such as shape, size, color, texture, and skin type that are important in determining their variety and quality. These characteristics can vary significantly depending on the cultivar, growing conditions, and ripening stage of the date fruits. This paper presents a novel date fruit type classification and grading system achieved through the feature-level fusion of deep learning features and wavelet scattering features. Wavelet scattering features are extracted at varying levels of decomposition; enabling reliable extraction of information from diverse channels. To extract deep features this study utilizes pre-trained architectures, including Alexnet, Googlenet, Resnet, and MobileNetV2. The proposed methodology has been experimentally evaluated with the Date Fruit in Controlled Environment dataset, which has nine classes, and has yielded an accuracy of 95.9% for date species classification. Various date fruit species from the TU-DG dataset were graded, and for Ajwa species, the accuracy is 97.8%, for Mabroom, 92.6% accuracy, and for Sukkary, 99.5% accuracy.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

User gait biometrics in smart ambient applications through wearable accelerometer signals: an analysis of the influence of training setup on recognition accuracy 通过可穿戴加速度计信号在智能环境应用中进行用户步态生物识别：分析训练设置对识别准确率的影响

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-15 DOI: 10.1007/s12652-024-04790-2

Maria De Marsico, Andrea Palermo

{"title":"User gait biometrics in smart ambient applications through wearable accelerometer signals: an analysis of the influence of training setup on recognition accuracy","authors":"Maria De Marsico, Andrea Palermo","doi":"10.1007/s12652-024-04790-2","DOIUrl":"https://doi.org/10.1007/s12652-024-04790-2","url":null,"abstract":"Gait recognition can exploit the signals from wearables, e.g., the accelerometers embedded in smart devices. At present, this kind of recognition mostly underlies subject verification: the incoming probe is compared only with the templates in the system gallery that belong to the claimed identity. For instance, several proposals tackle the continuous recognition of the device owner to detect possible theft or loss. In this case, assuming a short time between the gallery template acquisition and the probe is reasonable. This work rather investigates the viability of a wider range of applications including identification (comparison with a whole system gallery) in the medium-long term. The first contribution is a procedure for extraction and two-phase selection of the most relevant aggregate features from a gait signal. A model is trained for each identity using Logistic Regression. The second contribution is the experiments investigating the effect of the variability of the gait pattern in time. In particular, the recognition performance is influenced by the benchmark partition into training and testing sets when more acquisition sessions are available, like in the exploited ZJU-gaitacc dataset. When close-in-time acquisition data is only available, the results seem to suggest re-identification (short time among captures) as the most promising application for this kind of recognition. The exclusive use of different dataset sessions for training and testing can rather better highlight the dramatic effect of trait variability on the measured performance. This suggests acquiring enrollment data in more sessions when the intended use is in medium-long term applications of smart ambient intelligence.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed versus centralized computing of coverage in mobile crowdsensing 移动人群感应中覆盖范围的分布式计算与集中式计算

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-14 DOI: 10.1007/s12652-024-04788-w

Michele Girolami, Alexander Kocian, Stefano Chessa

引用次数: 0