Journal of Ambient Intelligence and Humanized Computing最新文献_第4页

Lightweight and privacy-preserving device-to-device authentication to enable secure transitive communication in IoT-based smart healthcare systems 在基于物联网的智能医疗系统中，通过轻量级和保护隐私的设备对设备身份验证，实现安全的跨设备通信

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-21 DOI: 10.1007/s12652-024-04810-1

Sangjukta Das, Maheshwari Prasad Singh, Suyel Namasudra

{"title":"Lightweight and privacy-preserving device-to-device authentication to enable secure transitive communication in IoT-based smart healthcare systems","authors":"Sangjukta Das, Maheshwari Prasad Singh, Suyel Namasudra","doi":"10.1007/s12652-024-04810-1","DOIUrl":"https://doi.org/10.1007/s12652-024-04810-1","url":null,"abstract":"Internet of Things (IoT) devices are often directly authenticated by the gateways within the network. In complex and large systems, IoT devices may be connected to the gateway through another device in the network. In such a scenario, new device should be authenticated with the gateway through the intermediate device. To address this issue, an authentication process is proposed in this paper for IoT-enabled healthcare systems. This approach performs a privacy-preserving mutual authentication between the gateway and an IoT device through intermediate devices, which are already authenticated by the gateway. The proposed approach relies on the session key established during gateway-intermediate device authentication. To emphasizes lightweight and efficient system, the proposed approach employs lightweight cryptographic operations, such as XOR, concatenation, and hash functions within IoT networks. This approach goes beyond the traditional device-to-device authentication, allowing authentication to propagate across multiple devices or nodes in the network. The proposed work establishes a secure session between an authorized device and a gateway, preventing unauthorized devices from accessing healthcare systems. The security of the protocol is validated through a thorough analysis using the AVISPA tool, and its performance is evaluated against existing schemes, demonstrating significantly lower communication and computation costs.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510483","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

Agent-based modelling of individual absorptive capacity for effective knowledge transfer 基于代理的个人吸收能力模型，促进有效的知识转移

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-21 DOI: 10.1007/s12652-024-04826-7

Thomas Dolmark, Osama Sohaib, Ghassan Beydoun, Firouzeh Taghikhah

{"title":"Agent-based modelling of individual absorptive capacity for effective knowledge transfer","authors":"Thomas Dolmark, Osama Sohaib, Ghassan Beydoun, Firouzeh Taghikhah","doi":"10.1007/s12652-024-04826-7","DOIUrl":"https://doi.org/10.1007/s12652-024-04826-7","url":null,"abstract":"The importance of knowledge for organizational success is widely recognized, leading managers to leverage knowledge actively. Within knowledge transfer, the Absorptive Capacity (ACAP) of Knowledge Recipients (KR) emerges as an unresolved barrier. ACAP is the dynamic capability to absorb knowledge and surpass the aggregation of individual ACAP within an organization. However, more research is needed on individual-level ACAP and its implications for bridging the gap between individual and organizational knowledge transfer. To address this gap, this study employs Agent-Based Modeling (ABM) as a simulation method to replicate individual ACAP within an organization, facilitating the examination of knowledge transfer dynamics. ABM allows for the detailed analysis of interactions between individual KRs and the organizational environment, revealing how uninterrupted time and other factors influence knowledge absorption. The implications of the study are that ABM provides specific insights into how individual ACAP affects organizational learning and performance, emphasizing the importance of uninterrupted time for KR to achieve optimal knowledge exploitation and highlighting the need for organizational practices and policies that foster environments conducive to knowledge absorption.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510484","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

Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F) 利用新算法分割过滤（S2F）寻找转录因子结合位置

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-15 DOI: 10.1007/s12652-024-04812-z

P. Theepalakshmi, U. Srinivasulu Reddy

引用次数: 0

LW-MHFI-Net: a lightweight multi-scale network for medical image segmentation based on hierarchical feature incorporation LW-MHFI-Net：基于分层特征整合的轻量级多尺度医学图像分割网络

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-15 DOI: 10.1007/s12652-024-04820-z

Yasmeen A. Kassem, S. Kishk, Mohamed A. Yakout, Doaa A. Altantawy

引用次数: 0

Rf-based fingerprinting for indoor localization: deep transfer learning approach 基于射频的室内定位指纹识别：深度迁移学习方法

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-14 DOI: 10.1007/s12652-024-04819-6

Rokaya Safwat, Eman Shaaban, S. Al-Tabbakh, Karim Emara

引用次数: 0

$$lambda $$-possibility-center based MCDM technique on the control of Ganga river pollution under non-linear pentagonal fuzzy environment 基于可能性中心的非线性五边形模糊环境下恒河污染控制的 MCDM 技术

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-12 DOI: 10.1007/s12652-024-04817-8

Totan Garai

引用次数: 0

Attention based: modeling human perception of reflectional symmetry in the wild 基于注意力：模拟人类对野外反射对称性的感知

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-07 DOI: 10.1007/s12652-024-04821-y

Habibie Akbar, Muhammad Munwar Iqbal

引用次数: 0

Multiview data fusion technique for missing value imputation in multisensory air pollution dataset 用于多感官空气污染数据集缺失值估算的多视图数据融合技术

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-04 DOI: 10.1007/s12652-024-04816-9

Asif Iqbal Middya, Sarbani Roy

{"title":"Multiview data fusion technique for missing value imputation in multisensory air pollution dataset","authors":"Asif Iqbal Middya, Sarbani Roy","doi":"10.1007/s12652-024-04816-9","DOIUrl":"https://doi.org/10.1007/s12652-024-04816-9","url":null,"abstract":"The missing readings in various sensors of air pollution monitoring stations is a common issue. Those missing sensor readings may greatly influence the performance of monitoring and analysis of air pollution data. To address this problem, in this paper, a multi-view based missing value (MV) imputation method called MVDI (Multi-View Data Imputation) is proposed for air pollution related time series data. MVDI combines four models namely LSTM (Long-Short Term Memory), IDS (Inverse Distance Squared), SVR (Support Vector Regressor), and KNN (K-Nearest Neighbors) to estimate MVs. These four models are mainly employed to capture the variations in data from different views of the dataset. Here, different views represent different portions (subsets) of the actual dataset. The estimates of MVs from all the views are combined using a kernel function to get an overall result. The proposed model MVDI is evaluated on real-world air pollution dataset in terms of RMSE, MAE, MAPE, and R2. The experimental results show that MVDI dominates over the baseline methods namely AR (AutoRegressive), ARIMA (AutoRegressive Integrated Moving Average), RFR (Random Forest Regressor), ANN (Artificial Neural Network), LI (Linear Interpolation), NN (Nearest Neighbors), MI (Mean Imputation), CNN (Convolutional Neural Network), ConvLSTM (Convolutional LSTM).","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141259626","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

Road intersection detection using the YOLO model based on traffic signs and road signs 利用基于交通标志和道路标志的 YOLO 模型检测道路交叉口

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-01 DOI: 10.1007/s12652-024-04815-w

William Eric Manongga, Rung-Ching Chen

{"title":"Road intersection detection using the YOLO model based on traffic signs and road signs","authors":"William Eric Manongga, Rung-Ching Chen","doi":"10.1007/s12652-024-04815-w","DOIUrl":"https://doi.org/10.1007/s12652-024-04815-w","url":null,"abstract":"A road intersection is an area where more than two roads in different directions connect. It is a point of transition where the driver navigates and makes the decision, making it an area with a high risk for traffic accidents. Road intersection detection is identifying and analyzing road intersections in real time using various technologies and algorithms. It is an essential part of intelligent transportation systems and autonomous driving. Road intersection detection helps the driver to identify the road intersection early to make good driving decisions and avoid accidents. Despite its high importance, only a few research is found regarding this topic. Existing research mainly focuses on detecting and classifying traffic signs, vehicles, and pedestrians. In this research, we propose an algorithm to detect road intersections using an image from the front-facing camera installed on the car as an input. We use traffic sign detection to detect seven types of traffic signs having a high probability of intersection nearby and combine it with our novel road intersection detection algorithm to detect the location of the road intersection. Our road inter-section detection algorithm leverages the relationship between the area of the traffic signs and the location of the intersection. Our proposed method gives promising results from the experiments and can detect road intersections from further distances. Our method is also able to perform detection in real time.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"2018 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141187889","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

Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation 通过有限混合物模型聚合使用加性时间序列建模进行非侵入式负荷监测

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-01 DOI: 10.1007/s12652-024-04814-x

Soudabeh Tabarsaii, Manar Amayri, Nizar Bouguila, Ursula Eicker

{"title":"Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation","authors":"Soudabeh Tabarsaii, Manar Amayri, Nizar Bouguila, Ursula Eicker","doi":"10.1007/s12652-024-04814-x","DOIUrl":"https://doi.org/10.1007/s12652-024-04814-x","url":null,"abstract":"Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), involves different methods aiming to distinguish the individual contribution of appliances, given the aggregated power signal. In this paper, the application of finite Generalized Gaussian and finite Gamma mixtures in energy disaggregation is proposed and investigated. The procedure includes approximation of the distribution of the sum of two Generalized Gaussian random variables (RVs) and the approximation of the distribution of the sum of two Gamma RVs using Method-of-Moments matching. By adopting this procedure, the probability distribution of each combination of appliances consumption is acquired to predict and disaggregate the specific device data from the aggregated data. Moreover, to make the models more practical we propose a deep version, that we call DNN-Mixture, as a cascade model, which is a combination of a deep neural network and each of the proposed mixture models. As part of our extensive evaluation process, we apply the proposed models on three different datasets, from different geographical locations, that had different sampling rates. The results indicate the superiority of proposed models as compared to the Gaussian mixture model and other widely used approaches. In order to investigate the applicability of our models in challenging unsupervised settings, we tested them on unseen houses with unlabeled data. The outcomes proved the extensibility and robustness of the proposed approach. Finally, the evaluation of the cascade model against the state of the art shows that by benefiting from the advantages of both neural networks and finite mixtures, cascade model can produce promising and competing results with RNN without suffering from its inherent disadvantages.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141198124","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