Journal of Ambient Intelligence and Humanized Computing最新文献

Predicting the unconfined compressive strength of stabilized soil using random forest coupled with meta-heuristic algorithms 利用随机森林和元启发式算法预测稳定土的无压抗压强度

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-09-16 DOI: 10.1007/s12652-024-04857-0

Yan Li

引用次数: 0

Expressive sign language system for deaf kids with MPEG-4 approach of virtual human character 采用 MPEG-4 虚拟人物方法的聋哑儿童手语表达系统

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-09-14 DOI: 10.1007/s12652-024-04842-7

Itimad Raheem Ali, Hoshang Kolivand

{"title":"Expressive sign language system for deaf kids with MPEG-4 approach of virtual human character","authors":"Itimad Raheem Ali, Hoshang Kolivand","doi":"10.1007/s12652-024-04842-7","DOIUrl":"https://doi.org/10.1007/s12652-024-04842-7","url":null,"abstract":"Children with language impairments during their significant developmental periods within ‎childhood are exposed to cognitive risk, social impairments, along with language. This is difficult ‎with children born deaf from hearing parents who own little or no experience of communicating ‎in sign language. This system presents the sign language in the context of British ‎Sign Language (BSL) for producing utterances through virtual characters. In capturing, Kinect ‎sensors use a motion capture sensor for motion actors. The connection uses sensors to read data, ‎connect to high-quality 3D scans, and then use these high-quality scans of the animated MPEG-4 ‎face and hand models. The main challenges of this system are the simultaneous capture of data ‎for the whole hand and the development of the MPEG-4 approach considering the animation ‎engines with descriptive sign language features. After synchronizing motion data from motion ‎capture results with Kinect, the combined hand character adjusts points, frames, and time with ‎virtual characters based on the motion of character actors. This study demonstrates the skills of ‎this sign language system instrumental in presenting an assessment by users, highlighting the ‎importance of the hand part in creating new accents and signs in BSL. We have validated this ‎system by testing the reliability and functionality of the virtual characters.‎.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257062","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 multi-objective gene selection for cancer diagnosis using particle swarm optimization and mutual information 利用粒子群优化和互信息进行癌症诊断的多目标基因选择

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-09-12 DOI: 10.1007/s12652-024-04853-4

Azar Rafie, Parham Moradi

引用次数: 0

Partial policy hidden medical data access control method based on CP-ABE 基于 CP-ABE 的部分策略隐藏式医疗数据访问控制方法

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-09-11 DOI: 10.1007/s12652-024-04843-6

Jing Huang, Detao Tang, Chenyu Jiang, Fulong Chen, Ji Zhang, Dong Xie, Taochun Wang, Chuanxin Zhao, Chao Wang, Jintao Li

引用次数: 0

Maximum dry density estimation of stabilized soil via machine learning techniques in individual and hybrid approaches 通过单独和混合方法中的机器学习技术估算稳定土的最大干密度

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-09-11 DOI: 10.1007/s12652-024-04860-5

Lianping Zhao, Guan Dashu Guan

{"title":"Maximum dry density estimation of stabilized soil via machine learning techniques in individual and hybrid approaches","authors":"Lianping Zhao, Guan Dashu Guan","doi":"10.1007/s12652-024-04860-5","DOIUrl":"https://doi.org/10.1007/s12652-024-04860-5","url":null,"abstract":"In geotechnical engineering, the maximum dry density (MDD) stands as an important parameter, denoting the utmost mass of soil achievable per unit volume when compacted to its maximum dry state. Its significance extends to the design of various earthworks like embankments, foundations, and pavements, influencing the soil’s strength, stiffness, and stability. The MDD is contingent on diverse elements like soil type, grain size distribution, moisture content and compaction effort. Generally, heightened compaction effort correlates with an increased MDD, while elevated moisture content corresponds to a reduced MDD. Accurate prediction of the MDD under specific conditions is imperative to uphold the quality and safety standards of earthworks. This research aims to introduce Support Vector Regression (SVR) as a modeling technique for predicting the MDD of soil-stabilizer mixtures. To establish an accurate and comprehensive model that can correlate the stabilized soil’s MDD with attributes of natural soil, consisting linear shrinkage, particle size distribution, plasticity, as well as the type and number of stabilizing additives, three optimization algorithms, namely Artificial Rabbits Optimization (ARO), Manta Ray Foraging Optimization (MRFO), and Improved Manta-Ray Foraging Optimizer (IMRFO), were employed in addition to SVR. Considering the results of evaluative metrics, the SVAR model (combination of SVR and ARO) experienced the highest predictive performance, registering an impressive value of R2 in the training phase with 0.9948, as well as the lowest RMSE value of 19.1376.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227800","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

Deep learning and encryption algorithms based model for enhancing biometric security for artificial intelligence era 基于深度学习和加密算法的人工智能时代生物识别安全增强模型

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-08-29 DOI: 10.1007/s12652-024-04855-2

Haewon Byeon, Mohammad Shabaz, Herison Surbakti, Ismail Keshta, Mukesh Soni, Vaibhav Bhatnagar

{"title":"Deep learning and encryption algorithms based model for enhancing biometric security for artificial intelligence era","authors":"Haewon Byeon, Mohammad Shabaz, Herison Surbakti, Ismail Keshta, Mukesh Soni, Vaibhav Bhatnagar","doi":"10.1007/s12652-024-04855-2","DOIUrl":"https://doi.org/10.1007/s12652-024-04855-2","url":null,"abstract":"The significance of facial recognition in the era of artificial intelligence lies in its utilization of facial features as a type of biometric characteristic possessing uniqueness and irreversibility. However, exposing these features to attacks, tampering, or unauthorized disclosure poses considerable threats to user privacy and security. A privacy and security solution based on deep learning and encryption algorithms is proposed to tackle this issue. This solution employs the FaceNet deep learning algorithm to extract facial features efficiently. The combination of biometric feature blurriness and cryptographic system precision is achieved, utilizing the CKKS fully homomorphic encryption algorithm for operations in the ciphertext domain of facial recognition. The SM4 algorithm is used to enhance the resilience of facial feature ciphertext against malicious attacks. By leveraging the properties of symmetric ciphers, a balance is achieved between security and computational efficiency. The management of the symmetric key used in the SM4 algorithm is conducted through the employment of the SM9 asymmetric encryption algorithm. Experimental results and analysis demonstrate that the proposed solution enhances the security of data transmission, storage, and comparison without compromising the accuracy and efficiency of facial recognition.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202566","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

GA-MPG: efficient genetic algorithm for improvised mobile plan generation GA-MPG：用于生成简易移动计划的高效遗传算法

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-08-27 DOI: 10.1007/s12652-024-04846-3

Rohan S. Shukla, Ekta A. Ghuse, Tausif Diwan, Jitendra V. Tembhurne, Parul Sahare

{"title":"GA-MPG: efficient genetic algorithm for improvised mobile plan generation","authors":"Rohan S. Shukla, Ekta A. Ghuse, Tausif Diwan, Jitendra V. Tembhurne, Parul Sahare","doi":"10.1007/s12652-024-04846-3","DOIUrl":"https://doi.org/10.1007/s12652-024-04846-3","url":null,"abstract":"In the competitive landscape of the telecom sector, a Communication Service Provider's success hinges on its ability to offer compelling mobile plans tailored to diverse customer needs. This not only boosts company profits but also enhances metrics like average revenue per user (ARPU), customer lifecycle value, and reduces customer churn. Striking a balance between these objectives presents a formidable task. To address this challenge, we propose a novel approach called Genetic Algorithm Mobile Plan Generation (GA-MPG). The proposed method stands out for its deterministic approach that equally focuses on minimizing customer churn. This is done by providing them with the best-suited plans without making them pay extra for features they would use. The efficient mobile plan generation using GA-MPG is accomplished by the combination of the AdaBoost classifier and the Fuzzy model. The AdaBoost is utilized for feasible mobile plan generation and predicting the optimal solution amongst the various plans. Additionally, a fuzzy model recommends personalized plans based on customers' typical service usage. This also maximizes company profits, contrasting with existing strategies employed by various telecom companies which focus on one of the two problems. The proposed GA-MPG algorithm demonstrated promising results on a prominent US-based telecom dataset encompassing around 7000 customers, with a substantial 44% reduction in customer churn. These findings are based on the simulation results. The algorithm also shows improvements of 13% and 18% in ARPU and company profit, respectively, over a defined period.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225977","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

Transforming Language Translation: A Deep Learning Approach to Urdu–English Translation 改变语言翻译：乌尔都语-英语翻译的深度学习方法

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-08-22 DOI: 10.1007/s12652-024-04839-2

Iqra Safder, Muhammad Abu Bakar, Farooq Zaman, Hajra Waheed, Naif Radi Aljohani, Raheel Nawaz, Saeed Ul Hassan

{"title":"Transforming Language Translation: A Deep Learning Approach to Urdu–English Translation","authors":"Iqra Safder, Muhammad Abu Bakar, Farooq Zaman, Hajra Waheed, Naif Radi Aljohani, Raheel Nawaz, Saeed Ul Hassan","doi":"10.1007/s12652-024-04839-2","DOIUrl":"https://doi.org/10.1007/s12652-024-04839-2","url":null,"abstract":"Machine translation has revolutionized the field of language translation in the last decade. Initially dominated by statistical models, the rise of deep learning techniques has led to neural networks, particularly Transformer models, taking the lead. These models have demonstrated exceptional performance in natural language processing tasks, surpassing traditional sequence-to-sequence models like RNN, GRU, and LSTM. With advantages like better handling of long-range dependencies and requiring less training time, the NLP community has shifted towards using Transformers for sequence-to-sequence tasks. In this work, we leverage the sequence-to-sequence transformer model to translate Urdu (a low resourced language) to English. Our model is based on a variant of transformer with some changes as activation dropout, attention dropout and final layer normalization. We have used four different datasets (UMC005, Tanzil, The Wire, and PIB) from two categories (religious and news) to train our model. The achieved results demonstrated that the model’s performance and quality of translation varied depending on the dataset used for fine-tuning. Our designed model has out performed the baseline models with 23.9 BLEU, 0.46 chrf, 0.44 METEOR and 60.75 TER scores. The enhanced performance attributes to meticulous parameter tuning, encompassing modifications in architecture and optimization techniques. Comprehensive parametric details regarding model configurations and optimizations are provided to elucidate the distinctiveness of our approach and how it surpasses prior works. We provide source code via GitHub for future studies.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225978","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

Device adaptation free-KDA based on multi-teacher knowledge distillation 基于多教师知识提炼的设备自适应自由 KDA

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-08-12 DOI: 10.1007/s12652-024-04836-5

Yafang Yang, Bin Guo, Yunji Liang, Kaixing Zhao, Zhiwen Yu

{"title":"Device adaptation free-KDA based on multi-teacher knowledge distillation","authors":"Yafang Yang, Bin Guo, Yunji Liang, Kaixing Zhao, Zhiwen Yu","doi":"10.1007/s12652-024-04836-5","DOIUrl":"https://doi.org/10.1007/s12652-024-04836-5","url":null,"abstract":"The keyboard, a major mean of interaction between human and internet devices, should beset right for good performance during authentication task. To guarantee that one legitimate user can interleave or simultaneously interact with two or more devices with protecting user privacy, it is essential to build device adaptation free-text keystroke dynamics authentication (free-KDA) model based on multi-teacher knowledge distillation methods. Some multi-teacher knowledge distillation methods have shown effective in C-way classification task. However, it is unreasonable for free-KDA model, since free-KDA model is one-class classification task. Instead of using soft-label to capture useful knowledge of source for target device, we propose a device adaptation free-KDA model. When one user builds the authentication model for target device with limited training samples, we propose a novel optimization objective by decreasing the distance discrepancy in Euclidean distance and cosine similarity between source and target device. And then, we adopt an adaptive confidence gate strategy to solve different correlation for each user between different source devices and target device. It is verified on two keystroke datasets with different types of keyboards, and compared its performance with the existing dominant multi-teacher knowledge distillation methods. Extensive experimental results demonstrate that AUC of target device reaches up to 95.17%, which is 15.28% superior to state-of-the-art multi-teacher knowledge distillation methods.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202568","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

Transfer learning in breast mass detection and classification 乳腺肿块检测和分类中的迁移学习

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-08-06 DOI: 10.1007/s12652-024-04835-6

Marya Ryspayeva, Alessandro Bria, Claudio Marrocco, Francesco Tortorella, Mario Molinara

{"title":"Transfer learning in breast mass detection and classification","authors":"Marya Ryspayeva, Alessandro Bria, Claudio Marrocco, Francesco Tortorella, Mario Molinara","doi":"10.1007/s12652-024-04835-6","DOIUrl":"https://doi.org/10.1007/s12652-024-04835-6","url":null,"abstract":"Covid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables the detection of suspicious lesions and classifying them in terms of malignancy. This paper aimed to investigate mass detection and classification in a large-scale OPTIMAM dataset with 6000 cases and extracted 3524 images with masses in the mammograms of the Hologic manufacturer. The methodology of the detection step is to train the RetinaNet architecture of ResNet50, ResNet101, and ResNet152 backbones with three types of initializations by ImageNet and COCO weights and from scratch. The dataset was pre-processed to generate two types of input with entire mammograms and patches, which are stated as the first and the second approaches. The results show that in the first approach, RetinaNet of ResNet50 backbone with ImageNet and COCO weights and ResNet152 with the same weights performed 0.91 True Positive Rate at 0.78 False Positive Per Image, respectively. In contrast, in the second approach, ResNet152 with ImageNet weights reached 0.88 TPR at 0.78 FPPI. In the classification step, the Transfer Learning approach was applied with fine-tuning by adding L2-regularization and class weights to balance class distribution in the datasets.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930504","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