Journal of Ambient Intelligence and Humanized Computing最新文献

{"title":"Fuzzy performance estimation of real-world driver’s stress recognition models based on physiological signals and deep learning approach","authors":"Muhammad Amin, Khalil Ullah, Muhammad Asif, Habib Shah, Abdul Waheed, Irfanud Din","doi":"10.1007/s12652-024-04834-7","DOIUrl":"https://doi.org/10.1007/s12652-024-04834-7","url":null,"abstract":"<p>Driver’s mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in losses of humans, vehicles, and infrastructure. Likewise, persistent mental stress could develop mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning (ML) approaches. These approaches recognize different stress levels based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring the good quality features from these modalities using feature engineering is often a difficult job. The recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. Conventional DL models, however, frequently over-fit due to large number of parameters. Thus, large networks face gradient vanishing issues causing an increase in learning failure and generalization errors. Furthermore, it is often hard to acquire a large dataset for training a deep learning model from scratch. To overcome these problems for driver’s stress recognition domain, this paper proposes fast and computationally efficient deep transfer learning models based on Xception pre-trained neural networks. These models classify the driver’s Low, Medium, and High stress levels through electrocardiogram (ECG), heart rate (HR), galvanic skin response (GSR), electromyogram (EMG), and respiration (RESP) signals. Continuous Wavelet Transform (CWT) acquires the scalograms for ECG, HR, GSR, EMG, and RESP signals separately. Then unimodal Xception models are trained based on these scalograms to classify the three stress levels. The proposed Xception models have achieved 97.2%, 86.4%, 82.7%, 71.9%, and 68.9% average validation accuracies based on ECG, RESP, HR, GSR, and EMG signals, respectively. The fuzzy EDAS (evaluation based on distance from average solution) approach also evaluates the performance of proposed models based on accuracy, recall, precision, F-score, and specificity. For the driver’s three stress levels, fuzzy EDAS performance estimation shows that the proposed ECG, RESP, and HR based Xception models achieved 1st, 2nd, and 3rd positions, respectively.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880770","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}

{"title":"A classifier based on mixed radial basis function network and combinatorial optimization model for medical diseases diagnosis","authors":"Taoufyq Elansari, Mohammed Ouanan, Hamid Bourray","doi":"10.1007/s12652-024-04840-9","DOIUrl":"https://doi.org/10.1007/s12652-024-04840-9","url":null,"abstract":"<p>The Mixed Radial Basis Function Neural Network (MRBFNN) is an artificial neural network that employs Radial Basis Functions (RBFs) as activation functions in its hidden layer. The number of neurons in the hidden layer and the choice of RBF functions used in these neurons significantly affect the convergence of MRBFNN learning algorithms and impact the overall performance of neural networks. This article presents a nonlinear optimization model and an algorithm to select an appropriate architecture and learning strategy for MRBFNN. To approximate the solution of our model, we utilized an algorithm based on Particle Swarm Optimization (PSO) techniques. We will apply our approach in Medical Diseases Diagnosis (MDD). The numerical results obtained demonstrate the effectiveness of the proposed theoretical approach and underscore the advantages of the new modeling methodology.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880771","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}

{"title":"Research on the training method of special strength quality of competitive taekwondo based on multi-scale Retinex algorithm under the background of “Internet+”","authors":"Fei Liu","doi":"10.1007/s12652-024-04830-x","DOIUrl":"https://doi.org/10.1007/s12652-024-04830-x","url":null,"abstract":"<p>At present, there are some problems in strength quality training methods, such as the sports training effect can not reach the expected goal, the feasibility is not good, and the lack of pertinence leads to the unsatisfactory training effect of some athletes. In order to improve the quality training effect of competitive Taekwondo special strength, this paper studies the quality training method of competitive Taekwondo special strength based on multi-scale Retinex algorithm under the background of “Internet+”. In this method, the strength quality training video image acquisition module is used to collect the strength quality training video image and transmit it to the strength quality training video image processing module, and the multi-scale Retinex algorithm is used to enhance the strength quality training video image and correct the measurement error of special strength quality training; Analyze the data related to the special strength quality training of competitive Taekwondo athletes in the intelligent evaluation module of strength quality training results, construct an evaluation index system, and evaluate the strength quality training results of competitive Taekwondo athletes; According to the evaluation results of the evaluation module, provide targeted special strength quality training programs for competitive taekwondo athletes; All sports training data are stored in the database management module. The experimental results show that this method can significantly improve the special strength quality training effect of competitive Taekwondo athletes, and can better grasp the training content and movement control accuracy.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"133 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778949","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}

{"title":"Intuitionistic fuzzy rough set model based on k-means and its application to enhance prediction of aptamer–protein interacting pairs","authors":"Pankhuri Jain, Anoop Tiwari, Tanmoy Som","doi":"10.1007/s12652-024-04837-4","DOIUrl":"https://doi.org/10.1007/s12652-024-04837-4","url":null,"abstract":"<p>Aptamers are very interesting peptide molecules or oligonucleic acid. They are used to bind particular target molecules. Aptamers play vital roles in various practical applications and physiological functions. Consequently, several diseases can be treated using therapies based on aptamer proteins and designing the binding of aptamers to specific proteins is essential to advance understanding into processes of interaction between aptamer-protein. Despite the wide applications of aptamers, identification of interaction between aptamer protein is always inadequate and challenging. Therefore, it is necessary to develop a computational approach for achieving good predictions of interaction between aptamer-protein. In the present study, a novel method for enhancing the prediction of interacting aptamer-target pairs based on sequence features obtained from both aptamers and their target proteins by employing a novel k-mean based intuitionistic fuzzy rough feature selection method is proposed. Firstly, an intuitionistic fuzzy rough set model based on k nearest neighbour concept is proposed. Then, a novel feature selection technique is introduced by using this model. Furthermore, non-redundant and relevant features are selected from training as well as testing datasets by using proposed feature selection technique. Secondly, SMOTE (Synthetic Minority Oversampling Technique) is applied to obtain the optimal balanced training and testing datasets. Thirdly, we apply various machine learning algorithms on optimally balanced reduced training and testing datasets to evaluate their performances. Experimental results shows that the best prediction performance is obtained by boosted random forest learning algorithm. Using a 10 fold cross-validation test, the proposed method is a good performer, with sensitivity of 91.3, 86.4, specificity of 91.9, 84.8, overall accuracy of 91.60%, 85.60%, Mathews correlation coefficient of 0.832, 0.713, AUC (area under curve) of 0.969, 0.908, and g-means of 91.5, 85.5 on optimal balanced reduced training and testing datasets consisting of aptamer-protein interacting pairs. Finally, a comparative study of the best obtained results with the existing best results is presented, which clearly indicates that our proposed approach is the best performing approach till date.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778951","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}

{"title":"Exploring bystander contagion in cyberbully detection: a systematic review","authors":"Haifa Saleh Alfurayj, Belén F. Hurtado, Syaheerah Lebai Lutfi, Toqir A. Rana","doi":"10.1007/s12652-024-04831-w","DOIUrl":"https://doi.org/10.1007/s12652-024-04831-w","url":null,"abstract":"<p>Since the advent of mass access to the Internet, aggressive behaviors such as cyberbullying have become widespread on social networking sites. An aggressive online environment can lead to negative attitudes that negatively impact the victim, bystanders, and the bullies themselves. One of the main reasons for the increase in this type of behavior is contagion from bystanders—a phenomenon that needs to be stopped. In recent years, many studies have looked at cyberbullying detection, considering various factors to improve detection, such as extracting different types of features, comparing the performance of different classifiers, and processing datasets in myriad ways. It is evident from our findings that previous works in the literature fell short of detecting cyberbullying by ignoring the characteristics of bystanders and their roles. Thus, this paper aims to present a systematic literature review of research conducted over the past 10 years to determine which methods encompassed features related to bystanders and their role and analyzed the contagion and causal factors of the spread of cyberbullying. There are different studies confirmed the existence of bystander contagion, which researchers rarely consider to detect cyberbullying. This gap could be exploited in future studies and used to improve the detection of cyberbullying. Therefore, in this paper, the summary and comparison of findings from the selected studies that examined the role of bystanders in cyberbullying are presented, concluding how bystander-related features could contribute to the detection of cyberbullying.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141741484","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}

{"title":"Providing bank branch ranking algorithm with fuzzy data, using a combination of two methods DEA and MCDM","authors":"Rouhollah Kiani-Ghalehno, Ali Mahmoodirad","doi":"10.1007/s12652-024-04833-8","DOIUrl":"https://doi.org/10.1007/s12652-024-04833-8","url":null,"abstract":"<p>Financial and credit institutions need to evaluate and rank their subsidiaries to control and improve their performances. There are several methods to evaluate the performance of such branches. In order to take advantage of the strengths of each of these methods and cover some of the limitations that exist in each of these methods alone, in this study, an algorithm which is a combination of multi-criteria decision-making methods, statistical analysis, and data envelopment analysis is proposed. The location of each of the methods mentioned in the steps of the algorithm, and its simulation to a standard linear programming model in MATLAB software, is the main research problem that is designed and presented for fuzzy type uncertain data. The proposed algorithm was used for 1736 branches of a certain bank in banking sector of Iran with uncertain data. Analysis of the results for different alpha-cuts and testing them with SPSS software show that with increasing the range of fuzzy numbers, the number of efficient branches increases and also affect the ranking. Nevertheless, there is still a significant correlation even in the alpha-cut changes in the ranking results.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141741482","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}

{"title":"Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121","authors":"A. Muthukumar, M. Thanga Raj, R. Ramalakshmi, A. Meena, P. Kaleeswari","doi":"10.1007/s12652-024-04829-4","DOIUrl":"https://doi.org/10.1007/s12652-024-04829-4","url":null,"abstract":"<p>An additional tool for swaying public opinion on social media is to present recent developments in the creation of natural language. The term “Deep fake” originates from deep learning technology, which effortlessly and seamlessly steers someone toward digital media. Artificial Intelligence (AI) techniques are a crucial component of deep fakes. The generative powers of generative capabilities greatly reinforce the advancements in language modeling for content generation. Due to low-cost computing infrastructure, sophisticated tools, and readily available content on social media, deep fakes propagate misinformation and hoaxes. These technologies make it simple to produce misinformation that spreads fear and confusion to everyone. As such, distinguishing between authentic and fraudulent content can be challenging. This study presents a novel automated approach for the identification of deep fakes, based on Adaptive Gaining Sharing Knowledge (AGSK) and using DenseNet121 architecture. During pre-processing, the image’s sensitive data variance or noise is eliminated. Following that, CapsuleNet is used to extract the feature vectors. The deep fake is identified from the resulting feature vectors by an AGSK with DenseNet121 architecture, together with the hyper-parameter that has been optimized using the AGSK model. Propaganda and defamation pose less of a concern, and the results of the suggested deepfake image recognition model show how reliable and successful the model is. The accuracy of detection is 98% higher than other cutting-edge models.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612837","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}

{"title":"On weighted threshold moment estimation of uncertain differential equations with applications in interbank rates analysis","authors":"Jiajia Wang, Helin Gong, Anshui Li","doi":"10.1007/s12652-024-04828-5","DOIUrl":"https://doi.org/10.1007/s12652-024-04828-5","url":null,"abstract":"<p>Uncertainty theory is a branch of mathematics for modeling belief degrees. Within the framework of uncertainty theory, uncertain variable is used to represent quantities with uncertainty, and uncertain process is used to model the evolution of uncertain quantities. Uncertain differential equation is a type of differential equation involving uncertain processes, which has been successfully applied in many disciplines such as finance, optimal control, differential game, epidemic spread and so on. Uncertain differential equation has become the main tool to deal with dynamic uncertain systems. One of the key issues within the research of uncertain differential equations is the estimation of parameters involved based on the observed data. However, it is relatively difficult to solve this issue when the structures of the corresponding terms in the equations are not known in advance. To address this problem, one nonparametric estimation technique called weighted threshold moment estimation for homogeneous uncertain differential equations is proposed in this paper when no prior information about the terms is obtained. Numerical examples are presented to demonstrate the feasibility and efficiency of our method, highlighted by an empirical study of the Shanghai Interbank Offered Rate in China. The paper concludes with final remarks and recommendations for future research.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612835","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}

{"title":"Weed detection in precision agriculture: leveraging encoder-decoder models for semantic segmentation","authors":"Shreya Thiagarajan, A. Vijayalakshmi, G. Hannah Grace","doi":"10.1007/s12652-024-04832-9","DOIUrl":"https://doi.org/10.1007/s12652-024-04832-9","url":null,"abstract":"<p>Precision agriculture uses data gathered from various sources to improve agricultural yields and the effectiveness of crop management techniques like fertiliser application, irrigation management, and pesticide application. Reduced usage of agrochemicals is a key step towards more sustainable agriculture. Weed management robots which can perform tasks like selective sprinkling or mechanical weed elimination, contribute to this objective. A trustworthy crop/weed classification system that can accurately recognise and classify crops and weeds is required for these robots to function. In this paper, we explore various deep learning models for achieving reliable segmentation results in less training time. We classify every pixel of the images into different categories using semantic segmentation models. The models are based on an encoder-decoder architecture, where feature maps are extracted during encoding and spatial information is recovered during decoding. We examine the segmentation output on a beans dataset containing different weeds, which were collected under highly distinct environmental conditions, including cloudy, rainy, dawn, evening, full sun, and shadow.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612836","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}

{"title":"A transformer-based Urdu image caption generation","authors":"Muhammad Hadi, Iqra Safder, Hajra Waheed, Farooq Zaman, Naif Radi Aljohani, Raheel Nawaz, Saeed Ul Hassan, Raheem Sarwar","doi":"10.1007/s12652-024-04824-9","DOIUrl":"https://doi.org/10.1007/s12652-024-04824-9","url":null,"abstract":"<p>Image caption generation has emerged as a remarkable development that bridges the gap between Natural Language Processing (NLP) and Computer Vision (CV). It lies at the intersection of these fields and presents unique challenges, particularly when dealing with low-resource languages such as Urdu. Limited research on basic Urdu language understanding necessitates further exploration in this domain. In this study, we propose three Seq2Seq-based architectures specifically tailored for Urdu image caption generation. Our approach involves leveraging transformer models to generate captions in Urdu, a significantly more challenging task than English. To facilitate the training and evaluation of our models, we created an Urdu-translated subset of the flickr8k dataset, which contains images featuring dogs in action accompanied by corresponding Urdu captions. Our designed models encompassed a deep learning-based approach, utilizing three different architectures: Convolutional Neural Network (CNN) + Long Short-term Memory (LSTM) with Soft attention employing word2Vec embeddings, CNN+Transformer, and Vit+Roberta models. Experimental results demonstrate that our proposed model outperforms existing state-of-the-art approaches, achieving 86 BLEU-1 and 90 BERT-F1 scores. The generated Urdu image captions exhibit syntactic, contextual, and semantic correctness. Our study highlights the inherent challenges associated with retraining models on low-resource languages. Our findings highlight the potential of pre-trained models for facilitating the development of NLP and CV applications in low-resource language settings.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141513000","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}