Journal of Ambient Intelligence and Humanized Computing最新文献

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.

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.

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.

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.

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.

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.

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.

Sleep posture is closely related to sleep quality, and can offer insights into an individual’s health. This correlation can potentially aid in the early detection of mental health disorders such as depression and anxiety. Current research focuses on embedding pressure sensors in bedsheets, attaching accelerometers on a subject’s chest, and installing cameras in bedrooms for sleep posture monitoring. However, such solutions sacrifice either the user's sleep comfort or privacy. This study explores the effectiveness of using contactless ultra-wideband (UWB) sensors for sleep posture monitoring. We employed a UWB dataset that is composed of the measurements from 12 volunteers during sleep. A stacking ensemble learning method is introduced for the monitoring of sleep postural transitions, which constitute two levels of learning. At the base-learner level, six transfer learning models (VGG16, ResNet50V2, MobileNet50V2, DenseNet121, VGG19, and ResNet101V2) are trained on the training dataset for initial predictions. Then, the logistic regression is employed as a meta-learner which is trained on the predictions gained from the base-learner to obtain final sleep postural transitions. In addition, a sleep posture monitoring algorithm is presented that can give accurate statistics of total sleep postural transitions. Extensive experiments are conducted, achieving the highest accuracy rate of 86.7% for the classification of sleep postural transitions. Moreover, time-series data augmentation is employed, which improves the accuracy by 13%. The privacy-preserving sleep monitoring solution presented in this paper holds promise for applications in mental health research.

This paper presents a new model based on Convolutional Neural Networks (CNN) with a long short-term memory network (LSTM) and ensemble technique for identifying seven different dogs’ behaviors. The proposed model uses data collected from two sensors attached to the dog’s back and neck. In the initial step in the model, the undefined tasks are removed, and the synthetic minority oversampling technique (SMOTE) is performed to address the imbalanced data problem. Then, CNN_LSTM and ensemble classifier are adapted to identify various dog behaviors. Finally, two experiments are performed to evaluate the model. The first experiment is performed utilizing the original data (imbalanced datasets) while the second uses a balanced dataset. Experimental results can identify seven dog behaviors with a potential accuracy of 96.73%, 96.76% sensitivity, 96.73% specificity, and 96.73% F1 score. Therefore, the SMOTE method, a data balancing strategy, not only overcomes the unbalanced data problem but also significantly improves minority class accuracy. Additionally, the suggested model is tested against cutting-edge algorithms, and the outcomes demonstrate its superior performance.

Cervical cancer is the most prevailing woman illness globally. Since cervical cancer is a very preventable illness, early diagnosis exhibits the most adaptive plan to lessen its global responsibility. However, because of infrequent knowledge, shortage of access to pharmaceutical centers, and costly schemes worldwide, most probably in emerging nations, the vulnerable subject populations cannot regularly experience the test. So, we need a clinical screening analysis to diagnose cervical cancer early and support the doctor to heal and evade cervical cancer?s spread in women?s other organs and save several lives. Thus, this paper introduces a novel hybrid approach to solve such problems: a hybrid feature selection approach with the Bayesian optimization-based optimized CatBoost (HFS-OCB) method to diagnose and predict cervical cancer risk. Genetic algorithm and mutual information approaches utilize feature selection methodology in this suggested research and form a hybrid feature selection (HFS) method to generate the most significant features from the input dataset. This paper also utilizes a novel Bayesian optimization-based hyperparameter optimization approach: optimized CatBoost (OCB) method to provide the most optimal hyperparameters for the CatBoost algorithm. The CatBoost algorithm is used to classify cervical cancer risk. There are two real-world, publicly available cervical cancer-based datasets utilized in this suggested research to evaluate and verify the suggested approach?s performance. A 20-fold cross-validation strategy and a renowned performance evaluation metric are utilized to assess the suggested approach?s performance. The outcome implies that the possibility of forming cervical cancer can be efficiently foretold using the suggested HFS-OCB method. Therefore, the suggested approach?s indicated result is compared with the other algorithms and provides the prediction. Such a predicted result shows that the suggested approach is more capable, reliable, scalable, and more effective than the other machine learning algorithms.