Journal of Ambient Intelligence and Humanized Computing最新文献

The fuzzy set theory was introduced to handle uncertainty due to imprecision, vagueness and partial information. Then, its extensions such as intuitionistic fuzzy set, intuitionistic interval-valued fuzzy set, Pythagorean fuzzy set were introduced and applied successfully in many fields. Then another extension of orthopair fuzzy set was introduced as Fermatean fuzzy set which is characterized by membership degree and non-membership degree which makes it to provide an excellent tool to present imprecise opinions of humans in decision-making processes. This study is devoted to construct a novel Fermatean fuzzy divergence measure along with its evidence of legitimacy and to deliberate its key properties. The proposed divergence measure for Fermatean fuzzy sets with weighted aggregation operators is applied to fix decision-making problems through numerical illustrations. A comparative study is given between the proposed Fermatean fuzzy divergence measure and the extant methods to test its effectiveness, viability and expediency. Their results were compared in order to check the superiority of the proposed measure.

Abstract

Bayesian inference is one of the important topics in modern statistics. The information of the parameter in Bayesian statistics which is regarded as some random variable will be updated by that of the posterior distribution. In other words, all the inferences in Bayesian statistics are based on the updated posterior information, which has been proven to be a very powerful technique. In this paper, we study the Bayesian inference in the framework of uncertainty theory based on the uncertain Bayesian rule developed by Lio and Kang in 2022. To be more precise, issues on the point estimation, credible intervals and hypothesis testing in Bayesian statistics under uncertain theory are explored, and one application of our method in an IQ test problem is also given in this paper.

Abstract

Mobile charging provides a new way for energy replenishment in the Wireless Rechargeable Sensor Network (WRSN), where the Mobile Charger (MC) is employed for charging nodes sequentially via wireless energy transfer according to the mobile charging sequence scheduling result. Mobile Charging Sequence Scheduling for Optimal Sensing Coverage (MCSS-OSC) is a critical problem for providing network application performance; it aims to maximize the Quality of Sensing Coverage (QSC) of the network by optimizing the MC’s mobile charging sequence and remains a challenging problem due to its NP-completeness in nature. In this paper, we propose a novel Improved Q-learning Algorithm (IQA) for MCSS-OSC, where MC is taken as an agent to continuously learn the space of mobile charging strategies through approximate estimation and improve the charging strategy by interacting with the network environment. A novel reward function is designed according to the network sensing coverage contribution to evaluate the MC charging action at each charging time step. In addition, an efficient exploration strategy is also designed by introducing an optimal experience-strengthening mechanism to record the current optimal mobile charging sequence regularly. Extensive simulation results via Matlab2021 software show that IQA is superior to existing heuristic algorithms in network QSC, especially for large-scale networks. This paper provides an efficient solution for WRSN energy management and new ideas for performance optimization of reinforcement learning algorithms.

To develop rice varieties with better nutritional qualities, it is important to classify rice seeds accurately. Hyperspectral imaging can be used to extract spectral information from rice seeds, which can then be used to classify them into different varieties. The challenges of precise classification increase when there are many classes and few training samples. In this paper, we present a novel method for high-precision Hyperspectral Image (HSI) classification of 90 different classes of rice seeds using ensemble deep learning. Our method first employs band selection techniques to select the optimal hyperspectral bands for rice seed classification. Then, a deep neural network is trained with the selected hyperspectral and RGB data from rice seed images to obtain different models for different bands. Finally, an ensemble of deep learning models is employed to classify rice seed images and improve classification accuracy. The proposed method achieves an overall precision ranging from 92.73 to 96.17% despite a large number of classes and low data samples for each class and with only 15 selected hyperspectral bands. This precision is significantly higher than the state-of-the-art classical machine learning methods like random forest, confirming the effectiveness of the proposed method in classifying hyperspectral images of rice seeds.

As software gets more complicated, diverse, and crucial to people’s daily lives, exploitable software vulnerabilities constitute a major security risk to the computer system. These vulnerabilities allow unauthorized access, which can cause losses in banking, energy, the military, healthcare, and other key infrastructure systems. Most vulnerability scoring methods employ Natural Language Processing to generate models from descriptions. These models ignore Impact scores, Exploitability scores, Attack Complexity and other statistical features when scoring vulnerabilities. A feature vector for machine learning models is created from a description, impact score, exploitability score, attack complexity score, etc. We score vulnerabilities more precisely than we categorize them. The Decision Tree Regressor, Random Forest Regressor, AdaBoost Regressor, K-nearest Neighbors Regressor, and Support Vector Regressor have been evaluated using the metrics explained variance, r-squared, mean absolute error, mean squared error, and root mean squared error. The tenfold cross-validation method verifies regressor test results. The research uses 193,463 Common Vulnerabilities and Exposures from the National Vulnerability Database. The Random Forest regressor performed well on four of the five criteria, and the tenfold cross-validation test performed even better (0.9968 vs. 0.9958).

In this paper, we have proved fixed point results for a pair of soft fuzzy maps in complete ordered soft metric spaces. We have also given some useful corollaries to our main result along with examples. Moreover, the application is also presented in this communication to show the validity of new results.

Abstract

Video summarization is an emerging research field. In particular, static video summarization plays a major role in abstraction and indexing of video repositories. It extracts the vital events in a video such that it covers the entire content of the video. Frames having those important events are called keyframes which are eventually used in video indexing. It also helps in giving an abstract view of the video content such that the internet users are aware of the events present in the video before watching it completely. The proposed research work is focused on efficient static video summarization by extracting various visual features namely color, texture and shape features. These features are aggregated and clustered using a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. In order to produce good video summary by clustering, the parameters of DBSCAN algorithm are optimized by using a meta heuristic population based optimization called Artificial Algae Algorithm (AAA). The experimental results on two public datasets namely VSUMM and OVP dataset show that the proposed Static Video Summarization with Multi-objective Constrained Optimization (SVS_MCO) achieves better results when compared to existing methods.

The rapid development of multimedia information processing technology provides development opportunities for digitization in sports, among which motion capture technology, as the latest achievement of multimedia information processing technology, has gradually gained the attention of scholars and started to be used for visualization of sports movements. Therefore, this paper introduces a monocular video motion capture method and optimizes it for the problems of reconstructing human movements such as floating, ground penetration and sliding, which provides a technical path for the specific application of motion capture technology in the field of sports training and also provides a technical guarantee for the visualization of sports training movements. Introduced a new motion capture optimization method. This method captures human motion trajectories from monocular videos, and trajectory operations combine human pose estimation and physical constraints. The proposed method uses foot contact judgment to obtain foot contact events for each motion frame. Then, it optimizes the overall body motion trajectory of the key points based on the obtained contact conditions, making the generated motion visually closer to reality. This article proposes LiteHumanPose Net with a inference speed of up to 22FPS, and conducts experimental analysis and comparison of several popular pose estimation methods from the perspectives of frame rate and average accuracy, such as Sim pleBaseline, HRNet, and Hourglass Net. LiteHumanPose Net outperforms Hourglass Net in terms of frame rate and accuracy, while HRNet has high accuracy due to its multiple parameters but low frame rate. The LiteHumanPose network proposed in this article has a good balance between accuracy and frame rate, and has obvious landing advantages.

Coronavirus belongs to the family of Coronaviridae. It is responsible for COVID-19 communicable disease, which has affected 213 countries and territories worldwide. Researchers in computational fields have been active in proposing techniques to filter the information and recommendations about this disease and provide surveillance in controlling this outbreak. Researchers used Chest X-ray images, abdominal Computed Tomography scans, and Tweet datasets for building machine learning and deep learning-based models for COVID-19 predictions and forecasting purposes. Accuracy, sensitivity, specificity, precision, and F1-measure are the five primary evaluation criteria researchers employ to evaluate the quality of their study. This article summarises research works on COVID-19 based on machine learning and deep learning models. The analysis of these research works, along with their limitations and source of datasets, will give a quick start for future research to arrive at a defined direction.