Journal of Ambient Intelligence and Humanized Computing最新文献

Unconfined Compressive Strength (UCS) is a crucial mechanical parameter of rocks, which is pivotal in developing accurate geomechanical models. Traditionally, UCS estimation involves expensive and time-consuming methods, such as lab testing of retrieved core samples or well-log data analysis. This research presents a novel approach for real-time estimation of UCS, crucial in various geomechanical applications. It employs Random Forest (RF) prediction models enhanced by Runge Kutta Optimization (RKO) and Beluga Whale Optimization (BWO) algorithms for improved accuracy and efficiency. Validation using UCS samples from diverse soil types yields three distinct models: RFRK (RF + RKO), RFBW (RF + BWO), and an individual RF model, each contributing valuable insights. The RFBW model particularly stands out with high R² values (0.994) and a favorable RMSE (73.93), indicating superior predictive and generalization capabilities. This method represents a significant advancement in UCS prediction, offering efficiency and time-saving benefits across geomechanical fields.

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.‎.

This paper introduces a new protocol named MEDCO for eMErgency Detection and COmpression, designed to minimize data transmission and optimize sensor energy usage in wireless body sensor networks. MEDCO operates in two stages. The first stage assesses the patient’s condition based on vital signs and compares it with the previous state to determine if the data should be transmitted to medical staff. Data is only sent if a change in the patient’s situation is detected. The second stage focuses on compressing the identified data using two algorithms: range and changed vital signs methods. The range method classifies patient readings into ranges based on the current health situation before compressing them. At the same time, the changed vital signs algorithm considers both current and previous situations during compression. Through simulations using actual patient data, we demonstrated the effectiveness of our protocol in reducing data transmission by 97% while maintaining a high level of accuracy in the transmitted information. The range method outperforms by achieving an additional data reduction of 34.6% compared to the selected protocol from state of the art, and the changed vital signs method achieves a reduction of 6.4%.

Gene expression profiling for cancer diagnosis requires the identification of optimal and non-redundant gene subsets from microarray data. We present a multi-objective particle swarm optimization (PSO) approach that balances gene-class relevancy and inter-gene redundancy by integrating mutual information. Our method employs a dual-phase search strategy: an initial PSO search followed by a local search to accelerate convergence, and a subsequent Pareto front selection to extract the non-dominated gene subsets. Experiments on cancer microarray benchmark datasets demonstrate that our approach significantly enhances feature selection and diagnosis accuracy compared to existing methods. Notably, our approach incorporates a novel dual-evaluation framework and an improved particle representation scheme, which collectively enhance robustness and prevent premature convergence. These innovations ensure a comprehensive and effective gene selection process for cancer diagnosis.

The secure sharing and privacy protection of medical data are of great significance during the development of smart medical care. In order to achieve data sharing among medical institutions, ciphertext-policy attribute-based encryption (CP-ABE), a potential technology, allows users to encrypt data under access policies which are defined on certain attributes of the data consumer, and only allows the data consumer to decrypt those attributes conforming to the access policy. However, some existing CP-ABE schemes still have some shortcomings. For example, the efficiency of encryption and decryption is not high enough, and some cannot support more sufficient and expressive access structures. To solve the above problems, combined with blockchain, this paper presents a CP-ABE scheme with partial policy hiding based on prime order bilinear groups. Extensive experiment and analysis results reveal that the proposed scheme protects the privacy of users and realizes that attribute values are hidden in the access policy.

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.

Accurate indoor localization remains a significant challenge due to the complex nature of indoor environments. This paper proposes a novel method for constructing a radio map (RM) based on Kernel density estimation (KDE) and human trajectories (HT) to enhance indoor localization accuracy. The proposed method utilizes historical HT data in RM construction to capture the spatial variability and complexity of indoor environments, which is crucial for accurate localization. By employing KDE, kernel density maps are generated, identifying high-density regions where additional interpolated fingerprints are strategically placed to improve localization accuracy. In contrast to the conventional method of uniformly placing interpolated points (IPs), the proposed approach better models natural walking patterns and trajectories, thereby enhancing the uniqueness and accuracy of user position identification. Through extensive experiments with various HT patterns, the proposed KDE-RM optimization method consistently outperforms the conventional approach of evenly distributed IPs using Kriging and inverse distance weighting interpolation by up to 36.4%. This demonstrates the effectiveness and potential of the proposed method as a valuable tool for enhancing indoor localization.

The utilization of Wireless Sensor Networks (WSN) in the agricultural field represents a significant stride in the application of Information Technology. Recent advancements in technology have made it possible for sensor networks not only to provide real-time information about soil nutrient levels but also to assist in the automation of various agricultural processes. However, it’s crucial to acknowledge a substantial limitation associated with WSN, namely, energy consumption. Through the analysis of experimental data gathered from diverse soil types and employing sophisticated data analytics, it has been observed that the Nutrient Index exhibits a relatively stable pattern over time. Consequently, predictive neural network techniques can be employed to extract detailed insights from the primary inputs received from WSN. This approach eliminates the need for continuous operation of the WSN throughout the day, contributing to enhanced energy efficiency. To achieve this energy-efficient operation, the NR-MDEC protocol is implemented in conjunction with a coordination algorithm, resulting in a substantial improvement in overall efficiency.

The topic of persuasion in online conversations has social, political and security implications; as a consequence, the problem of predicting persuasive comments in online discussions is receiving increasing attention in the literature. Following recent advancements in graph neural networks, we analyze the impact of conversation structure in predicting persuasive comments in online discussions. We evaluate the performance of artificial intelligence models receiving as input graphs constructed on the top of online conversations sourced from the “Change My View” Reddit channel. We experiment with different graph architectures and compare the performance on graph neural networks, as structure-based models, and dense neural networks as baseline models. Experiments are conducted on two tasks: (1) persuasive comment detection, aiming to predict which comments are persuasive, and (2) influence prediction, aiming to predict which users are persuasive. The experimental results show that the role of the conversation structure in predicting persuasiveness is strongly dependent on its graph representation given as input to the graph neural network. In particular, a graph structure linking only comments belonging to the same speaker in the conversation achieves the best performance in both tasks. This structure outperforms both the baseline model, which does not consider any structural information, and structures linking different speakers’ comments with each other. Specifically, the F1 score of the best performing model is 0.58, which represents an improvement of 5.45% over the baseline model (F1 score of 0.55) and 7.41% over the model linking different speakers’ comments (F1 score of 0.54).

Advanced metering infrastructure (AMI) plays a critical role in the smart grid by integrating metering systems with communication capabilities, especially for the industrial internet of things. However, existing authentication protocols have proven ineffective against quantum computing attacks and are computationally intensive since AMI contains limited computing components, such as smart meters. In this paper, we present a novel, efficient module learning with errors-based authentication and key agreement system for AMI, which we call EMAS. As part of the security measures of EMAS, Kyber Post-Quantum Public Key Encryption, a one-time pad mechanism, and hash functions are used. A formal and informal analysis of the security features is presented, showing that the proposed system is secure and resistant to known attacks. The performance analysis of our proposed EMAS on a B-L475E-IOT01A node equipped with a ARM Cortex M4 microcontroller shows that EMAS is more efficient than existing relevant schemes. About the computation time, EMAS takes 15.693 ms. This result is lower than other existing relevant schemes.