Recently, many neural network methods have been proposed for multilabel classification in the literature. One of these recent methods is the Multi-Layer Extreme Learning Machines ((ML)-ELMs) in which stack auto encoders are used for tuning their weights. However, (ML)-ELMs suffer from three primary drawbacks: First, input weights and biases are chosen rando(ML)y; second, the pseudoinverse solution for calculating output weights will increase the reconstruction error; third, memory and execution time of transformation matrices are proportional to the number of hidden layers. In this paper, Multi-Layer Kernel Extreme Learning Machine ((ML)-CK-ELM) that uses a linear combination of base kernels in each layer is proposed for multi-label classification. The proposed approach effectively addresses the above-mentioned drawbacks. Furthermore, multi-label classification data are inherently characterized by multi-modal aspects due to a variety of labels assigned to each instance. Applying a combination of different kernels is the added advantage of (ML)-CK-ELM that implicitly assesses the inherent multi-modal aspects of multi-label data; each kernel can be effectively used to cover one of the modals better than other kernels. The empirical study indicates that (ML)-CK-ELM shows competitively better performance than other state-of-the-art methods, and experimental results of multilabel datasets verify the feasibility of (ML)-CK-ELM.

This study focuses on the characterization and investigation of effective agents in medicinal plants and nanoparticles, aiming to understand their potential applications. X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) techniques were employed to analyze the structural and morphological properties of the samples. XRD provided valuable information on crystalline phases, crystal structure, and lattice parameters, while SEM revealed surface morphology, particle size distribution, and aggregation behavior. These techniques facilitated a comprehensive understanding of the physical and chemical properties, crucial for effective utilization. Machine Learning ((ML)) analysis was employed to uncover patterns and correlations within the data. (ML) algorithms were used to identify significant features, establish predictive models, and gain insights into the relationships between sample properties and effective agents. This enhanced understanding of the factors influencing efficacy, paving the way for targeted applications. The study encompassed two main research areas. Firstly, a (ML) was developed to estimate Z, P>|Z|, and the 95% confidence interval by manipulating coefficients (COEF) and robust standard errors (ROBUST STD.ERR) in wider intervals compared to the experimental samples. The study revealed a direct relationship between coefficients and robust standard errors, with increasing coefficients leading to higher robust standard errors and an expanded 95% confidence interval. Additionally, the study emphasized the significance of income from Chinese medicinal materials in the financing process for growers, as income variations impacted their willingness to finance technology adoption. By exploring the connection between technology adoption and financing, the research aimed to enhance understanding and logical linkage, contributing to more effective and sustainable agricultural development.

The field of bioceramics has emerged as a critical component in various medical and dental applications, with calcium phosphate (CaP) materials like tricalcium phosphate (TCP) gaining significant attention. CaP bioceramics are valued for their exceptional biocompatibility, osteoconductivity, and ability to promote new bone formation, making them invaluable in the optimization of dental implant integration and performance. This study explores a novel approach to developing versatile CaP-based ceramics that can find applications in the pharmaceutical, dental, and even ancient artifacts preservation domains, leveraging the power of Machine Learning ((ML)) modeling techniques. Tricalcium phosphate, a widely studied CaP ceramic, was the focus of this investigation, as it can be fabricated with varying degrees of crystallinity and porosity to tailor its biodegradation and bone regeneration properties. Through the use of a feedforward artificial neural network (FFANN), the researchers were able to predict the changes in dental ceramics, biocompatibility, and tissue reactions across a wide range of non-toxicity and bone growth parameters. The FFANN modeling approach provided valuable insights into the relationships between these key attributes, allowing for the optimization of CaP-based ceramics for specific clinical and preservation applications. The versatility of TCP extends beyond dental implants, with applications in periodontal regeneration, tooth root repair, and even direct pulp capping procedures. By manipulating the material's composition and microstructure, researchers and clinicians can tailor the performance of CaP bioceramics to meet the diverse needs of the healthcare and cultural heritage sectors. As the field of bioceramics continues to evolve, the integration of advanced (ML) modeling techniques, such as the FFANN approach employed in this study, promises to unlock new possibilities for the development of innovative, tissue-friendly ceramics that can revolutionize dentistry, pharmaceutical formulations, and the preservation of precious ancient artifacts.