4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer.

Refrigeration systems are complex, non-linear, multi-modal, and multi-dimensional. However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm.

Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture.

Agriculture is the primary and oldest industry in the world and has been transformed over the centuries from the prehistoric era to the technology-driven 21 century, where people are always solving complex problems with the aid of technology. With the power of Information and Communication Technologies (ICTs), the world has become a global village, where every digital object that prevails in the world is connected to each other with the Internet of Things (IoT). The fast proliferation of IoT-based technology has revolutionized practically every sector, including agriculture, shifting the industry from statistical to quantitative techniques.

A transformer fine-tuning strategy for text dialect identification.

Online medical consultation can significantly improve the efficiency of primary health care. Recently, many online medical question-answer services have been developed that connect the patients with relevant medical consultants based on their questions. Considering the linguistic variety in their question, social background identification of patients can improve the referral system by selecting a medical consultant with a similar social origin for efficient communication.

Pharmacotherapeutics Applications and Chemistry of Chalcone Derivatives.

Chalcones have been well examined in the extant literature and demonstrated antibacterial, antifungal, anti-inflammatory, and anticancer properties. A detailed evaluation of the purported health benefits of chalcone and its derivatives, including molecular mechanisms of pharmacological activities, can be further explored. Therefore, this review aimed to describe the main characteristics of chalcone and its derivatives, including their method synthesis and pharmacotherapeutics applications with molecular mechanisms.

A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming.

Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to overcome many of the challenges associated with their farming activities by enabling precise and timely decision making on the basis of data that are observed and subsequently converged. In this regard, Artificial Intelligence (AI) holds a key place, whereby it can assist key stakeholders in making precise decisions regarding the conditions on their farms.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives.

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity.

Spatial position constraint for unsupervised learning of speech representations.

The success of supervised learning techniques for automatic speech processing does not always extend to problems with limited annotated speech. Unsupervised representation learning aims at utilizing unlabelled data to learn a transformation that makes speech easily distinguishable for classification tasks, whereby deep auto-encoder variants have been most successful in finding such representations. This paper proposes a novel mechanism to incorporate geometric position of speech samples within the global structure of an unlabelled feature set.