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The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.

Inflammation is a natural protective mechanism that occurs when the body’s tissue homeostatic mechanisms are disrupted by biotic, physical, or chemical agents. The immune response generates pro-inflammatory mediators, but excessive output, such as chronic inflammation, contributes to many persistent diseases. Some phenolic compounds work in tandem with nonsteroidal anti-inflammatory drugs (NSAIDs) to inhibit pro-inflammatory mediators’ activity or gene expression, including cyclooxygenase (COX). Various phenolic compounds can also act on transcription factors, such as nuclear factor-κB (NF-κB) or nuclear factor-erythroid factor 2-related factor 2 (Nrf-2), to up-or downregulate elements within the antioxidant response pathways. Phenolic compounds can inhibit enzymes associated with the development of human diseases and have been used to treat various common human ailments, including hypertension, metabolic problems, incendiary infections, and neurodegenerative diseases. The inhibition of the angiotensin-converting enzyme (ACE) by phenolic compounds has been used to treat hypertension. The inhibition of carbohydrate hydrolyzing enzyme represents a type 2 diabetes mellitus therapy, and cholinesterase inhibition has been applied to treat Alzheimer’s disease (AD). Phenolic compounds have also demonstrated anti-inflammatory properties to treat skin diseases, rheumatoid arthritis, and inflammatory bowel disease. Plant extracts and phenolic compounds exert protective effects against oxidative stress and inflammation caused by airborne particulate matter, in addition to a range of anti-inflammatory, anticancer, anti-aging, antibacterial, and antiviral activities. Dietary polyphenols have been used to prevent and treat allergy-related diseases. The chemical and biological contributions of phenolic compounds to cardiovascular disease have also been described. This review summarizes the recent progress delineating the multifunctional roles of phenolic compounds, including their anti-inflammatory properties and the molecular pathways through which they exert anti-inflammatory effects on metabolic disorders. This study also discusses current issues and potential prospects for the therapeutic application of phenolic compounds to various human diseases.

Obesity and diabetes are the most demanding health problems today, and their prevalence, as well as comorbidities, is on the rise all over the world. As time goes on, both are becoming big issues that have a big impact on people’s lives. Diabetes is a metabolic and endocrine illness set apart by hyperglycemia and glucose narrow-mindedness because of insulin opposition. Heftiness is a typical, complex, and developing overall wellbeing worry that has for quite some time been connected to significant medical issues in individuals, all things considered. Because of the wide variety and low adverse effects, herbal products are an important hotspot for drug development. Synthetic compounds are not structurally diverse and lack drug-likeness properties. Thus, it is basic to keep on exploring herbal products as possible wellsprings of novel drugs. We conducted this review of the literature by searching Scopus, Science Direct, Elsevier, PubMed, and Web of Science databases. From 1990 until October 2021, research reports, review articles, and original research articles in English are presented. It provides top to bottom data and an examination of plant-inferred compounds that might be utilized against heftiness or potentially hostile to diabetes treatments. Our expanded comprehension of the systems of activity of phytogenic compounds, as an extra examination, could prompt the advancement of remedial methodologies for metabolic diseases. In clinical trials, a huge number of these food kinds or restorative plants, as well as their bioactive compounds, have been shown to be beneficial in the treatment of obesity.

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The current healthcare sector is facing difficulty in satisfying the growing issues, expenses, and heavy regulation of quality treatment. Surely, electronic medical records (EMRs) and protected health information (PHI) are highly sensitive, personally identifiable information (PII). However, the sharing of EMRs, enhances overall treatment quality. A distributed ledger (blockchain) technology, embedded with privacy and security by architecture, provides a transparent application developing platform. Privacy, security, and lack of confidence among stakeholders are the main downsides of extensive medical collaboration. This study, therefore, utilizes the transparency, security, and efficiency of blockchain technology to establish a collaborative medical decision-making scheme. This study considers the experience, skill, and collaborative success rate of four key stakeholders (patient, cured patient, doctor, and insurance company) in the healthcare domain to propose a local reference-based consortium blockchain scheme, and an associated consensus gathering algorithm, proof-of-familiarity (PoF). Stakeholders create a transparent and tenable medical decision to increase the interoperability among collaborators through PoF. A prototype of PoF is tested with multichain 2.0, a blockchain implementing framework. Moreover, the privacy of identities, EMRs, and decisions are preserved by two-layer storage, encryption, and a timestamp storing mechanism. Finally, superiority over existing schemes is identified to improve personal data (PII) privacy and patient-centric outcomes research (PCOR).

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