Threshold Computation to Discover Cluster Structure: A New Approach

Cluster members are decided based on how close they are with each other. Compactness of cluster plays an important role in forming better quality clusters. ICNBCF incremental clustering algorithm computes closeness factor between every two data series. To decide members of cluster, it is necessary to know one more decisive factor to compare, threshold. Internal evaluation measure of cluster like variance and dunn index provide required decisive factor. in intial phase of ICNBCF, this decisive factor was given manually by investigative formed closeness factors. With values generated by internal evaluation measure formule, this process can be automated. This paper shows the detailed study of various evaluation measuress to work with new incremental clustreing algorithm ICNBCF

Blood Pressure Estimation from Speech Recordings: Exploring the Role of Voice-over Artists

Hypertension, a prevalent global health concern, is associated with cardiovascular diseases and significant morbidity and mortality. Accurate and prompt Blood Pressure monitoring is crucial for early detection and successful management. Traditional cuff-based methods can be inconvenient, leading to the exploration of non-invasive and continuous estimation methods. This research aims to bridge the gap between speech processing and health monitoring by investigating the relationship between speech recordings and Blood Pressure estimation. Speech recordings offer promise for non-invasive Blood Pressure estimation due to the potential link between vocal characteristics and physiological responses. In this study, we focus on the role of Voice-over Artists, known for their ability to convey emotions through voice. By exploring the expertise of Voice-over Artists in controlling speech and expressing emotions, we seek valuable insights into the potential correlation between speech characteristics and Blood Pressure. This research sheds light on presenting an innovative and convenient approach to health assessment. By unraveling the specific role of Voice-over Artists in this process, the study lays the foundation for future advancements in healthcare and human-robot interactions. Through the exploration of speech characteristics and emotional expression, this investigation offers valuable insights into the correlation between vocal features and Blood Pressure levels. By leveraging the expertise of Voice-over Artists in conveying emotions through voice, this study enriches our understanding of the intricate relationship between speech recordings and physiological responses, opening new avenues for the integration of voice-related factors in healthcare technologies

Bibliometric Survey on Incremental Learning in Text Classification Algorithms for False Information Detection

The false information or misinformation over the web has severe effects on people, business and society as a whole. Therefore, detection of misinformation has become a topic of research among many researchers. Detecting misinformation of textual articles is directly connected to text classification problem. With the massive and dynamic generation of unstructured textual documents over the web, incremental learning in text classification has gained more popularity. This survey explores recent advancements in incremental learning in text classification and review the research publications of the area from Scopus, Web of Science, Google Scholar, and IEEE databases and perform quantitative analysis by using methods such as publication statistics, collaboration degree, research network analysis, and citation analysis. The contribution of this study in incremental learning in text classification provides researchers insights on the latest status of the research through literature survey, and helps the researchers to know the various applications and the techniques used recently in the field

Bibliometric Study of Bibliometric Papers about Clustering

Bibliometric survey or bibliometric review papers generally analyses the work done previously by eminent personalities, authors, countries and various institutions which was published in giant databases like Scopus, Web of Science, Google Scholar, Research Gate and others. Bibliometric papers provide amalgamation of wide range of research papers from journals, conferences, reviews and other papers, which are working papers, papers with results, proposals and few of them are survey papers etc. Bibliometric papers are One-Stop-Solution for the readers and upcoming researchers to get acquainted entirely about the specific topic / domain. Bibliometric papers also help in smartly locating research-gaps for the aspiring PhD scholars. There are varieties of bibliometric analysis carried out so far by the authors and hence such bibliometric papers plays a vital role in the fraternity of researchers, as a stepping stone. Clustering is the widely used and beneficial method of segregating heap of information and data in a meaningful manner, so as to effectually used by decision authorities for forecasting, assessing and planning etc. Clustering is widely applicable to numeric and text form of data which is available and generated in real time on large scale, due to invent of internet, IoT and other techniques. Hence it is essential to understand the overall research details about the clustering and alike domains, with a special focus on bibliometric papers published in the domain of clustering. This paper discusses about how many authors, institutions, countries etc. have published the bibliometric analysis in Scopus and WoS databases, so as to aptly direct the readers, researchers who wish to initiate their research in the field of clustering

MAPPING WITH THE HELP OF NEW PROPOSED ALGORITHM AND MODIFIED CLUSTER FORMATION ALGORITHM TO RECOMMEND AN ICE CREAM TO THE DIABETIC PATIENT BASED ON SUGAR CONTAIN IN IT

The research for suggesting an ice cream for a diabetic patient is carried out in data mining by using clustering and mapping between the data for ice cream and diabetic patients. Here, mapping of ice cream dataset with diabetic patient dataset is done by using MFCA, which is proposed and explained in this paper. The results obtained from MCFA algorithm and the new proposed algorithm are explained and verified and it is observed that they are having the relevance

Cheminformatics: A Patentometric Analysis

Cheminformatics has entrenched itself as a core discipline within chemistry, biology, and allied sciences, more particularly in the field of Drug Design Discovery and Development. The article begins with a patent analysis of the progressing field of cheminformatics from 1996 to early 2021 using the Relecura and Lens patent database. It proceeds with a description of patents in various domains and aspects. The eye-catching mind map shows the landscape of cheminformatics patent search. The results reveal the star rating-wise patent counts and the trends in the sub-technological research areas. At the end of the article, quantum clustering and eminent directions towards the future of cheminformatics have been discussed. This study would provide the directions to academicians, techno enthusiasts, researchers, stakeholders, or investors and helps increase the awareness of the potential of cheminformatics and quantum clustering

HR Process Automation: A Bibliometric Analysis

Automation is interpreted as the replacement of manual operations by electronics and computer-controlled systems. Human resource management is an indispensable part of every firm be it the space of retail, healthcare, education or any other sector. Activities such as hiring new workers, training, or making sure that local labour laws are obeyed with HR processes and are a crucial part of every organisation. HR has typically been believed of as an extremely manual department procedure. Employees are accustomed to doing this manually and getting the job done themselves. But everything around the HR processes are changing rapidly. HR Automation is a tool for increasing the efficiency of an employment organisation by freeing employees from tedious repetitive tasks and allowing them to focus on more complex assignments such as decision-making and strategy creation. Automation is interpreted as the replacement of manual operations by electronics and computer-controlled systems. By automating regular and routine HR tasks, organisations may lead to significant savings and resources they expend on manual HR processing and preparation. The HR space is being invaded by automation, and any automation that can be implemented will be implemented very quickly. This article is written with the help of Scopus, Web of Science, Google Scholar and Crossref databases. This article will be useful for upcoming researchers, students and managers in the field of HRM across the world

Detection of Pulmonary Embolism: Workflow Architecture and Comparative Analysis of the CNN Models

Machine learning has proven to be a practical medical image processing technique for pattern discovery in low-quality labelled and unlabeled datasets. Deep vein thrombosis and pulmonary embolism are both examples of venous thromboembolism, which is a key factor in patient mortality and necessitates prompt diagnosis by experts. An immediate diagnosis and course of treatment are necessary for the life-threatening cardiovascular condition known as pulmonary embolism (PE). In the study of medical imaging, especially the identification of PE, machine learning (ML) algorithms have produced encouraging results. This study's objective is to assess how well machine learning (ML) algorithms perform in identifying PE in computed tomography (CT) scans. A range of ML approaches were used to the dataset, including deep learning algorithms such as convolutional neural networks. The effectiveness of PE detection systems can be greatly enhanced by the use of cutting-edge methodologies like deep learning, which lowers the possibility of incorrect diagnoses and enables the quick administration of therapy to individuals who require it. This work contributes to the growing body of evidence that supports the use of ML in medical imaging and diagnosis. Future research should examine how these algorithms might be included into clinical workflows, resolving any potential implementation challenges, and making sure their adoption is done so in a secure and efficient way. In this study, we provide a thorough evaluation of three different models: the streamlined architecture MobileNetV2 with an accuracy of 96%, compared to other models like the Xception model with an accuracy of 91%, and the Efficientnet B5 model with an accuracy of 97%, after observation and process following

Analysis of Chinese Patents associated with Incremental Clustering Algorithms: A Review

With the advent of Internet-of-Things (IoT) and overall Information-Technology world, an enormous amount of data is getting generated dynamically and in real-time mode, in almost all domains of research and application systems. Such huge data has embedded patterns and hidden information to extract and learn. This learning is incremental in nature for all involved entities and users, as the data is growing exponentially in real-time. To achieve learning from such dynamic data sources, incremental clustering algorithms are used mandatorily. This mandate has given rise to increased patents related to incremental clustering concept, which is primarily a significant part of Machine Learning field. In this paper, we contribute to the in-progress discussion on the use of intellectual property resources, particularly patents related to machine learning, incremental clustering, incremental learning with a special focus to country China. Due consideration of the prior art search, the author found that China the country of registration of the application extensively contributes to the intellectual property related to incremental clustering domain hence felt the need to undertake this detailed patent analysis about this topic. We hope all readers, research scholars will be benefited with the latest research presented in this paper pertaining to various patents in the advanced areas of computer engineering

Bibliometric Survey on Impact of Sound Therapy on Blood Pressure and Covid-19.

The current situation of spread of Corona Virus is really worth to worry and critical. As we are moving from stage 2 to stage 3, it is really our duty and right to prevent its spread effectually, as its threat to life of every individual. As per the expert opinion from Health Organizations, the elder people are most at risk of Corona infection. But if the people with high blood pressure or high cholesterol fail to take their prescribed medication, then they are also prone to various infections, due to low immunity levels. Along with regular medication, Sound Therapy is proven the best to improve blood circulation to different organs and also to develop better heart health. So the main objective of this Bibliometric paper is to show positive impact of Yoga and Sound Therapy like clapping, which is one of the best acupressure techniques, on the control of blood pressure. These techniques may lead to one step towards avoiding the spread of Corona Virus infection. The Bibliometric analysis in this paper is done using the giant databases including Scopus, Web of Science, Google Scholar, and Research Gate. The tools like Gephi, iMapBuilder, NodeXL etc are used for Data Visualization purpose. The study exposed that most of the publications of impact of Sound Therapy on Covid-19 and blood pressure are from conference and journals, affiliated to Computer Science and healthcare, United States lead publications followed by United Kingdom and then Australia, India