BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning

Agricultural bioinformatics and machine learning techniques in Areca nut disease and crop improvement – a review

2024-11-08T07:01:08+00:00

Bioinformatics is an interdisciplinary scientific field comprising biology, mathematics, and computer science. It is the application of information technology to the management of biological data that aids in deciphering plant genomes. Biological research, which previously began in laboratories, fields, and botanical clinics, now begins at the computational level, using computers (in silico) for data analysis, experimental design, and hypothesis development. Agriculture is the backbone of the nation, so agricultural bioinformatics is one of the fastest-growing scientific fields that use computational approaches to study biology and life sciences. India ranks first in the world for areca nut production and many farmers depend on areca nut cultivation for their livelihood. Areca nut yields are affected by many diseases caused by heavy rainfall and high relative humidity. Early prediction of crop diseases based on weather data helps farmers take preventive measures. Many machine learning methods are used to detect a disease from image data. Expanding knowledge about the molecules and mechanisms associated with specific phenotypic traits and specific responses to biotic or abiotic stresses will be complemented by the predictive power of bioinformatics to influence agricultural practices and improve diagnostic, monitoring, and advocating innovative methods in traceability, enhancing human value and supporting sustainability at low cost. This review briefs about the field of agricultural bioinformatics and the application of machine-learning techniques in the overall crop improvement of areca nut including disease prediction.

Yoga Pose Recognition (YPR) using ML-DL and android application

2024-09-24T07:09:15+00:00

The study aimed to create a Human Activity Recognition (HAR) model for Yoga Pose Recognition and Classification
using datasets gathered through smart sensor technologies and imaging and filming devices to read various human
actions, recognize various poses, analyze them, and then predict and classify the Yoga pose with minimum error.
Pre-recorded data was fed to the model for the initial run and thereafter the model would learn and re-learn
new inputs and outputs by supervised learning methods. A collection of data from cameras present in smart
smartphones and other devices were used to create a dynamic dataset of posture photos and videos to predict the
most feasible output and add the mapping in the dataset to recognize particular Yoga poses. Yoga is a methodical
way of attaining balance and harmony both inside oneself and outside the body. It has its roots in ancient India.
Its history spans millennia, with the word “yoga” being first used in the Rig Veda, an ancient Indian scripture,
which dates back to around 1500 BC. The Atharva Veda, which was written about 1200–1000 BC, places a strong
emphasis on breath regulation. Indus-Saraswati seals and fossils depicting yoga sadhana practitioners have also
been discovered. These artifacts date back to 2700 BC (10). Nowadays, yoga is performed by millions of people
worldwide. It provides mental and physical health advantages, such as lowering stress, anxiety, and depression, as
well as physical benefits like better flexibility, strength, and posture. Yoga has grown popular as more individuals
try to live healthier lives.
The study investigated various human postures and actions to predict the possible Yoga pose performed by that
particular human through ML/DL (Machine Learning and Deep Learning) approaches. The proposed system or
model that learned and evolved by obtaining new data and through supervised learning. We have used single-user
pose recognition to create personalized datasets. Our aim was to provide a self-instruction system that allows
people to learn and practice yoga correctly by themselves. This development laid the foundation for building
such a system by discussing various ML and DL approaches to accurately classify Yoga poses on pre-recorded
videos and photos.

Early-stage diagnosis of diabetes mellitus using machine learning and uncertainty quantiﬁcation

2024-11-14T07:27:56+00:00

Diabetes is a chronic condition that has the power to ruin world health. A total of 3820 million people worldwide have diabetes, and the International Diabetes Federation (IDF) projects that number to double over the next 15◦years. Increase in blood glucose levels is a defining feature of diabetes, commonly known as diabetes mellitus. This condition can be determined using a variety of physical and chemical testing. The eyes, heart, kidneys, feet, and nerves are just a few of the human body parts that can be harmed by uncontrolled and incorrectly diagnosed diabetes, in addition to death. Thus, detecting and analyzing diabetes early can reduce the mortality rate. The research aims to develop a machine learning model for accurately predicting diabetes in humans using classifiers like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), logistic regression, Navie Bayes, Gradient Boosting, Decision Tree, Random Forest, and Ensemble Learning. The study uses the Pima Indian Diabetes Database (PIDD) dataset from Kaggle. Performance is compared using accuracy scores, Receiver Operating Characteristic (ROC), F-measure, and L1-Loss function. Uncertainty in medical datasets is addressed

Analyzing the guiding principles of AI ethics: A framing theory perspective on the communication of ethical considerations in artificial intelligence (AI)

2024-10-12T06:22:22+00:00

Various organizations have created AI ethics standards and protocols in an era of rapidly expanding AI, all to ensure
ethical AI use for the benefit of society. However, the ethical issues raised by AI’s societal applications in the actual
world have generated scholarly debates. Through the prism of framing theory in media and communication, this
study examines AI ethics principles from three significant organizations: Microsoft, NIST, and the AI HLEG of the
European Commission. Institutional AI ethics communication must be closely examined in this rapidly changing
technical environment because of how institutions frame their AI principles.