A survey on deep learning and its impact on agriculture: Challenges and opportunities
M Albahar - Agriculture, 2023 - mdpi.com
The objective of this study was to provide a comprehensive overview of the recent
advancements in the use of deep learning (DL) in the agricultural sector. The author …
advancements in the use of deep learning (DL) in the agricultural sector. The author …
A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease
AD Arya, SS Verma, P Chakarabarti, T Chakrabarti… - Brain Informatics, 2023 - Springer
Alzheimer's disease (AD) is a brain-related disease in which the condition of the patient gets
worse with time. AD is not a curable disease by any medication. It is impossible to halt the …
worse with time. AD is not a curable disease by any medication. It is impossible to halt the …
[HTML][HTML] Nutrients deficiency diagnosis of rice crop by weighted average ensemble learning
MSH Talukder, AK Sarkar - Smart Agricultural Technology, 2023 - Elsevier
Rice is one of the most extensively cultivated food crops on the planet, especially in
Bangladesh, China, and India. However, rice production is frequently hampered by nutrient …
Bangladesh, China, and India. However, rice production is frequently hampered by nutrient …
Landslide identification using machine learning techniques: Review, motivation, and future prospects
Abstract The WHO (World Health Organization) study reports that, between 1998-2017, 4.8
million people have been affected by landslides with more than 18000 deaths. The …
million people have been affected by landslides with more than 18000 deaths. The …
Deep learning model for detection of brown spot rice leaf disease with smart agriculture
Given that it provides nourishment for more than half of humanity, rice is regarded as one of
the most significant plants in the world in agriculture. The quantity and quality of the product …
the most significant plants in the world in agriculture. The quantity and quality of the product …
An insight into diagnosis of depression using machine learning techniques: a systematic review
Background In this modern era, depression is one of the most prevalent mental disorders
from which millions of individuals are affected today. The symptoms of depression are …
from which millions of individuals are affected today. The symptoms of depression are …
An Improved Model for Diabetic Retinopathy Detection by using Transfer Learning and Ensemble Learning
MSH Talukder, AK Sarkar, S Akter… - arXiv preprint arXiv …, 2023 - arxiv.org
Diabetic Retinopathy (DR) is an ocular condition caused by a sustained high level of sugar
in the blood, which causes the retinal capillaries to block and bleed, causing retinal tissue …
in the blood, which causes the retinal capillaries to block and bleed, causing retinal tissue …
Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review
A Ghavidel, P Pazos - Journal of Cancer Survivorship, 2023 - Springer
Abstract Knowledge discovery in databases (KDD) is crucial in analyzing data to extract
valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in …
valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in …
[HTML][HTML] A comparative assessment of most widely used machine learning classifiers for analysing and classifying autism spectrum disorder in toddlers and …
Individuals with autism spectrum disorder (ASD) have social interaction and communication
challenges due to a disruption in brain development that impacts how they perceive and …
challenges due to a disruption in brain development that impacts how they perceive and …
SE_SPnet: Rice leaf disease prediction using stacked parallel convolutional neural network with squeeze‐and‐excitation
Rice is one of the significant crops, and the early identification and prevention of its diseases
are essential to ensure adequate and healthy availability to the world's growing population …
are essential to ensure adequate and healthy availability to the world's growing population …