Life Sciences Commons^™

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

Therapeutic Potential Of Snake Venom: Toxin Distribution And Opportunities In Deep Learning For Novel Drug Discovery, Anas Bedraoui, Montamas Suntravat, Salim El Mejjad, Salwa Enezari, Naoual Oukkache, Elda E. Sanchez, Jacob Galan, Rachid El Fatimy, Tariq Daouda

School of Medicine Publications and Presentations

Snake venom is a rich source of bioactive molecules that hold great promise for therapeutic applications. These molecules can be broadly classified into enzymes and non-enzymes, each showcasing unique medicinal properties. Noteworthy compounds such as Bradykinin Potentiating Peptides (BPP) and Three-Finger Toxins (3FTx) are showing therapeutic potential in areas like cardiovascular diseases (CVDs) and pain-relief. Meanwhile, components like snake venom metalloproteinases (SVMP), L-amino acid oxidases (LAAO), and Phospholipase A2s (PLA2) are paving new ways in oncology treatments. The full medicinal scope of these toxins is still emerging. In this review, we discuss drugs derived from snake venoms that address …

Classification Of Colorectal Cancer Using Resnet And Efficientnet Models, Abhishek Ranjan, Priyanshu Srivastva, B Prabadevi, R Sivakumar, Rahul Soangra, Shamala K. Subramaniam

Physical Therapy Faculty Articles and Research

Introduction:

Cancer is one of the most prevalent diseases from children to elderly adults. This will be deadly if not detected at an earlier stage of the cancerous cell formation, thereby increasing the mortality rate. One such cancer is colorectal cancer, caused due to abnormal growth in the rectum or colon. Early screening of colorectal cancer helps to identify these abnormal growth and can exterminate them before they turn into cancerous cells.

Aim:

Therefore, this study aims to develop a robust and efficient classification system for colorectal cancer through Convolutional Neural Networks (CNNs) on histological images.

Methods:

Despite challenges in …

Crop Classification In South Korea For Multitemporal Planetscope Imagery Using Sfc-Densenet-Am, Seonkyeong Seong, Anjin Chang, Junsang Mo, Sangil Na, Hoyong Ahn, Jaehong Oh, Jaewan Choi

Agricultural and Environmental Sciences Faculty Research

In this manuscript, a new methodology based on a deep learning model using a Siamese network and attention module was proposed to classify crop cultivation areas, such as onion and garlic, from multitemporal PlanetScope images in South Korea. To consider the seasonal characteristics of crops in the model, training data were constructed from multitemporal satellite images. It was generated using PlanetScope satellite imagery from January and April, corresponding to the seasonal growth period of onion and garlic, in South Korea. Image patches were generated by considering the ratio of crops to minimize the influence of imbalanced data in the training …

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …