Forecasting seasonal hydrologic response in major river basins.

Seasonal precipitation variation due to natural climate variation influences stream flow and the apparent frequency and severity of extreme hydrological conditions such as flood and drought. To study hydrologic response and understand the occurrence of extreme hydrological events, the relevant forcing variables must be identified. This study attempts to assess and quantify the historical occurrence and context of extreme hydrologic flow events and quantify the relation between relevant climate variables. Once identified, the flow data and climate variables are evaluated to identify the primary relationship indicators of hydrologic extreme event occurrence. Existing studies focus on developing basin-scale forecasting techniques based on climate anomalies in El Nino/La Nina episodes linked to global climate. Building on earlier work, the goal of this research is to quantify variations in historical river flows at seasonal temporal-scale, and regional to continental spatial-scale. The work identifies and quantifies runoff variability of major river basins and correlates flow with environmental forcing variables such as El Nino, La Nina, sunspot cycle. These variables are expected to be the primary external natural indicators of inter-annual and inter-seasonal patterns of regional precipitation and river flow. Relations between continental-scale hydrologic flows and external climate variables are evaluated through direct correlations in a seasonal context with environmental phenomenon such as sun spot numbers (SSN), Southern Oscillation Index (SOI), and Pacific Decadal Oscillation (PDO). Methods including stochastic time series analysis and artificial neural networks are developed to represent the seasonal variability evident in the historical records of river flows. River flows are categorized into low, average and high flow levels to evaluate and simulate flow variations under associated climate variable variations. Results demonstrated not any particular method is suited to represent scenarios leading to extreme flow conditions. For selected flow scenarios, the persistence model performance may be comparable to more complex multivariate approaches, and complex methods did not always improve flow estimation. Overall model performance indicates inclusion of river flows and forcing variables on average improve model extreme event forecasting skills. As a means to further refine the flow estimation, an ensemble forecast method is implemented to provide a likelihood-based indication of expected river flow magnitude and variability. Results indicate seasonal flow variations are well-captured in the ensemble range, therefore the ensemble approach can often prove efficient in estimating extreme river flow conditions. The discriminant prediction approach, a probabilistic measure to forecast streamflow, is also adopted to derive model performance. Results show the efficiency of the method in terms of representing uncertainties in the forecasts

Report of the 4th World Climate Research Programme International Conference on Reanalyses

The 4th WCRP International Conference on Reanalyses provided an opportunity for the international community to review and discuss the observational and modelling research, as well as process studies and uncertainties associated with reanalysis of the Earth System and its components. Characterizing the uncertainty and quality of reanalyses is a task that reaches far beyond the international community of producers, and into the interdisciplinary research community, especially those using reanalysis products in their research and applications. Reanalyses have progressed greatly even in the last 5 years, and newer ideas, projects and data are coming forward. While reanalysis has typically been carried out for the individual domains of atmosphere, ocean and land, it is now moving towards coupling using Earth system models. Observations are being reprocessed and they are providing improved quality for use in reanalysis. New applications are being investigated, and the need for climate reanalyses is as strong as ever. At the heart of it all, new investigators are exploring the possibilities for reanalysis, and developing new ideas in research and applications. Given the many centres creating reanalyses products (e.g. ocean, land and cryosphere research centres as well as NWP and atmospheric centers), and the development of new ideas (e.g. families of reanalyses), the total number of reanalyses is increasing greatly, with new and innovative diagnostics and output data. The need for reanalysis data is growing steadily, and likewise, the need for open discussion and comment on the data. The 4th Conference was convened to provide a forum for constructive discussion on the objectives, strengths and weaknesses of reanalyses, indicating potential development paths for the future

Insect Classification and Explainability from Image Data via Deep Learning Techniques

Since the dawn of the Industrial Revolution, humanity has always tried to make labor more efficient and automated, and this trend is only continuing in the modern digital age. With the advent of artificial intelligence (AI) techniques in the latter part of the 20th century, the speed and scale with which AI has been leveraged to automate tasks defy human imagination. Many people deeply entrenched in the technology field are genuinely intrigued and concerned about how AI may change many of the ways in which humans have been living for millennia. Only time will provide the answers. This dissertation is concerned with designing, deploying and validating computer vision algorithms (a branch of AI dealing with image data sets) for addressing a range of problems in insect classifications. The broader impacts of this dissertation lie in agriculture, species evolution, and public health. Bees are vital in agriculture and ecology. Food cultivation depends on bees to a great extent. Therefore ensuring a healthy environment for bees and the conservation of endangered bee species are a top priority. For conserving bees, their rapid identification in nature is necessary. To help entomologists in this regard, we design, deploy and validate convolutional neural network models to classify bees from image datasets. At first, we collected 6,332 original Research-Grade images of bees and other insects from the iNaturalist platform. We trained and evaluated our first model to classify bee images from other insect images using a VGG16-based CNN model. We could achieve more than 91% accuracy for that model. We trained another CNN model using ResNet-101-based model to classify be- tween bumble bee and other bee images, and for that model we could achieve almost 89% classification accuracy. We used cutting-edge Class Activation Maps (CAMs) to test our model’s capability to identify insect body pixels from the background pixels. We also collected 360 mimic insect images to evaluate how capable our model is for classifying between bees and bee mimics from morphological perspective. We discovered that bee mimics were most capable of fooling our models compared to the non-mimic insects. Additionally, we employed the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm on the mimic insect images. t-SNE is an unsupervised, non-linear technique for dimensionality reduction, primarily used for visualizing high-dimensional data. We employed this method in our study to understand the clustering of various mimic groups. We found out that t-SNE generated 12 distinct clusters defining the 12 phylogenetic groups of mimic insects. As a next step of our study, we again tried to take advantages of CNN algorithms to identify one of the most endangered bees in nature today - namely, the Rusty-patched bumble bee Bombus affinis. We collected 200 rusty-patched bumble bee images and 200 other bee images, again from the iNaturalist platform. These other bee images were collected from 20 different species from six distinct genera. We used mirror and rotate techniques to augment and increase the dataset to 3,200 images. Additionally, we generated grayscale version for each of these 3,200 images. At first, we trained and evaluated an EfficientNetV2B0-based classifier model to identify rusty-patched bumble bees from other bee images. We developed this model separately for both color and grayscale images. For the test dataset, the models could achieve around 90% and 92% accuracy respectively. From the CAM results, we were motivated by a natural marker - a “V”-shaped black spot on the thorax - that was always highlighted in our classification algorithms. Motivated by this, we tried an innovative method, which we call as anatomically inspired classification. We manually cropped the thorax portion from the images and trained another EfficientNetV2B0-based classifier to classify between the same classes, for both color and grayscale images. This time we could see a good classification performance boost achieving around 95% and 94% accuracy for color and grayscale images respectively. Next, we annotated the thorax portion on 400 images and trained an object detection and localization model to automatically detect the thorax on the bee images. Us- ing that model we automatically cropped all the thorax images and trained a new set of CNN networks to classify between rusty-patched bumble bees and other bees. Here also we found better classification accuracy than for the full-body classification. This demonstrates that learning from nature, and being able to look at the right anatomical components for classification indeed brings improved results. Later in this dissertation, we move towards mosquitoes - another insect important in nature from a public health perspective. Specifically, in this study, we attempted to employ CNN models to identify the sex of mosquito larvae - an important problem in certain mosquito control techniques. Within the broad ambit of mosquitoes, our focus is one of the deadliest mosquitoes today on the planet - the Anopheles stephensi mosquito, which is proving to be an unstoppable vector of malaria in Asia and more recently, Africa. Our partners in this study are researchers across multiple disciplines from organizations such as the CDC. For this study, we used Anopheles stephensi larvae images to identify the sex, as the 6th abdominal segments of males include two black spots representing the gonads. We collected 560 images from insectaries at the CDC and USF. The images were from L3 (3rd instar) and L4 (4th instar) stages. We attempted this solution in two different phases. In the first phase, we collected 362 images, augmented those data with mirror and rotate techniques, then trained a VGG16-based CNN model to classify between male and female images. We achieved around 90% accuracy in the validation dataset. But in the test dataset, the accuracy was a little poor, 74%. We applied CAM on the larvae images and found that the model was not focusing much on the gonads at the 6th abdominal stage. Instead, it was focusing more on the thorax and nearby hairs. Next, we collected some more larva image data from the lab at USF and increased our dataset to 560 images and trained the CNN model in the second phase. In this phase, we trained an Xception-based network and augmented images with eight different algorithms. Training in three steps we could achieve a validation accuracy of 96% and test accuracy of around 86%. We again employed the CAM algorithm, and this time also the model was focusing on the thorax and nearby hairs. Finally, we integrated our Anopheles stephensi larvae sex classifier model into a web-based portal using Firebase app and a Linode GPU instance to be used by the general public and mosquito control personnel around the world. A beta version of the system is open for anyone to use now. We believe that this dissertation demonstrates the innovative uses of AI for insect classification. The novel aspects of this study include investigating the capability of bee mimics to fool AI algorithms, creating anatomically inspired algorithms for classifying an endangered bee, and classifying the sex of a highly invasive and deadly mosquito at the larval stage. We hope that these results will help drive forward AI applications in insect biology

Insect Classification and Explainability from Image Data via Deep Learning Techniques

Since the dawn of the Industrial Revolution, humanity has always tried to make labor more efficient and automated, and this trend is only continuing in the modern digital age. With the advent of artificial intelligence (AI) techniques in the latter part of the 20th century, the speed and scale with which AI has been leveraged to automate tasks defy human imagination. Many people deeply entrenched in the technology field are genuinely intrigued and concerned about how AI may change many of the ways in which humans have been living for millennia. Only time will provide the answers. This dissertation is concerned with designing, deploying and validating computer vision algorithms (a branch of AI dealing with image data sets) for addressing a range of problems in insect classifications. The broader impacts of this dissertation lie in agriculture, species evolution, and public health. Bees are vital in agriculture and ecology. Food cultivation depends on bees to a great extent. Therefore ensuring a healthy environment for bees and the conservation of endangered bee species are a top priority. For conserving bees, their rapid identification in nature is necessary. To help entomologists in this regard, we design, deploy and validate convolutional neural network models to classify bees from image datasets. At first, we collected 6,332 original Research-Grade images of bees and other insects from the iNaturalist platform. We trained and evaluated our first model to classify bee images from other insect images using a VGG16-based CNN model. We could achieve more than 91% accuracy for that model. We trained another CNN model using ResNet-101-based model to classify be- tween bumble bee and other bee images, and for that model we could achieve almost 89% classification accuracy. We used cutting-edge Class Activation Maps (CAMs) to test our model’s capability to identify insect body pixels from the background pixels. We also collected 360 mimic insect images to evaluate how capable our model is for classifying between bees and bee mimics from morphological perspective. We discovered that bee mimics were most capable of fooling our models compared to the non-mimic insects. Additionally, we employed the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm on the mimic insect images. t-SNE is an unsupervised, non-linear technique for dimensionality reduction, primarily used for visualizing high-dimensional data. We employed this method in our study to understand the clustering of various mimic groups. We found out that t-SNE generated 12 distinct clusters defining the 12 phylogenetic groups of mimic insects. As a next step of our study, we again tried to take advantages of CNN algorithms to identify one of the most endangered bees in nature today - namely, the Rusty-patched bumble bee Bombus affinis. We collected 200 rusty-patched bumble bee images and 200 other bee images, again from the iNaturalist platform. These other bee images were collected from 20 different species from six distinct genera. We used mirror and rotate techniques to augment and increase the dataset to 3,200 images. Additionally, we generated grayscale version for each of these 3,200 images. At first, we trained and evaluated an EfficientNetV2B0-based classifier model to identify rusty-patched bumble bees from other bee images. We developed this model separately for both color and grayscale images. For the test dataset, the models could achieve around 90% and 92% accuracy respectively. From the CAM results, we were motivated by a natural marker - a “V”-shaped black spot on the thorax - that was always highlighted in our classification algorithms. Motivated by this, we tried an innovative method, which we call as anatomically inspired classification. We manually cropped the thorax portion from the images and trained another EfficientNetV2B0-based classifier to classify between the same classes, for both color and grayscale images. This time we could see a good classification performance boost achieving around 95% and 94% accuracy for color and grayscale images respectively. Next, we annotated the thorax portion on 400 images and trained an object detection and localization model to automatically detect the thorax on the bee images. Us- ing that model we automatically cropped all the thorax images and trained a new set of CNN networks to classify between rusty-patched bumble bees and other bees. Here also we found better classification accuracy than for the full-body classification. This demonstrates that learning from nature, and being able to look at the right anatomical components for classification indeed brings improved results. Later in this dissertation, we move towards mosquitoes - another insect important in nature from a public health perspective. Specifically, in this study, we attempted to employ CNN models to identify the sex of mosquito larvae - an important problem in certain mosquito control techniques. Within the broad ambit of mosquitoes, our focus is one of the deadliest mosquitoes today on the planet - the Anopheles stephensi mosquito, which is proving to be an unstoppable vector of malaria in Asia and more recently, Africa. Our partners in this study are researchers across multiple disciplines from organizations such as the CDC. For this study, we used Anopheles stephensi larvae images to identify the sex, as the 6th abdominal segments of males include two black spots representing the gonads. We collected 560 images from insectaries at the CDC and USF. The images were from L3 (3rd instar) and L4 (4th instar) stages. We attempted this solution in two different phases. In the first phase, we collected 362 images, augmented those data with mirror and rotate techniques, then trained a VGG16-based CNN model to classify between male and female images. We achieved around 90% accuracy in the validation dataset. But in the test dataset, the accuracy was a little poor, 74%. We applied CAM on the larvae images and found that the model was not focusing much on the gonads at the 6th abdominal stage. Instead, it was focusing more on the thorax and nearby hairs. Next, we collected some more larva image data from the lab at USF and increased our dataset to 560 images and trained the CNN model in the second phase. In this phase, we trained an Xception-based network and augmented images with eight different algorithms. Training in three steps we could achieve a validation accuracy of 96% and test accuracy of around 86%. We again employed the CAM algorithm, and this time also the model was focusing on the thorax and nearby hairs. Finally, we integrated our Anopheles stephensi larvae sex classifier model into a web-based portal using Firebase app and a Linode GPU instance to be used by the general public and mosquito control personnel around the world. A beta version of the system is open for anyone to use now. We believe that this dissertation demonstrates the innovative uses of AI for insect classification. The novel aspects of this study include investigating the capability of bee mimics to fool AI algorithms, creating anatomically inspired algorithms for classifying an endangered bee, and classifying the sex of a highly invasive and deadly mosquito at the larval stage. We hope that these results will help drive forward AI applications in insect biology

Methods and systems of authenticating of personal communications

A system for authenticating an individual\u27s location activity includes a mobile communications device connected to a network and in electronic communication with at least one other computer. The mobile communications device is configured to authenticate the individual\u27s presence at a location using biometric data entered by the individual. The mobile communications device has applications stored thereon to access location information for the mobile communications device using a GPS application stored on the mobile communications device and to access time information for the mobile communications device from a clock application stored on the mobile communications device. The mobile communications devices creates a digital signature that authenticates an individual\u27s location activity by storing an encrypted digital certificate comprising a hash calculation using the biometric data, a validation key generated by authenticating the biometric data, the location information, and the time information

Systems and methods for authenticating of personal communications cross reference to related applications

A system for authenticating an individual\u27s location activity includes a mobile communications device connected to a network and in electronic communication with at least one other computer. The mobile communications device is configured to authenticate the individual\u27s presence at a location using biometric data entered by the individual. The mobile communications device has applications stored thereon to access location information for the mobile communications device using a GPS application stored on the mobile communications device and to access time information for the mobile communications device from a clock application stored on the mobile communications device. The mobile communications devices creates a digital signature that authenticates an individual\u27s location activity by storing an encrypted digital certificate comprising a hash calculation using the biometric data, a validation key generated by authenticating the biometric data, the location information, and the time information

Methods and systems of authenticating of personal communications

A system for authenticating an individual\u27s location activity includes a mobile communications device connected to a network and in electronic communication with at least one other computer. The mobile communications device is configured to authenticate the individual\u27s presence at a location using biometric data entered by the individual. The mobile communications device has applications stored thereon to access location information for the mobile communications device using a GPS application stored on the mobile communications device and to access time information for the mobile communications device from a clock application stored on the mobile communications device. The mobile communications devices creates a digital signature that authenticates an individual\u27s location activity by storing an encrypted digital certificate comprising a hash calculation using the biometric data, a validation key generated by authenticating the biometric data, the location information, and the time information

Comparing Machine Learning Techniques for Detecting Chronic Kidney Disease in Early Stage

In medical care, side effect trial and error processes are utilized for the discovery of hidden reasons for ailments and the determination of conditions. In our exploration, we used a crossbreed strategy to refine our optimal model, improving the Pearson relationship for highlight choice purposes. The underlying stage included the choice of ideal models through a careful survey of the current writing. Hence, our proposed half-and-half model incorporated a blend of these models. The base classifiers utilized included XGBoost, Arbitrary Woods, Strategic Relapse, AdaBoost, and the Crossover model classifiers, while the Meta classifier was the Irregular Timberland classifier. The essential target of this examination was to evaluate the best AI grouping techniques and decide the best classifier concerning accuracy. This approach resolved the issue of overfitting and accomplished the most elevated level of exactness. The essential focal point of the assessment was precision, and we introduced a far-reaching examination of the significant writing in even configuration. To carry out our methodology, we used four top-performing AI models and fostered another model named "half and half," utilizing the UCI Persistent Kidney Disappointment dataset for prescient purposes. In our experiment, we found out that the AI model XGBoost classifier gains almost 94% accuracy, a random forest gains 93% accuracy, Logistic Regression about 90% accuracy, AdaBoost gains 91% accuracy, and our proposed new model named hybrid gains the highest 95% accuracy, and performance of Hybrid model is best on this equivalent dataset. Various noticeable AI models have been utilized to foresee the event of persistent kidney disappointment (CKF). These models incorporate Naïve Bayes, Random Forest, Decision Tree, Support Vector Machine, K-nearest neighbor, LDA (Linear Discriminant Analysis), GB (Gradient Boosting), and neural networks. In our examination, we explicitly used XGBoost, AdaBoost, Logistic Regression, Random Forest, and Hybrid models with the equivalent dataset of highlights to analyze their accuracy scores

Revolutionizing Retail: A Hybrid Machine Learning Approach for Precision Demand Forecasting and Strategic Decision-Making in Global Commerce

A thorough comparison of several machine learning methods is provided in this paper, including gradient boosting, AdaBoost, Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and a unique hybrid framework (RF-XGBoost-LR). The assessment investigates their efficacy in real-time sales data analysis using key performance metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R2 score. The study introduces the hybrid model RF-XGBoost-LR, leveraging both bagging and boosting methodologies to address the limitations of individual models. Notably, Random Forest and XGBoost are scrutinized for their strengths and weaknesses, with the hybrid model strategically combining their merits. Results demonstrate the superior performance of the proposed hybrid model in terms of accuracy and robustness, showcasing potential applications in supply chain studies and demand forecasting. The findings highlight the significance of industry-specific customization and emphasize the potential for improved decision-making, marketing strategies, inventory management, and customer satisfaction through precise demand forecasting