Milk is a highly important consumer for Americans and the health of the cows' teats directly affects the quality of the milk. Traditionally, veterinarians manually assessed teat health by visually inspecting teat-end hyperkeratosis during the milking process which is limited in time, usually only tens of seconds, and weakens the accuracy of the health assessment of cows' teats. Convolutional neural networks (CNNs) have been used for cows' teat-end health assessment. However, there are challenges in using CNNs for cows' teat-end health assessment, such as complex environments, changing positions and postures of cows' teats, and difficulty in identifying cows' teats from images. To address these challenges, this paper proposes a cows' teats self-attention residual convolutional neural network (CTSAR-CNN) model that combines residual connectivity and self-attention mechanisms to assist commercial farms in the health assessment of cows' teats by classifying the magnitude of teat-end hyperkeratosis using digital images. The results showed that upon integrating residual connectivity and self-attention mechanisms, the accuracy of CTSAR-CNN has been improved. This research illustrates that CTSAR-CNN can be more adaptable and speedy to assist veterinarians in assessing the health of cows' teats and ultimately benefit the dairy industry.

Honey bees pollinate about one-third of the world's food supply, but bee colonies have alarmingly declined by nearly 40% over the past decade due to several factors, including pesticides and pests. Traditional methods for monitoring beehives, such as human inspection, are subjective, disruptive, and time-consuming. To overcome these limitations, artificial intelligence has been used to assess beehive health. However, previous studies have lacked an end-to-end solution and primarily relied on data from a single source, either bee images or sounds. This study introduces a comprehensive system consisting of bee object detection and health evaluation. Additionally, it utilized a combination of visual and audio signals to analyze bee behaviors. An Attention-based Multimodal Neural Network (AMNN) was developed to adaptively focus on key features from each type of signal for accurate bee health assessment. The AMNN achieved an overall accuracy of 92.61%, surpassing eight existing single-signal Convolutional Neural Networks and Recurrent Neural Networks. It outperformed the best image-based model by 32.51% and the top sound-based model by 13.98% while maintaining efficient processing times. Furthermore, it improved prediction robustness, attaining an F1-score higher than 90% across all four evaluated health conditions. The study also shows that audio signals are more reliable than images for assessing bee health. By seamlessly integrating AMNN with image and sound data in a comprehensive bee health monitoring system, this approach provides a more efficient and non-invasive solution for the early detection of bee diseases and the preservation of bee colonies.

One of the primary challenges faced by researchers and clinicians seeking to study mental health is that direct observation of indicators of mental health issues can be challenging, as a diagnosis often relies on either self-reporting of specific feelings or actions, or direct observation of a subject (which can be difficult due to time and cost considerations). That is why there has been a specific focus over the past two decades on deploying technology to help human clinicians identify and assess mental health issues. Between 2000 and 2019, 54 academic papers focused on the development of machine learning systems to help diagnose and address mental health issues were published, according to a 2020 article published in ACM Transactions on Computer-Human Interaction. Of the 54 papers, 40 focused on the development of a machine learning (ML) model based on specific data as their main research contribution, while seven were proposals of specific concepts, data methods, models, or systems, and three applied existing ML algorithms to better understand and assess mental health, or improve the communication of mental health providers. A few of the papers described the conduct of empirical studies of an end-to-end ML system or assessed the quality of ML predictions, while one paper specifically discusses design implications for user-centric, deployable ML systems.

Cognitive impairment has become epidemic in older adult population. The recent advent of tiny wearable and ambient devices, a.k.a Internet of Things (IoT) provides ample platforms for continuous functional and cognitive health assessment of older adults. In this paper, we design, implement and evaluate AutoCogniSys, a context-aware automated cognitive health assessment system, combining the sensing powers of wearable physiological (Electrodermal Activity, Photoplethysmography) and physical (Accelerometer, Object) sensors in conjunction with ambient sensors. We design appropriate signal processing and machine learning techniques, and develop an automatic cognitive health assessment system in a natural older adults living environment. We validate our approaches using two datasets: (i) a naturalistic sensor data streams related to Activities of Daily Living and mental arousal of 22 older adults recruited in a retirement community center, individually living in their own apartments using a customized inexpensive IoT system (IRB #HP-00064387) and (ii) a publicly available dataset for emotion detection. The performance of AutoCogniSys attests max. 93\% of accuracy in assessing cognitive health of older adults.

Editor's Note: The DigitalGlobe 2018 Australia Sustainability Hackathon aimed to address Australia's most conflicting issues surrounding mining, agriculture and environmental sustainability using machine learning and satellite imagery. This blog post is written by the winning team from the agriculture category. The forestry industry can benefit from multi-spectral, high-resolution satellite imagery in a number of ways, particularly for inventory components, such as tree stocking assessment, Leaf Area Index (LAI) estimation, volume survey and health analysis at stand and individual tree level. These could be measured in direct way through sampling. However, direct methods are very labour intensive, costly and subject to sampling error. Image-based remote sensing and advanced artificial intelligence (AI) technology offer an affordable solution to this problem.

Last June, a simple question changed my life: "Hey Bryn, what do you know about whales?" Of course like most people, my answer was "Not much," but that marked the beginning of an important project to help track the health of whale populations by using machine learning to analyze video from drones. Parley for the Oceans introduced me and my colleagues Ted Willke and Javier Turek to Dr. Iain Kerr of Ocean Alliance, and we started talking about how machine learning could help make marine biologists' lives easier and help protect the whales, using the video from Dr. Kerr's SnotBot drones. Dr. Kerr started the SnotBot program because in the not-so-distant past, when people wanted to understand the health of a whale, or get its DNA, the only way to do this was to shoot it with a crossbow with a specially prepared bolt with a string on it, that would only go a couple of inches into the (remember, bus-size) body. The bolt would then be reeled in and the sample could be extracted from it.

After six years in incubator development, KBS Albion and start-up Ada Health have released a artificial intelligence (AI) engine and app to provide health assessments in response to real-time patient symptom data. Berlin-based health tech startup Ada Health approached London-based KBS Albion to help develop this app. "Almost all our work is analogous to the work we did for Ada, blending business innovation with brand and product design," says Adam Lawrenson, ECD, KBS Albion. KBS and Ada had to conduct a "huge amount" of upfront research to understand consumer responses to digital health globally. "To do this, we had to design a specific methodology," with help from University College London, says Lawrenson. "We then had to create a name, brand and identity that would resonate globally.

Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring.The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-one-person-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features.Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring.These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.