A common heart condition that plagues small dogs can be picked up by AI, experts have found. Mitral valve disease regularly affects breeds such as King Charles spaniels, miniature poodles, Pomeranians and chihuahuas. It occurs when one of the heart's valves becomes distorted and leaky. It can progress to become fatal if not treated early on. A research team, led by the University of Cambridge, adapted an algorithm originally designed for humans and found it could automatically detect and grade heart murmurs in dogs - one of the main indicators of the disease.

In summary, implementing a solution for AI-powered anomaly detection in log data for improved troubleshooting in DevOps requires a well-structured plan, a good understanding of the use case, and a good knowledge of the different AI-based anomaly detection techniques. With proper planning, implementation, and maintenance, AI-powered anomaly detection can be a valuable asset for any DevOps team.

ConsultDSAI (C-DSAI) uses Artificial intelligence (AI) on CCTV video analytics as a technology that utilises advanced algorithms and machine learning techniques to analyze video footage captured by CCTV cameras. The technology is designed to automatically detect and identify objects, people, and events in real-time, and it can be used for a wide range of applications in various industries. One of the key uses of AI-based CCTV video analytics is in security and surveillance. The technology can be used to automatically detect and alert security personnel of potential security threats, such as intruders or loiterers. It can also be used to track the movement of people and vehicles, which can help to identify suspicious behavior or potential criminal activity.

With a 73 percent positive predictive value, the AI technique accurately identified 80 percent of the instances of plaque erosion. Researchers have created a brand-new artificial intelligence (AI) technique that uses optical coherence tomography (OCT) images to automatically detect plaque erosion in the arteries of the heart. Monitoring arterial plaque is crucial because, if it disintegrates, it may obstruct blood flow to the heart, triggering a heart attack or other dangerous problems. "If cholesterol plaque lining arteries starts to erode it can lead to a sudden reduction in blood flow to the heart known as acute coronary syndrome, which requires urgent treatment," said research team leader Zhao Wang from the University of Electronic Science and Technology of China. "Our new method could help improve the clinical diagnosis of plaque erosion and be used to develop new treatments for patients with heart disease."

Sounds provide important information about how well a machine is running. ETH researchers have now developed a new machine learning method that automatically detects whether a machine is "healthy" or requires maintenance. Whether railway wheels or generators in a power plant, whether pumps or valves--they all make sounds. For trained ears, these noises even have a meaning: devices, machines, equipment or rolling stock sound differently when they are functioning properly compared to when they have a defect or fault. The sounds they make, thus, give professionals useful clues as to whether a machine is in a good--or "healthy"--condition, or whether it will soon require maintenance or urgent repair.

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both types and targets of hateful comments, and 2) experimenting with machine learning, including Logistic Regression, Decision Tree, Random Forest, Adaboost, and Linear SVM, to generate a multiclass, multilabel classification model that automatically detects and categorizes hateful comments in the context of online news media. We find that the best performing model is Linear SVM, with an average F1 score of 0.79 using TF-IDF features. We validate the model by testing its predictive ability, and, relatedly, provide insights on distinct types of hate speech taking place on social media.

This paper introduces k-splits, an improved hierarchical algorithm based on k-means to cluster data without prior knowledge of the number of clusters. K-splits starts from a small number of clusters and uses the most significant data distribution axis to split these clusters incrementally into better fits if needed. Accuracy and speed are two main advantages of the proposed method. We experiment on six synthetic benchmark datasets plus two real-world datasets MNIST and Fashion-MNIST, to prove that our algorithm has excellent accuracy in finding the correct number of clusters under different conditions. We also show that k-splits is faster than similar methods and can even be faster than the standard k-means in lower dimensions. Finally, we suggest using k-splits to uncover the exact position of centroids and then input them as initial points to the k-means algorithm to fine-tune the results.

IMAGE: Deep Learning detects virus infected cells and predicts acute, severe infections. In most cases, this does not lead to the production of new virus particles, as the viruses are suppressed by the immune system. However, adenoviruses and herpes viruses can cause persistent infections that the immune system is unable to completely suppress and that produce viral particles for years. These same viruses can also cause sudden, violent infections where affected cells release large amounts of viruses, such that the infection spreads rapidly. This can lead to serious acute diseases of the lungs or nervous system. The research group of Urs Greber, Professor at the Department of Molecular Life Sciences at the University of Zurich (UZH), has now shown for the first time that a machine-learning algorithm can recognize the cells infected with herpes or adenoviruses based solely on the fluorescence of the cell nucleus.

When viruses infect a cell, changes in the cell nucleus occur, and these can be observed through fluorescence microscopy. Using fluoresence images made in live cells, researchers at the University of Zurich have trained an artificial neural network to reliably recognize cells that are infected by adenoviruses or herpes viruses. The procedure also identifies severe acute infections at an early stage. In most cases, this does not lead to the production of new virus particles, as the viruses are suppressed by the immune system. However, adenoviruses and herpes viruses can cause persistent infections that the immune system is unable to keep completely in check and that produce viral particles for years. These same viruses can also cause sudden, violent infections where affected cells release large amounts of viruses, such that the infection spreads rapidly.

Juniper Networks has further expanded several AI solutions within its Mist portfolio. The improvements, such as the Mist WAN Assurance tool and an update to the Marvis AI engine, will make it easier for businesses to automatically detect network problems and resolve connectivity issues. Juniper Networks is strongly committing to AI for detecting problems within corporate networks and the underlying connections. The portfolio of Mist, which was acquired last year, has played a key role. With the tools of Mist, companies are able to automatically detect and solve network problems in (wireless) networks.