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Anomaly Detection


Heartbeat Anomaly Detection

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According to a report of WHO, around 17.9 million people die each year due to Cardiovascular Diseases.Over the years it has been found that these deaths can be prevented if the diseases are diagnosed at an early stage and even the disease can be cured. Artificial Intelligence has been applied in various fields and one of them is AI for healthcare.We have seen AI practitioners coming up with solution for various disease diagnosis such as Cancer Detection, Detection of Diabetic Retinopathy and much more.The techniques used in these detections mostly involve Deep Learning. So, by combining our knowledge of deep learning and with its integration Iot we can develop a smart digital-stethoscope which can help in diagnosing anomalies in heartbeat in real-time and can help in classifying Cardio-diseases. While working in cAInvas one of its key features is UseCases Gallary.When working on any of its UseCases you don't have to look for data manually.As they have the feature to import your dataset to your workspace when you work on them.To load the data we just have to enter the following commands: As with all unstructured data formats, audio data has a couple of preprocessing steps which have to be followed before it is presented for analysis. Another way of representing audio data is by converting it into a different domain of data representation, namely the frequency domain.


Passive Income with Device Embedded Artificial Intelligence

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The artificial intelligence for embedded applications is always profitable, for example, using Face-id to authorize access to the restricted areas. There are also many possibilities like face recognition, voice control, anomaly detection, and artificial intelligence for other tasks.


Microsoft AI Open-Sources 'SynapseML' For Developing Scalable Machine Learning Pipelines

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Microsoft has announced the release of SynapseML, an open-source library that simplifies and speeds up the creation of machine learning (ML) pipelines. SynapseML can be used for building scalable and intelligent systems to solve various types of challenges, including anomaly detection, computer vision, deep learning, form and face recognition, Gradient boosting, microservice orchestration, model interpretability, reinforcement learning, and personalization, search and retrieval, speech processing, text analytics, and translation. SynapseML is a powerful platform for building production-ready distributed machine learning pipelines. It bridges the gap between several existing ML frameworks and Microsoft algorithms in order to create one scalable API that works across Python, R Language-based platforms like Scala or Java. In order to build a machine learning pipeline, you need more than just coding skills.


Anomaly Detection on Servo Drives

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Given servo feedback information, such as torque, velocity, acceleration, and power, one can predict the likelihood of an issue with the servo. By knowing when the servo is likely to fail, one can reduce downtime and prevent potential damage to the system from a faulty servo. By applying a simple Gaussian probability density function to a set of trained features, the classification of an anomalous servo can be determined with little computational cost. The figure illustrates how the variance affects the probability density function magnitude and coverage along the feature axis. A higher variance value indicates the data are more spread out, and a lower variance value indicates the data are close in value, with a variance of zero meaning all of the data are identical.


What is the difference between outlier detection and data drift detection?

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As we build Evidently, an open-source tool to monitor ML models, we spend a lot of time answering questions about ML in production, monitoring, and system design. Some questions are repeated over and over! We decided to write some things down in the format of machine learning Q&A. Expect visual explainers, both high-level and deep dives. And if you want to ask your question, welcome to our community server!


Artificial Intelligence Spots Anomalies in Medical Images

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Scientists from Skoltech, Philips Research, and Goethe University Frankfurt have trained a neural network to detect anomalies in medical images to assist physicians in sifting through countless scans in search of pathologies. Reported in IEEE Access, the new method is adapted to the nature of medical imaging and is more successful in spotting abnormalities than general-purpose solutions. Image anomaly detection is a task that comes up in data analysis in many industries. Medical scans, however, pose a particular challenge. It is way easier for algorithms to find, say, a car with a flat tire or a broken windshield in a series of car pictures than to tell which of the X-rays show early signs of pathology in the lungs, like the onset of COVID-19 pneumonia.


Time Series Analysis on Smart Home IOT with Weather data

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This paper proposes an efficient way to reduce usage or predict the future needs of appliances or power consumption by using the weather information data . Over the last few years, activity recognition in the smart home has become an active research area due to the wide range of human centric-applications. IoT brings together everything at home under one umbrella which has the potential to monitor and remote control such as air conditioning, alarm system, lighting, heating, ventilation, telephone system, tv, etc. To enhance our comfort and security with low energy consumption and energy management is one of the IoT use cases with which energy being sent out or consumed can be monitored. One can monitor each of the IoT appliances and how much power each of the devices is consuming, and easily switch between energy-efficient appliances across the day. In this case study we are going to focus on predicting the future energy consumption with the past data so that we can manage our day to day usage of appliances at home.


HPE Ezmeral ML Ops Recognized by Gartner

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On September 1, 2021 Gartner published their 2021 "Market Guide for AI Trust, Risk and Security Management". Per Gartner, "This Market Guide identifies new capabilities that data and analytics leaders must have to ensure model reliability, trustworthiness and security, and presents representative vendors who implement these functions."1 At HPE, we believe HPE Ezmeral ML Ops was recognized for the advantages our solution provides to our customers. As such, we're proud to announce that Gartner listed HPE Ezmeral ML Ops as a Representative ModelOps Vendor in the 2021 "Market Guide for AI Trust, Risk and Security Management." Gartner defines the AI Trust, Risk and Security Management (TRiSM) market as being made up of multiple software segments.


Artificial intelligence spots anomalies in medical images

#artificialintelligence

Scientists from Skoltech, Philips Research, and Goethe University Frankfurt have trained a neural network to detect anomalies in medical images to assist physicians in sifting through countless scans in search of pathologies. Reported in IEEE Access, the new method is adapted to the nature of medical imaging and is more successful in spotting abnormalities than general-purpose solutions. Image anomaly detection is a task that comes up in data analysis in many industries. Medical scans, however, pose a particular challenge. It is way easier for algorithms to find, say, a car with a flat tire or a broken windshield in a series of car pictures than to tell which of the X-rays show early signs of pathology in the lungs, like the onset of COVID-19 pneumonia.


Popular Artificial Intelligence APIs to Explore Today

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"Just use AI!" While you may have heard this before, using artificial intelligence can seem like a lot of work. But it doesn't have to be, and there are many AI APIs out there ready for you to leverage. Check out some of them in Postman's latest featured list, Artificial Intelligence APIs, then get started straight away by forking any or all of these popular APIs to your own workspace. OpenAI is a non-profit AI research company whose goal is to advance digital intelligence. They've been widely talked about recently when they announced Codex, an AI that translates natural language to code.