Mediaboss Marketing

In recent years, the field of machine learning has experienced exponential growth, with applications in diverse domains such as healthcare, finance, and automation. One of the most promising areas of development is TinyML, which brings machine learning to resource-constrained devices. We will explore the concept of TinyML, its applications, and its potential to revolutionize industries by offering intelligent solutions on a small scale.

TinyML is an emerging area in machine learning that focuses on the development of algorithms and models that can run on low-power, memory-constrained devices. The term TinyML is derived from the words tiny and machine learning, reflecting the goal of enabling ML capabilities on small-scale hardware. By designing efficient models that can operate in such environments, TinyML has the potential to bring artificial intelligence (AI) to billions of devices that were previously unable to support it.

As the number of IoT devices skyrockets, so does the need for intelligent, localized decision-making. Traditional cloud-based approaches to AI can be limited by factors such as latency, bandwidth, and privacy concerns. In contrast, TinyML enables on-device intelligence, allowing for faster, more efficient decision-making without the need for constant communication with the cloud.

Furthermore, the resource constraints of small devices necessitate efficient algorithms that consume minimal power and memory. TinyML addresses these challenges by optimizing models and leveraging specialized hardware to achieve impressive results, even with limited resources.

Several technologies and advancements have facilitated the growth of TinyML:

The potential applications of TinyML are vast, spanning various industries:

Wildlife Conservation: TinyML-enabled devices can help track and monitor endangered species, allowing for more effective conservation efforts and data collection.

While TinyML presents immense potential, it also faces several challenges that must be addressed to fully realize its capabilities:

Conclusion

TinyML is an exciting and rapidly growing field that promises to bring the power of machine learning to billions of small, resource-constrained devices. By optimizing ML models and leveraging cutting-edge hardware and software technologies, TinyML has the potential to revolutionize industries and improve the lives of people worldwide. As researchers and engineers continue to innovate and overcome the challenges facing TinyML, the future of this technology looks incredibly promising.

More here:
TinyML: The Future of Machine Learning on a Minuscule Scale - Unite.AI

Amanda Morris, a press release writer at Northwestern University, describes an important astronomical effect in terms entertaining enough to be worth reposting here: The cosmos would look a lot better if the Earths atmosphere wasnt photobombing it all the time. Thats certainly one way to describe the airs effect on astronomical observations, and its annoying enough to astronomers that they constantly have to correct for distortions from the Earths atmosphere, even at the most advanced observatories at the highest altitudes. Now a team from Northwestern and Tsinghua Universities have developed an AI-based tool to allow astronomers to automatically remove the blurring effect of the Earths atmosphere from pictures taken for their research.

Dr. Emma Alexander and her student Tianao Li developed the technique in the Bio Inspired Vision Lab, a part of Northwesterns engineering school, though Li was a visiting undergraduate from Tsinghua University in Beijing. Dr. Alexander realized that accuracy was an essential part of scientific imaging, but astronomers had a tough time as their work was constantly being photobombed, as Ms. Morris put it, by the atmosphere.

Weve spent plenty of time in articles discussing the difficulties of seeing and the distortion effect that air brings to astronomical pictures, so we wont rehash that here. But its worth looking at the details of this new technique, which could save astronomers significant amounts of time either chasing bad data or deblurring their own images.

Using a technique known as optimization and a more commonly known AI technique called deep learning, the researchers developed an algorithm that could successfully deblur an image with less error than both classic and modern methods. This resulted in crisper images that were both better scientifically but also more visually appealing. However, Dr. Alexander notes that was simply a happy side effect of their work to try to improve the science.

To train and test their algorithm, the team worked with simulated data developed by the team responsible for the upcoming Vera C Rubin observatory, which is set to be one of the worlds most powerful ground-based telescopes when it begins operations next year. Utilizing the simulated data as a training set allowed the Northwestern researchers to get a head start on testing their algorithm ahead of the observatorys opening but also tweak it to make it well-suited for use with what will arguably be one of the most important observatories of the coming decades.

Besides that usefulness, the team also decided to make the project open-source. They have released a version on Github, so programmers and astronomers alike can pull the code, tweak it to their own specific needs, and even contribute to a set of tutorials the team developed that could be utilized on almost any data from a ground-based telescope. One of the beauties of algorithms like that is they can easily remove photobombers even if they are less substantive than most.

Learn More:Northwestern AI algorithm unblurs the cosmosLi & Alexander Galaxy Image Deconvolution for Weak Gravitational Lensing with Unrolled Plug-and-Play ADMMUT Telescopes Laser Pointer Clarifies Blurry SkiesUT A Supercomputer Gives Better Focus to Blurry Radio Images

Lead Image: Different phases of deblurring the algorithm applies to a galaxy. Original image is in the top left, final image is the bottom right.Credit Li & Alexander

Like Loading...

Continued here:
Machine Learning Tidies Up the Cosmos - Universe Today

In todays world, all organizations want to use Machine learning to analyze the data they generate daily from the users. With the help of a machine or deep learning algorithms, they can analyze the data. Afterwards, they can make the prediction of testing data in the production environment. But suppose we start following the mentioned process. In that case, we may face problems such as building and training machine learning models since this is time-consuming and requires expertise in domains like programming, statistics, data science, etc.

So, to overcome such challenges, Automated Machine Learning (AutoML) comes into the picture, which emerged as one of the most popular solutions that can automate many aspects of the machine learning pipeline. So, in this article, we will discuss AutoML with Python through a real-life case study on the Prediction of heart disease.

We can easily observe that problem-related to the heart are the major cause of death worldwide. The only way to reduce such types of impact is to detect the disease early with some of the automated methods so that less time will be consumed there and, after that, take some prevention measures to reduce its effect. So, by keeping this problem in mind, we will explore one of the datasets related to medical patient records to build a machine-learning model from which we can predict the likelihood or probability of a patient with heart disease. This type of solution can easily be applied in hospitals to check so doctors can provide some treatments as soon as possible.

The complete model pipeline we followed in this case study is shown below.

Step-1: Before starting to implement, let's import the required libraries, including NumPy for matrix manipulation, Pandas for data analysis, and Matplotlib for Data Visualization.

Step-2: After importing all the required libraries in the above step, we will now try to load our dataset while utilizing the Pandas data frame to store that in an optimized manner, as they are much more efficient in terms of both space and time complexity compared to other data structures like a linked list, arrays, trees, etc.

Further, we can perform Data preprocessing to prepare the data for further modelling and generalization. To download the dataset which we are using here, you can easily refer to the link.

Step-3: After preparing the data for the machine learning model, we will use one of the famous automated machine learning libraries called H2O.ai, which helps us create and train the model.

The main benefit of this platform is that it provides high-level API from which we can easily automate many aspects of the pipeline, including Feature Engineering, Model selection, Data Cleaning, Hyperparameter Tuning, etc., which drastically the time required to train the machine learning model for any of the data science projects.

Step-4: Now, to build the model, we will use the API of the H2O.ai library, and to use this, we have to specify the type of problem, whether it is a regression problem or a classification problem, or some other type with the target variable mentioned. Then, automatically this library chooses the best model for the given problem statement, including algorithms such as Support Vector Machines, Decision Trees, Deep neural networks, etc.

Step-5: After finalizing the best model from a set of algorithms, the most critical task is fine-tuning our model based on the hyperparameters involved. This tuning process involved many techniques, such as Grid-search Cross Validation, etc., which allowed for finding the best set of hyperparameters for the given problem.

Step-6: Now, the final task is to check the models performance, using evaluation metrics such as Confusion matrix, Precision, recall, etc., for classification problems and MSE, MAE, RMSE, and R-square, for regression models so that we can find some inference of our models working in the production environment.

Step-7: Finally, we will plot the ROC curve which shows the graph between false positive rate (which means that our model is predicting the wrong result compare to the actual and model predicts the positive class, where it belongs to the negative class), and false negative rate(which means that our model is predicting the wrong result compare to the actual and model predicts the negative class, where it belongs to the positive class) and also print the confusion matrix and eventually our model prediction and evaluation on the test data is completed. Then we will shut down our H2O.

You can access the notebook of the mentioned code from here.

To conclude this article, we have explored the different aspects of one of the most popular platforms which automate the whole process of machine learning or data science tasks, through which we can easily create and train machine learning models using the python programming language and also we have covered one of the famous case studies of heart disease prediction, which enhances the understanding on how to use such platforms effectively. Using such platforms, machine learning pipelines can be easily optimized, saving the engineers time in the organization and reducing system latency and resource utilization such as GPU and CPU cores, which are easily accessible to a large audience.Aryan Garg is a B.Tech. Electrical Engineering student, currently in the final year of his undergrad. His interest lies in the field of Web Development and Machine Learning. He have pursued this interest and am eager to work more in these directions.

Link:
Automated Machine Learning with Python: A Case Study - KDnuggets

This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

by Beijing Institute of Technology Press Co., Ltd

Scientists from Osaka University designed a cyborg cockroach and optimized its movement by utilizing machine learning-based automatic stimulation. Credit: Cyborg and Bionic Systems

Have you ever wondered why some insects like cockroaches prefer to stay or decrease movement in darkness? Some may tell you it's called photophobia, a habit deeply coded in their genes. A further question would be whether we can correct this habit of cockroaches, that is, moving in the darkness just as they move in bright backgrounds.

Scientists from Osaka University may have answered this question by converting a cockroach into a cyborg. They published their research in the journal Cyborg and Bionic Systems.

With millions of years of evolution, natural animals are endowed with outstanding capabilities to survive and thrive in hostile environments. In recent years, these animals have inspired roboticists to develop automatic machines to recapitulate part of these extinguished capabilities, that is, biologically inspired biomimetic robots.

An alternative to this path is to directly build controllable machines on these natural animals by implanting stimulation electrodes into their brains or peripheral nervous system to control their movement and even see what they see, so-called cyborgs. Among these studies, cyborg insects are attracting ever-increasing attention for their availability, simpler neuro-muscular pathways, and easier operation to intrusively stimulate their peripheral nervous system or muscles.

Cockroaches have marvelous locomotion ability, which significantly outperforms any biomimetic robots of similar size. Therefore, cyborg cockroaches equipped with such agile locomotion are suitable for search and rescue missions in unknown and unstructured environments that traditional robots can hardly access.

"Cockroaches prefer to stay in the darkened, narrow areas over the bright, spacious areas. Moreover, they tend to be active in the hotter environment," explained study author Keisuke Morishima, a roboticist from Department of Mechanical Engineering, Osaka University, "These natural behaviors will hinder the cockroaches to be utilized in unknown and under-rubble environments for search and rescue applications. It will be difficult to apply a mini live stream camera attached to them in a dark or without light areas for real-time monitoring purposes."

"This study aims to optimize cyborg cockroach movement performance," said Morishima. To this end, they proposed a machine learning-based approach that automatically detects the motion state of this cyborg cockroach via IMU measurements. If the cockroach stops or freezes in darkness or cooler environment, electrical stimulation would be applied to their brain to make it move.

"With this online detector, the stimulation is minimized to prevent the cockroaches from fatigue due to too many stimulations," said Mochammad Ariyanto, Morishima's colleague from Department of Mechanical Engineering, Osaka University.

This idea of restraining electrical stimulation to necessary circumstances, which is determined by AI algorithms via onboard measurements, is intuitively promising. "We don't have to control the cyborg like controlling a robot. They can have some extent of autonomy, which is the basis of their agile locomotion. For example, in a rescue scenario, we only need to stimulate the cockroach to turn its direction when it's walking the wrong way or move when it stops unexpectedly," said Morishima.

"Equipped with such a system, the cyborg successfully increased its average search rate and traveled distance up to 68% and 70%, respectively, while the stop time was reduced by 78%," said the study authors. "We have proven that it's feasible to apply electrical stimulation on the cockroach's cerci; it can overcome its innate habit, for example, increase movement in dark and cold environments where it normally decreases its locomotion."

"In this study, cerci were stimulated to trigger the free-walking motion of the Madagascar hissing cockroach (MHC)."

More information: Mochammad Ariyanto et al, Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning, Cyborg and Bionic Systems (2023). DOI: 10.34133/cbsystems.0012

Provided by Beijing Institute of Technology Press Co., Ltd

More:
Exploring movement optimization for a cyborg cockroach with machine learning - Tech Xplore

Type 2 diabetes is a chronic disease that affects millions of people around the world, leading to long-term health complications such as heart disease, nerve damage, and kidney failure. The early diagnosis of type 2 diabetes is critical in order to prevent these complications, and machine learning is helping to revolutionize the way this disease is diagnosed.

Machine learning algorithms use patterns in data to make predictions and decisions, and this same capability can be applied to the analysis of medical data in order to improve the diagnosis of type 2 diabetes. One of the key ways that machine learning is improving diabetes diagnosis is through the use of predictive algorithms. These algorithms can use data from patient histories, such as age, BMI, blood pressure, and blood glucose levels, to predict the likelihood of a patient developing type 2 diabetes. This can help healthcare providers to identify patients who are at high risk of developing the disease and take early action to prevent it.

Another way that machine learning is improving diabetes diagnosis is through the use of advanced imaging techniques. Machine learning algorithms can be used to analyze images of the retina and identify early signs of diabetic retinopathy, a condition that often develops in people with type 2 diabetes and can cause vision loss. In addition, machine learning algorithms can be used to analyze images of the pancreas and identify early signs of insulin resistance, which is a hallmark of type 2 diabetes.

Machine learning algorithms can also be used to analyze large datasets from electronic health records in order to identify patterns and markers that are associated with type 2 diabetes. For example, machine learning algorithms can be used to analyze the medical histories of patients and identify risk factors such as family history, age, and lifestyle habits that may increase the likelihood of developing type 2 diabetes. By analyzing large datasets in this way, machine learning algorithms can help healthcare providers to identify patients who are at high risk of developing the disease, and take early action to prevent it.

One of the key benefits of machine learning in diabetes diagnosis is the ability to quickly and accurately analyze large amounts of data. Machine learning algorithms can process data much faster and more accurately than humans, and this can help healthcare providers to make more informed decisions about patient care. Additionally, machine learning algorithms can be trained to recognize patterns and markers that are specific to type 2 diabetes, which can improve the accuracy of diagnoses and reduce the number of false positives.

In conclusion, machine learning is playing a critical role in the diagnosis of type 2 diabetes. With its ability to analyze large datasets, identify patterns and markers associated with the disease, and predict the likelihood of a patient developing type 2 diabetes, machine learning is helping to revolutionize the way this disease is diagnosed. By improving the accuracy and speed of diagnoses, machine learning is helping to ensure that patients receive the care they need as early as possible, and prevent the long-term health complications associated with this disease.

References:

1. Machine learning in healthcare: past, present and future R. Andrew Shah, M.D., David W. Orellana, M.D., M.S. (2018)

2. Using Machine Learning for Early Diagnosis of Type 2 Diabetes Ahmed Al-Emadi, M.D., (2020)

3. The impact of machine learning on healthcare: A review of the literature Joshua D. Bloom, M.D., Ph.D., et al. (2019)

*This article was produced with the assistance of artificial intelligence. Please always check and confirm with your own sources, and always consult with your healthcare professional when seeking medical treatment

More:
How Machine Learning Plays a Key Role in Diagnosing Type 2 ... - Diabetes In Control