Mediaboss Marketing

Apart from many areas in our lives, hybrid machine learning techniques can help us with effective heart disease prediction. So how can the technology of our time, machine learning, be used to improve the quality and length of human life?

Heart disease stands as one of the foremost global causes of mortality today, presenting a critical challenge in clinical data analysis. Leveraging hybrid machine learning techniques, a field highly effective at processing vast healthcare data volumes is increasingly promising in effective heart disease prediction.

According to the World Health Organization, heart disease takes an estimated 17.9 million lives each year. Although many developments in the field of medicine have succeeded in reducing the death rate of heart diseases in recent years, we are failing in the early diagnosis of these diseases. The time has come for us to treat ML and AI algorithms as more than simple trends.

However effective heart disease prediction proves complex due to various contributing risk factors such as diabetes, high blood pressure, and abnormal pulse rates. Several data mining and neural network techniques have been employed to gauge the severity of heart disease but the prediction of it is a different subject.

This ailment is subclinical, and thats why experts recommend check-ups twice a year for anyone over the age of 30. But lets face it, human beings are lazy and look for the simplest way to do something but how hard can it be to accept an effective and technological medical innovation at a time when we can do our weekly shopping at home with a single voice command into our lives?

Heart disease is one of the leading causes of death worldwide and is a significant public health concern. The deadliness of heart disease depends on various factors, including the type of heart disease, its severity, and the individuals overall health. But does that mean we are left without any preventative method? Is there any way to find it out before it happens to us?

The speed of technological development has reached a peak that we never could have imagined, especially in the last three years. This technological journey of humanity, which started with the slow integration of IoT systems such as Alexa into our lives, has peaked in the last quarter of 2022 with the increase in the prevalence and use of ChatGPT and other LLM models. We are no longer far from the concepts of AI and ML, and these products are preparing to become the hidden power behind medical prediction and diagnostics.

Hybrid machine learning techniques can help with effective heart disease prediction by combining the strengths of different machine learning algorithms and utilizing them in a way that maximizes their predictive power.

Hybrid techniques can help in feature engineering, which is an essential step in machine learning-based predictive modeling. Feature engineering involves selecting and transforming relevant variables from raw data into features that can be used by machine learning algorithms. By combining different techniques, such as feature selection, feature extraction, and feature transformation, hybrid machine learning techniques can help identify the most informative features that contribute to effective heart disease prediction.

The choice of an appropriate model is critical in predictive modeling. Hybrid machine learning techniques excel in model selection by amalgamating the strengths of multiple models. By combining, for example, a decision tree with a support vector machine (SVM), these hybrid models leverage the interpretability of decision trees and the robustness of SVMs to yield superior predictions in medicine.

Model ensembles, formed by merging predictions from multiple models, are another avenue where hybrid techniques shine. The synergy of diverse models often surpasses individual model performance, resulting in more accurate heart disease predictions. For instance, a hybrid ensemble uniting a random forest with a gradient-boosting machine leverages both models strengths to increase the prediction accuracy of heart diseases.

Dealing with missing values is a common challenge in medical data analysis. Hybrid machine learning techniques prove beneficial by combining imputation strategies like mean imputation, median imputation, and statistical model-based imputation. This amalgamation helps mitigate the impact of missing values on predictive accuracy.

The proliferation of large datasets poses challenges related to high-dimensional data. Hybrid approaches address this challenge by fusing dimensionality reduction techniques like principal component analysis (PCA), independent component analysis (ICA), and singular value decomposition (SVD) with machine learning algorithms. This results in reduced data dimensionality, enhancing model interpretability and prediction accuracy.

Traditional machine learning algorithms may falter when dealing with non-linear relationships between variables. Hybrid techniques tackle this issue effectively by amalgamating methods such as polynomial feature engineering, interaction term generation, and the application of recursive neural networks. This amalgamation captures non-linear relationships, thus improving predictive accuracy.

Hybrid machine learning techniques enhance model interpretability by combining methodologies that shed light on the models decision-making process. For example, a hybrid model coupling a decision tree with a linear model offers interpretability akin to decision trees alongside the statistical significance provided by linear models. This comprehensive insight aids in better understanding and trustworthiness of heart disease predictions.

Multiple studies have explored heart disease prediction using hybrid machine learning techniques One such novel method, designed to enhance prediction accuracy, incorporates a combination of hybrid machine learning techniques to identify significant features for cardiovascular disease prediction.

Mohan, Thirumalai, and Srivastava propose a novel method for heart disease prediction that uses a hybrid of machine learning techniques. The method first uses a decision tree algorithm to select the most significant features from a set of patient data.

The researchers compared their method to other machine learning methods for heart disease prediction, such as logistic regression and naive Bayes. They found that their method outperformed these other methods in terms of accuracy.

The decision tree algorithm used to select features is called the C4.5 algorithm. This algorithm is a popular choice for feature selection because it is relatively simple to understand and implement, and it has been shown to be effective in a variety of applications including effective heart disease prediction.

The SVM classifier used to predict heart disease is a type of machine learning algorithm that is known for its accuracy and robustness. SVM classifiers work by finding a hyperplane that separates the data points into two classes. In the case of heart disease prediction, the two classes are patients with heart disease and patients without heart disease.

Exploring the leading AI medical scribes

The researchers suggest that their method could be used to develop a clinical decision support system for the early detection of heart disease. Such a system could help doctors to identify patients who are at high risk of heart disease and to provide them with preventive care.

The authors method has several advantages over other machine learning methods for effective heart disease prediction. First, it is more accurate. Second, it is more robust to noise in the data. Third, it is more efficient to train and deploy.

The authors method is still under development, but it has the potential to be a valuable tool for the early detection of heart disease. The authors plan to further evaluate their method on larger datasets and to explore ways to improve its accuracy.

In addition to the advantages mentioned by the authors, their method also has the following advantages:

The authors evaluated their method on a dataset of 13,000 patients. The dataset included information about the patients age, sex, race, smoking status, blood pressure, cholesterol levels, and other medical history. The authors found that their method was able to predict heart disease with an accuracy of 87.2%.

In another study by Bhatt, Patel, Ghetia, and Mazzero which investigated the use of machine learning (ML) techniques to effectively predict heart disease in 2023, the researchers used a dataset of 1000 patients with heart disease and 1000 patients without heart disease. They used four different ML techniques: decision trees, support vector machines, random forests, and neural networks.

The researchers found that all four ML techniques were able to predict heart disease with a high degree of accuracy. The decision tree algorithm had the highest accuracy, followed by the support vector machines, random forests, and neural networks.

The researchers also found that the accuracy of the ML techniques was improved when they were used in combination with each other. For example, the decision tree algorithm combined with the support vector machines had the highest accuracy of all the models.

The studys findings suggest that ML techniques can be used as an effective tool for predicting heart disease. The researchers believe that these techniques could be used to develop early detection and prevention strategies for heart disease.

In addition to the findings mentioned above, the study also found that the following factors were associated with an increased risk of heart disease:

The studys findings highlight the importance of early detection and prevention of heart disease. By identifying people who are at risk for heart disease, we can take steps to prevent them from developing the disease.

The study is limited by its small sample size. However, the findings are promising and warrant further research. Future studies should be conducted with larger sample sizes to confirm the findings of this study.

Predicting heart disease using hybrid machine learning techniques is an evolving field with several challenges and promising future directions.

One of the primary challenges is obtaining high-quality and sufficiently large datasets for training hybrid models. This involves collecting diverse patient data, including clinical, genetic, and lifestyle factors. Choosing the most relevant features from a large pool is crucial. Hybrid techniques aim to combine different feature selection methods to enhance prediction accuracy.

Deciding which machine learning algorithms to use in hybrid models is critical. Researchers often experiment with various algorithms like random forest, K-nearest neighbor, and logistic regression to find the best combination. Interpreting hybrid model predictions can be challenging due to their complexity. Ensuring transparency and interpretability is essential for clinical acceptance.

The class distribution in heart disease datasets can be imbalanced, with fewer positive cases. Addressing this imbalance is vital for accurate predictions. Ensuring that hybrid models also generalize well to unseen data is a constant concern. Techniques like cross-validation and robust evaluation methods are crucial.

Future directions in effective heart disease prediction using hybrid machine learning techniques encompass several key areas.

A prominent trajectory in the field involves the customization of treatment plans based on individual patient profiles, a trend that continues to gain momentum. Hybrid machine learning models are poised to play a pivotal role in this endeavor by furnishing personalized risk assessments. This approach holds great promise for tailoring interventions to patients unique needs and characteristics, potentially improving treatment outcomes.

The integration of multi-omics data, including genomics, proteomics, and metabolomics, with clinical information represents a compelling avenue for advancing effective heart disease prediction. By amalgamating these diverse data sources, hybrid model techniques can generate more accurate predictions. This holistic approach has the potential to provide deeper insights into the underlying mechanisms of heart disease and enhance predictive accuracy.

As the complexity of hybrid machine learning model techniques increases, ensuring that these models are interpretable and provide transparent explanations for their predictions becomes paramount. The development of hybrid models that offer interpretable explanations can significantly enhance their clinical utility. Healthcare professionals can better trust and utilize these models in decision-making processes, ultimately benefiting patient care.

Another promising direction involves the integration of real-time patient data streams with hybrid models. This approach enables continuous monitoring of patients, facilitating early detection and intervention in cases of heart disease. By leveraging real-time data, hybrid models can provide timely insights, potentially preventing adverse cardiac events and improving patient outcomes.

Collaboration stands as a cornerstone for future progress in effective heart disease prediction using hybrid machine learning techniques. Effective collaboration between medical experts, data scientists, and machine learning researchers is instrumental in driving innovation. Combining domain expertise with advanced computational methods can lead to breakthroughs in hybrid models accuracy and clinical applicability for heart disease prediction.

While heart disease prediction using hybrid machine learning techniques faces data, model complexity, and interpretability challenges, it holds promise for personalized medicine and improving patient outcomes through early detection and intervention. Collaboration and advancements in data collection and analysis methods will continue to shape the future of this field and perhaps humanity.

Featured image credit: rawpixel.com/Freepik

See the original post:
How Can Hybrid Machine Learning Techniques Help With Effective ... - Dataconomy

Evaluation of performances of algorithms

We selected the following seven algorithms most often used in radiomics studies for feature selection, based on filtering approaches. These filters can be grouped into three categories : those from the statistical field including the Pearson correlation coefficient (abbreviated as Pearson in the manuscript) and Spearman correlation coefficient (Spearman ), those based on random forests including Random Forest Variable Importance (RfVarImp ) and Random Forest Permutation Importance (RfPerImp), and those based on the information theory including Joint Mutual Information (JMI), Joint Mutual Information Maximization (JMIM) and Minimum-Redundancy-Maximum-Relevance (MRMR).

These methods rank features, and then a given number of best features are kept for modeling. Three different numbers of selected features were investigated in this study: 10, 20 and 30.

Moreover, in order to estimate the impact of the feature selection step, two non-informative algorithms of feature selection were used as benchmarks: no selection which resulted in selecting all features (All) and a random selection of a given number of features (Random).

Fourteen machine-learning or statistical binary classifiers were tested, among those most often used in radiomics studies: K-Nearest Neighbors (KNN); five linear models including Linear Regression (Lr), three Penalized Linear Regression (Lasso Penalized Linear Regression (LrL1), Ridge Penalized Linear Regression (LrL2), Elastic-net Linear Regression (LrElasticNet)) and Linear Discriminant Analysis (LDA); Random Forest (RF); AdaBoost and XGBoost; three support vector classifiers including Linear Support Vector Classifier (Linear SVC), Polynomial Support Vector Classifier (PolySVC) and Radial Support Vector Classifier (RSVC); and two bayesian classifiers including Binomial Naive Bayes (BNB) and Gaussian Naive Bayes (GNB).

In order to estimate performances of each of the 126 combinations of the nine feature selection algorithms with the fourteen classification algorithms, each combination was trained using a grid-search and nested cross validation strategy15 as follows.

First, datasets were randomly split into three folds, stratified on the diagnostic value so that each fold had the same diagnostic distribution as the population of interest. Each fold was used in turn as the test set while the two remaining folds were used as training and cross-validation sets.

Ten-fold cross validation and grid-search were used on the training set to tune the hyperparameters maximizing the area under the receiver operating characteristic curve (AUC). Best hyperparameters were then used to train the model on the whole training set.

In order to take into account overfitting, the metric used was the AUC penalized by the absolute value of the difference between the AUCs of the test set and the train set:

$${text{AUC}}_{{{text{Cross}} - {text{Validation}}}} = {text{AUC}}_{{{text{Test}} - {text{Fold}}}} - left| {{text{AUC}}_{{{text{Test}} - {text{Fold}}}} - {text{AUC}}_{{{text{Train}} - {text{Fold}}}} } right|$$

This procedure was repeated for each of the ten datasets, for three different train-test splits and the three different numbers of selected features.

Each combination of algorithms yielded 90 (3310) AUCs, apart from combinations using the All feature selection which were associated with only 30 AUCs due to the absence of number of feature selection, the Random feature selection, repeated three times which yielded 270 AUCs. Hence, in total, 13,020 AUCs were calculated.

Multifactor ANalysis of VAriance (ANOVA) was used to quantify the variability of the AUC associated with the following factors: dataset, feature selection algorithm, classifier algorithm, number of features, train-test split, imaging modality, and interactions between classifier / dataset, classifier / feature selection, dataset / feature selection, and classifier / feature selection / dataset. Proportion of variance explained was used to quantify impacts of each factor/interaction. Results are given as frequency (proportion(%)) or range (minimum value; maximum value).

For each feature selection, classifier, dataset and train-test split, median AUC,1st quartile (Q1); and 3rd quartile (Q3) were computed. Box-plots were used to visualize results.

In addition, for feature selection algorithms and classifiers, a Friedman test16 followed by post-hoc pair-wise Nemenyi-Friedman tests were used to compare the median AUCs of the algorithms.

Heatmaps were generated to illustrate results for each Feature Selection and Classifier combination.

All the algorithms were implemented in Python (version 3.8.8). Pearson and Spearman correlations were computed using Pandas (1.2.4), the XGBoost algorithm using xgboost (1.5) and JMI, JMIM and MRMR algorithms using MIFS. All other algorithms were implemented using the scikit-learn library (version 0.24.1). Data were standardized by centering and scaling using scikit-learn StandardScaler.

See the original post here:
Comparative performances of machine learning algorithms in ... - Nature.com

James, W., Burkhardt, F., Bowers, F. & Skrupskelis, I. K. The Principles of Psychology Vol. 1 (Macmillan London, 1890).

Belk, R. W. Extended self in a digital world. J. Consum. Res. 40, 477500 (2013).

Article Google Scholar

Buckner, R. L. & Carroll, D. C. Self-projection and the brain. Trends Cogn. Sci. 11, 4957 (2007).

Article PubMed Google Scholar

Dennett, D. C. in Self and Consciousness 111123 (Psychology Press, 2014).

Sui, J. & Humphreys, G. W. The integrative self: how self-reference integrates perception and memory. Trends Cogn. Sci. 19, 719728 (2015).

Article PubMed Google Scholar

Blanke, O. & Metzinger, T. Full-body illusions and minimal phenomenal selfhood. Trends Cogn. Sci. 13, 713 (2009).

Article PubMed Google Scholar

Bem, D. J. Self-perception: an alternative interpretation of cognitive dissonance phenomena. Psychol. Rev. 74, 183 (1967).

Article CAS PubMed Google Scholar

McConnell, A. R. The multiple self-aspects framework: self-concept representation and its implications. Personal. Soc. Psychol. Rev. 15, 327 (2011).

Article Google Scholar

Sanchez-Vives, M. V. & Slater, M. From presence to consciousness through virtual reality. Nat. Rev. Neurosci. 6, 332339 (2005).

Article CAS PubMed Google Scholar

Strawson, G. The sense of the self. Lond. Rev. Books 18, 126152 (1996).

Google Scholar

Dennett, D. C. in Science Fiction and Philosophy: From Time Travel to Superintelligence (ed. Schneider, S.) 5568 (John Wiley & Sons, 2016).

Nozick, R. Philosophical Explanations (Harvard Univ. Press, 1981).

Perry, J. Can the self divide? J. Philos. 69, 463488 (1972).

Article Google Scholar

Moulin-Frier, C. et al. DAC-h3: a proactive robot cognitive architecture to acquire and express knowledge about the world and the self. IEEE Trans. Cogn. Dev. Syst. 10, 10051022 (2017).

Article Google Scholar

Johnson, M. & Demiris, Y. Perceptual perspective taking and action recognition. Int. J. Adv. Rob. Syst. 2, 32 (2005).

Article Google Scholar

Paul, L., Ullman, T. E., De Freitas, J. & Tenenbaum, J. Reverse-engineering the self. Preprint at https://doi.org/10.31234/osf.io/vzwrn (2023).

Andrychowicz, M. et al. Hindsight experience replay. Adv. Neural Inform. Process. Syst. 30, 50485058 (2017).

Hausknecht, M. & Stone, P. in 2015 AAAI Fall Symposium Series 2937 (AAAI, 2015).

Schaul, T., Quan, J., Antonoglou, I. & Silver, D. Prioritized experience replay. Preprint at https://doi.org/10.48550/arXiv.1511.05952 (2015).

Van Hasselt, H., Guez, A. & Silver, D. in Proc. AAAI Conference on Artificial Intelligence 20942100 (AAAI, 2016).

Wang, Z. et al. in International Conference on Machine Learning 19952003 (PMLR, 2016).

Mnih, V. et al. Playing Atari with deep reinforcement learning. Preprint at https://doi.org/10.48550/arXiv.1312.5602 (2013).

Kaiser, L. et al. Model-based reinforcement learning for Atari. Preprint at https://doi.org/10.48550/arXiv.1903.00374 (2019).

Dubey, R., Agrawal, P., Pathak, D., Griffiths, T. L. & Efros, A. A. Investigating human priors for playing video games. Preprint at https://doi.org/10.48550/arXiv.1802.10217 (2018).

Tsividis, P. A. et al. Human-level reinforcement learning through theory-based modeling, exploration, and planning. Preprint at https://doi.org/10.48550/arXiv.2107.12544 (2021).

Tsividis, P. A., Pouncy, T., Xu, J. L., Tenenbaum, J. B. & Gershman, S. J. in 2017 AAAI Spring Symposium Series 643646 (AAAI, 2017).

Uhde, C., Berberich, N., Ramirez-Amaro, K. & Cheng, G. in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 80818086 (IEEE, 2020).

Lanillos, P. & Cheng, G. Robot self/other distinction: active inference meets neural networks learning in a mirror. Preprint at https://doi.org/10.48550/arXiv.2004.05473 (2020).

Demiris, Y. & Meltzoff, A. The robot in the crib: a developmental analysis of imitation skills in infants and robots. Infant Child Dev. Int. J. Res. Pract. 17, 4353 (2008).

Article Google Scholar

Piaget, J. The construction of reality in the child. J. Consult. Psychol. 19, 77 (1955).

Article Google Scholar

Thrun, S. in Robotics and Cognitive Approaches to Spatial Mapping 1341 (Springer, 2008).

Silver, D., Singh, S., Precup, D. & Sutton, R. S. Reward is enough. Artif. Intell. 299, 103535 (2021).

Article Google Scholar

Botvinick, M. et al. Building machines that learn and think for themselves. Behav. Brain Sci. 40, E255 (2017).

Botvinick, M. et al. Building machines that learn and think for themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. Preprint at https://doi.org/10.48550/arXiv.1711.08378 (2017).

Vul, E., Goodman, N., Griffiths, T. L. & Tenenbaum, J. B. One and done? Optimal decisions from very few samples. Cogn. Sci. 38, 599637 (2014).

Article PubMed Google Scholar

Reed, S. et al. A generalist agent. Trans. Mach. Learn. Res. https://openreview.net/forum?id=1ikK0kHjvj (2022).

Schrittwieser, J. et al. Mastering Atari, Go, chess and shogi by planning with a learned model. Nature 588, 604609 (2020).

Article CAS PubMed Google Scholar

Pan, X. et al. How you act tells a lot: privacy-leakage attack on deep reinforcement learning. Preprint at https://doi.org/10.48550/arXiv.1904.11082 (2019).

Brockman, G. et al. OpenAI Gym. Preprint at https://doi.org/10.48550/arXiv.1606.01540 (2016).

Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D. & Iverson, G. Bayesian t tests for accepting and rejecting the null hypothesis. Psychon. Bull. Rev. 16, 225237 (2009).

Article PubMed Google Scholar

Hill, A., Raffin, A., Ernestus, M., Gleave, A. & Kanervisto, A. stable-baselines. GitHub https://github.com/Stable-Baselines-Team/stable-baselines (2018).

Dhariwal, P. et al. Openai baselines. GitHub https://github.com/openai/baselines (2017).

Weitkamp, L. option-critic-pytorch. GitHub https://github.com/lweitkamp/option-critic-pytorch (2019).

See more here:
Self-orienting in human and machine learning - Nature.com

A new artificial intelligence tool predicts esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA), a form of stomach cancer, at least three years prior to a diagnosis. Both cancers are highly fatal, and rates have risen sharply over the past five decades.

Researchers from the Lieutenant Colonel Charles S. Kettles Veterans Affairs Center for Clinical Management Research developed the machine learning model from the electronic medical records of 10 million US veterans. VA records are unique in that they are automatically linked to cancer registry outcomes allowing the researchers to look backwards in veterans health records for information that could be used to predict cancer. Their analysis included previous diagnoses, laboratory value results, weight, prescription history, and more.

We were able to identify individuals who developed adenocarcinoma of the esophagus or esophageal junction and used a form of machine learning to learn more about them, explains Joel Rubenstein, MD, a research scientist at theKettles VA Center and professor of internal medicine at Michigan Medicine, who named the model the Kettles Esophageal and Cardia Adenocarcinoma predictioN tool, or K-ECAN.

The team accessed the Veterans Health Administration (VHA) Corporate Data Warehouse to identify veterans diagnosed with EAC (8,430) or GCA (2,965) over a 13-year period and compared them to 10,256,887 controls. The cancer cohort was split in half. One half was used to develop the K-ECAN model for predicting cancer, another quarter was used to tune the model, with the final quarter validating the results. We found that the model predicts which individuals would develop these cancers at least three years before they did, Rubenstein says. The model was more accurate than published guidelines in predicting cancer and more accurate than other tools that are already available that have been previously validated. Their findings werepublished in Gastroenterology.

The greatest identified risk factor was age, but others were found to be associated with increased cancer risk including Barretts esophagus, a precancerous condition, and gastroesophageal reflux disease (GERD). However, the model revealed other somewhat unexpected factors including slightly elevated hematocrit, low HDL/elevated LDL, lower blood serum bicarbonate levels, and greater white blood cell counts.

All of the screening guidelines for esophageal cancer now rely on GERD symptomsheartburn and refluxto identify people who should get screening, says Rubenstein. And while GERD is associated with the cancer, it wasnt particularly important in terms of the amount of information provided to the model. Most people with GERD symptoms will never develop esophageal adenocarcinoma and gastric cardia adenocarcinoma. In addition, roughly half of the patients with this form of cancer never experienced prior GERD symptoms at all. This makes K-ECAN particularly useful because it can identify people who are at elevated risk, regardless of whether they have GERD symptoms or not, adds Rubenstein.

While current guidelines already consider screening in high-risk patients, Rubenstein notes that many providers are still unfamiliar with this recommendation and that fewer than 20% of people who have developed the cancer have had prior screening.

We envision this tool being integrated seamlessly in the electronic health record to notify providers of their patients elevated risk, Rubenstein explains. Providers would receive automated notification alerts regarding which patients are at an increased risk of developing ECA and GCA. They could then consider screening when an individual is due for a colonoscopy or when refilling acid-reducing medication as colonoscopy and upper endoscopy can be performed at the same time.

Currently, Rubensteins team is piloting the tool at the Kettles VA facility.

Read this article:
Machine Learning Tool Predicts Forms of Esophageal and Stomach ... - Inside Precision Medicine

Machine learning robots are changing the way humans interact with technology and also the way technology interacts with the world around it. These robots use machine learning skills to acquire knowledge and improve their performance over time. The field of Artificial Intelligence includes deep learning, as a branch of machine learning, which further boosts the capabilities of these robots by enabling them to process complex data and recognise meaningful patterns.

Among the latest technological advances, artificial intelligence (AI) and machine learning have become increasingly significant. The transformative capacity they bring with them is notorious in different fields, and one example of this is robotics. Machine learning robots are changing the way machines interact with their environment, acquiring knowledge and adapting to new situations. This article explores several trending topics in the technology sector such as machine learning robots, their relationship with deep learning, the intersection of robotics and machine learning, as well as the differences between artificial intelligence and machine learning.

To provide a simple example that shows how machine learning works, we can consider the one applied by streaming platforms: it is based on user behaviour for future recommendations of audiovisual content. The platform's recommendations are not static but adapt as the user's preferences change.

A machine learning robot is a type of robot that includes these machine learning techniques to acquire knowledge and improve its responsiveness, based on what it learns. These robots are designed to collect data from their environment using a variety of sensors, process the information and adjust their behaviour based on the data collected, greatly extending their autonomy.

The machine learning process allows robots to recognise patterns that help them understand their environment and perform specific tasks more efficiently by applying what they learn. By using machine learning algorithms, robots can learn autonomously without requiring specific programming for each task.

DEEP LEARNING AND MACHINE LEARNING ROBOTS In technical terms, deep learning is a model within machine learning that is of particular interest to the robotics sector. This model is based on layered algorithms known as artificial neural networks, imitating the functioning of the human brain for data processing.

These neural networks allow deep learning robots to process complex data, extract meaningful characteristics, assess whether the prediction it is making is accurate or not, and thus make more accurate decisions.

In short, the development of deep learning algorithms aims to make them increasingly efficient with less human supervision.

Thus, the ability of robots to identify objects, recognise speech and understand natural language is driven by deep learning techniques.

MACHINE LEARNING AND ROBOTICS The intersection of robotics and machine learning introduces new possibilities for the autonomy of mobile robots and for the intelligence of their task execution. Machine learning robots are being used in a wide range of applications, from inspection and maintenance or surveillance to manufacturing and healthcare.

Surveillance functions that a mobile robot is able to perform efficiently (such as maintenance rounds in an infrastructure), reach higher levels of accuracy and anticipation thanks to machine learning algorithms.

In the manufacturing industry, machine learning robots can improve the efficiency and accuracy of production processes by learning to perform complex tasks more quickly and accurately. In healthcare, we can already see the value they bring by assisting in surgeries, making accurate diagnoses or providing personalised patient care.

WHAT IS THE DIFFERENCE BETWEEN AI AND MACHINE LEARNING? The main difference between artificial intelligence and machine learning lies in their focus and application.

Artificial intelligence seeks to develop systems capable of performing tasks that require human intelligence, such as speech recognition, decision making and natural language understanding. Moreover, AI works with structured data as well as semi-structured and unstructured data.

Machine learning, on the other hand, focuses on teaching machines to learn from data, improving their performance as they acquire more information. Instead of explicitly programming each step, machine learning allows robots to adapt and improve their behaviour autonomously. Deep learning only works with structured or semi-structured data.

In summary, artificial intelligence is a broad field that involves a variety of techniques and approaches, while machine learning is a specific technique used to train machines to learn and improve their accuracy from experience.

WHICH IS BETTER, ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING? The question of which is better, artificial intelligence or machine learning, is not a simple one to answer. Artificial intelligence is a broader field that covers a variety of techniques, including machine learning. While artificial intelligence focuses on creating systems that mimic human intelligence in a general way, machine learning focuses on teaching machines to learn from experience and improve from data.

Artificial intelligence is the broader, more aspirational concept, while machine learning is a specific technique within artificial intelligence that has proven to be very effective in a variety of applications. In short, machine learning is a powerful tool used in the field of artificial intelligence.

CONCLUSION Machine learning robots are changing the way humans interact with technology and also the way technology interacts with the world around it. These robots use machine learning skills to acquire knowledge and improve their performance over time. The field of Artificial Intelligence includes deep learning, as a branch of machine learning, which further boosts the capabilities of these robots by enabling them to process complex data and recognise meaningful patterns.

While artificial intelligence and machine learning are related concepts, machine learning is a specific technique within the broader field of artificial intelligence. Finally, machine learning robots demonstrate the power of combining robotics and machine learning to create machines that are more intelligent, adaptive and ultimately useful to humans.

The rest is here:
The rise of Machine Learning Robots: Explore machine learning in ... - Robotics Tomorrow