Age Prediction Using Machine Learning Based on the data source we detect age using Machine Learning (ML) is defined in many ways. We help you to choose the right topics as per your interest we don’t not execute our wish on you. Our topic assistance team creates topics that add major domain value to your academics. Synopsis is also accompanied by us we write it which reflects the brief outline of the paper, as our synopsis team are inbuilt professionals with language knowledge grammar mistakes will be avoided. Plagiarism free paper will be given.
Over all Explanation of the Age Prediction Using Machine Learning objectives, its methodology and proposed results will be discussed.
The two most common sources are facial pictures and biomedical data like blood test results. Here we have a step-by-step process for developing an age prediction project for both methodologies:
We construct a ML model to detect the age of a single person depends on their facial image.
Transforming the structure into an application where users upload a picture and retain an age prediction using our model.
To detect the age of an individual we create ML framework that works based on their clinical data.
We combine the model into a clinical data entry system where age detections can support us in several diagnostic and research tasks.
Common Steps for Both Approaches:
Training & Validation
Optimization & Hyperparameter Tuning
Feedback & Continuous Learning
Conclusion & Future Work
We find that age prediction models can offer valuable understanding and exciting applications. It is also important to set perfect expectations, when the detection rate is not achieving 100%, we use these approaches wisely in critical applications. So, we are well versed in trending techniques to achieve the desired result. No matter where you are struck up with, we will guide to until you are well knowledgeable of the machine learning project that we have created.
Some of the interesting thesis topics that we have worked are listed below, have a look at it and get
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Brain age prediction, machine learning, multi-modality MRI, UK Biobank
A various ML methods like Lasso, relevance vector regression, SVR, XGBoost, category boost, and MLP are examined in our article to forecast middle and older aged person’s brain age. We illustrate that, brain age forecasting related to multi-modality improves the efficiency of model when compared with unimodality. Result shows the significance of image modality selection and considered Lasso as an efficient ML approach.
Aging, epigenetic clock, vascular risk factors, stroke
A major goal of our paper is to discover an innovative approach to forecast the Age-A and to evaluate the offerings of quantitative attributes to Age-A in cerebrovascular disease patients by utilizing several ML techniques. By utilizing Hannum’s epigenetic clock, we evaluated Age-A. We trained various methods like traditional LR, EN, KNN, RF, SVM and MLP for AGE-A prediction. At last, EN and MLP provides greater end results.
To forecast the active aging via motor functional age (MFA) evaluation, the ML based framework is suggested in our approach by utilizing HD-sEMG signals and retrieved features. We examined and integrated various time and frequency characteristics with ML methods. For active subjects, MFA must be similar or equal to Chronological Age (CA). We trained the methods using various datasets to examine the results.
Mathematical modeling, literature review, artificial intelligence, mathematics, deep learning, big data, XAI, mechanical engineering, structural failure, materials, structural health monitoring, service life, prediction, PRISMA
Our article describes the efficiency and evaluation of aging process categorization and mathematical modeling. We illustrated the significance and adaption of several ML techniques such as DL, DT, CNN, SVM, regression analysis, and ANN which assist to enhance the modeling of aging process performance. We carried out the comparative analysis of various ML techniques. A prediction analysis framework by ML methods ease the difficult mathematical equations illustrates the physical aspect of structural aging.
Neural networks, Abalone, Back propagation neural networks
To predict the Abalone age, we employed several ML methodologies in our research. ML approaches such as backpropagation feed-forward neural network (BPFFNN), K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, Random Forest, Gauss Naive Bayes, and Support Vector Machine (SVM) are compared for the forecasting of abalone age. We examined various optimizers with BPFFNN to estimate their impact on its performance.
Age, Pupil size, Pupil diameter, Feature extraction, CNN, Geometric features
For the forecasting of human age through the utilization of iris, our study provides a brief description of various methodologies and techniques employed by the investigator. We reviewed several research papers by considering various procedures such as image segmentation, feature extraction and categorization of the iris. An effective prediction is carried out in our study by utilizing iris based current techniques.
Twitter dataset, Author profiling, NLP, Age prediction, Gender prediction, TF-IDF
For author profiling, we employed natural language processing (NLP) and ML techniques in our article. We combined the NLP methodologies such as Tokenization, lemmatization, word and char n-grams with ML techniques including logistic regression (LR), random forest (RF), Decision tree (DT) and support vector machine (SVM). As a result, SVM technique offers better efficiency than other techniques in age and gender prediction.
Multi-layer perceptron, K nearest neighbors, Random forests, Extreme learning machine
A multi class categorization issue named Abalone age prediction is carried out in our research by utilizing Abalone dataset. Here we utilized one of the randomization methods named Extreme Learning Machine (ELM) which utilizes the concept of least square estimation. As a consequence, we compared the efficiency of ELM and various traditional ML techniques such as MLP, KNN and random forests.
Brain complexity, explainable AI, fractal dimension, SHAP
To evaluate the real generalization capabilities of the explanation, we suggested an approach that acquires the SHAP values inside the repeated nested cross-validation process. We forecast the person’s age by employing this approach utilizing brain complexity characteristics through MRI pictures. A SHAP values states that the newly executed FD has the greatest effect on others and considered as top level features for age prediction.
CoVID-19 death’s age prediction, Feature selection, AdaBoost
A major objective of our study is to forecast the age of mortality in India’s Covid-19 environment. We employed several ML approaches such as AdaBoost and Random Forest. To enhance the training speed, we carried out the feature selection procedure by utilizing random selection, PCA, SVD, and correlation. As a consequence, AdaBoost provides greater efficiency in age forecasting process.
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