Body-focused repetitive behaviors (BFRBs), like face-touching or skin-picking, are hand-driven behaviors which can damage one's appearance, if not identified early and treated. Technology for automatic detection is still under-explored, with few previous works being limited to wearables with single modalities (e.g., motion). Here, we propose a multi-sensory approach combining motion, orientation, and heart rate sensors to detect BFRBs. We conducted a feasibility study in which participants (N=10) were exposed to BFRBs-inducing tasks, and analyzed 380 mins of signals under an extensive evaluation of sensing modalities, cross-validation methods, and observation windows. Our models achieved an AUC > 0.90 in distinguishing BFRBs, which were more evident in observation windows 5 mins prior to the behavior as opposed to 1-min ones. In a follow-up qualitative survey, we found that not only the timing of detection matters but also models need to be context-aware, when designing just-in-time interventions to prevent BFRBs.

Machine Learning is the path to a better and advanced future. A Machine Learning Developer is the most demanding job in 2021 and it is going to increase by 20–30% in the upcoming 3–5 years. Machine Learning by the core is all statistics and programming concepts. The language that is mostly used by Machine learning developers for coding is python because of its simplicity. In this blog, you will some of the most asked machine learning questions that every machine learning enthusiast has to answer one day.

There are many solutions proposed for classification purposes. Most of them share one common approach. Calculate the probability that a given sample belongs to a specific class. After that it is more subjective to decide if the given probability is an indication of class membership which is derived by cut-off threshold. This threshold is mainly determined by the utility function or risk-aversion policies.

One of the important and tedious task in agricultural practices is the detection of the disease on crops. It requires huge time as well as skilled labor. This paper proposes a smart and efficient technique for detection of crop disease which uses computer vision and machine learning techniques. The proposed system is able to detect 20 different diseases of 5 common plants with 93% accuracy.

This is a technical report for CVPR 2021 AliProducts Challenge. AliProducts Challenge is a competition proposed for studying the large-scale and fine-grained commodity image recognition problem encountered by worldleading ecommerce companies. The large-scale product recognition simultaneously meets the challenge of noisy annotations, imbalanced (long-tailed) data distribution and fine-grained classification. In our solution, we adopt stateof-the-art model architectures of both CNNs and Transformer, including ResNeSt, EfficientNetV2, and DeiT. We found that iterative data cleaning, classifier weight normalization, high-resolution finetuning, and test time augmentation are key components to improve the performance of training with the noisy and imbalanced dataset. Finally, we obtain 6.4365% mean class error rate in the leaderboard with our ensemble model.

Have you been in a situation where you expected your machine learning model to perform really well but it sputtered out a poor accuracy? You've done all the hard work – so where did the classification model go wrong? How can you correct this? There are plenty of ways to gauge the performance of your classification model but none have stood the test of time like the confusion matrix. It helps us evaluate how our model performed, where it went wrong and offers us guidance to correct our path.

If you have ever tried to watch a live nest camera hoping to observe a falcon or other interesting bird, you may have had the experience of opening the live stream and seeing an empty nest. Not sure how long you should wait for the bird to return? In this article, I will describe my final project for the 12-week Metis Data Science Bootcamp that I attended January–March 2021. My project was aimed at automating nest monitoring for the Nottingham Trent University Falcon Cam. Using deep learning and automation tools, I designed a method for 24-hour bird detection that serves as an infrastructure upon which a Twitter bot or other notification system can be built to notify users when the bird enters or leaves the nest.

Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the cost of retaining an existing customer is far less than acquiring a new one (Wikipedia). According to this article, the probability of selling to a new customer is 60-70%, while the probability of selling to a new prospect is 5-20%. So knowing if a customer is at risk of leaving is one of the most important tasks a company has to perform in order to keep growing its business. The data can be found here on kaggle public datasets. We will predict if a customer will churn based on his informations. There are 7043 customers and 20 features.

Predicting gender by the name is not a simple task. In many applications, especially in the natural language processing (NLP) field, this task may be necessary, mainly when considering foreign names. Some machine learning algorithms can satisfactorily perform the prediction. In this paper, we examined and implemented feedforward and recurrent deep neural network models, such as MLP, RNN, GRU, CNN, and BiLSTM, to classify gender through the first name. A dataset of Brazilian names is used to train and evaluate the models. We analyzed the accuracy, recall, precision, and confusion matrix to measure the models' performances. The results indicate that the gender prediction can be performed from the feature extraction strategy looking at the names as a set of strings. Some models accurately predict the gender in more than 90% of the cases. The recurrent models overcome the feedforward models in this binary classification problem.

QuaPy is an open-source framework for performing quantification (a.k.a. supervised prevalence estimation), written in Python. Quantification is the task of training quantifiers via supervised learning, where a quantifier is a predictor that estimates the relative frequencies (a.k.a. prevalence values) of the classes of interest in a sample of unlabelled data. While quantification can be trivially performed by applying a standard classifier to each unlabelled data item and counting how many data items have been assigned to each class, it has been shown that this "classify and count" method is outperformed by methods specifically designed for quantification. QuaPy provides implementations of a number of baseline methods and advanced quantification methods, of routines for quantification-oriented model selection, of several broadly accepted evaluation measures, and of robust evaluation protocols routinely used in the field. QuaPy also makes available datasets commonly used for testing quantifiers, and offers visualization tools for facilitating the analysis and interpretation of the results. The software is open-source and publicly available under a BSD-3 licence via https://github.com/HLT-ISTI/QuaPy, and can be installed via pip (https://pypi.org/project/QuaPy/)