The way Complex Machine Learning (ML) models generate their results is not fully understood, including by very knowledgeable users. If users cannot interpret or trust the predictions generated model, they will use them. Furthermore, human role often properly considered in development of ML systems. In this article, we present design, implementation and evaluation Explain-ML, an Interactive (IML...

The emergence of artificial intelligence (AI) and its derivative technologies, such as machine learning (ML) deep (DL), heralds a new era knowledge management (KM) presentation discovery. KM necessitates ML for improved organisational experiences, particularly in making more discoverable shareable. Machine is type that requires tools techniques to acquire, store, analyse data used improve decis...

exploiting multimodal information like acceleration and heart rate is a promising method to achieve human action recognition. a semi-supervised action recognition approach aucc (action understanding with combinational classifier) using the diversity of base classifiers to create a high-quality ensemble for multimodal human action recognition is proposed in this paper. furthermore, both labeled ...

For treatment effects - one of the core issues in modern econometric analysis prediction and estimation are two sides same coin. As it turns out, machine learning methods tool for generalized models. Combined with theory, they allow us to estimate not only average but a personalized effect conditional (CATE). In this tutorial, we give an overview novel methods, explain them detail, apply via Qu...

Opportunistic Networks (OppNets) is gaining popularity day-by-day due to their various applications in the real-life world. The two major reasons for its are suitability be established without any requirement of additional infrastructure and ability tolerate long delays during data communication. Network also considered as a descendant Mobile Ad hoc (Manets) Wireless Sensor (WSNs), therefore, i...

<span lang="EN-US">Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict comparing previous data of different stores. Firstly, primary task recognize pattern factors that help sales. This study helps us understand and using machines learning models. process gets beautifies imputing missing values feature engineering. While...

Effort estimation is important for the control, quality and success of any software development product. Most efficient categories of effort estimation is Expert judgment, Algorithmic estimation and Machine Learning. The aim of this paper is to present the comparative analysis between traditional techniques and Machine Learning (ML) techniques. Results show that ML methods give more accurate ef...