Anomaly detection with machine learning algorithms and big data in electricity consumption
When analyzing smart metering data, both reading errors and frauds can be identified. The
purpose of this analysis is to alert the utility companies to suspicious consumption behavior …
purpose of this analysis is to alert the utility companies to suspicious consumption behavior …
Efficient e-mail spam filtering approach combining Logistic Regression model and Orthogonal Atomic Orbital Search algorithm
Phishing emails called spam have created a need for reliable and intelligent spam filters.
Machine-learning techniques are effective, but current methods such as Logistic Regression …
Machine-learning techniques are effective, but current methods such as Logistic Regression …
Data augmentation using BiWGAN, feature extraction and classification by hybrid 2DCNN and BiLSTM to detect non-technical losses in smart grids
In this paper, we present a hybrid deep learning model that is based on a two-dimensional
convolutional neural network (2D-CNN) and a bidirectional long short-term memory network …
convolutional neural network (2D-CNN) and a bidirectional long short-term memory network …
Envisioning Romania's Path to Sustainable Development: A Prognostic Approach
AN Ciucu-Durnoi, MS Florescu, C Delcea - Sustainability, 2023 - mdpi.com
The objectives of sustainable development aim to find a balance between economic, social,
and ecological plans through which to reduce the use of the planet's resources without …
and ecological plans through which to reduce the use of the planet's resources without …
Diagnosis of parkinson's disease based on voice signals using SHAP and hard voting ensemble method
Parkinson's disease (PD) is the second most common progressive neurological condition
after Alzheimer's. The significant number of individuals afflicted with this illness makes it …
after Alzheimer's. The significant number of individuals afflicted with this illness makes it …
Deep learning-based electricity theft prediction in non-smart grid environments
In developing countries, smart grids are nonexistent, and electricity theft significantly
hampers power supply. This research introduces a lightweight deep-learning model using …
hampers power supply. This research introduces a lightweight deep-learning model using …
Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning
Detecting fraud related to electricity consumption is usually a difficult challenge as the input
datasets are sometimes unreliable due to missing and inconsistent records, faults …
datasets are sometimes unreliable due to missing and inconsistent records, faults …
Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel‐Tree boosting classifier—A novel sequentially executed supervised …
S Hussain, MW Mustafa… - IET Generation …, 2022 - Wiley Online Library
This paper presents a novel, sequentially executed supervised machine learning‐based
electric theft detection framework using a Jaya‐optimized combined Kernel and Tree …
electric theft detection framework using a Jaya‐optimized combined Kernel and Tree …
[HTML][HTML] Enhancing anomaly detection in electrical consumption profiles through computational intelligence
The advancement of society and the raising of people's standards of living depend heavily
on electricity in today's world. The" zero energy buildings" idea, which recommends that …
on electricity in today's world. The" zero energy buildings" idea, which recommends that …
A new electricity theft detection method using hybrid adaptive sampling and pipeline machine learning
Electricity theft not only results in higher electricity costs for regular paying customers but is
also a safety threat to the public due to illegal power connections made for cheating. Many …
also a safety threat to the public due to illegal power connections made for cheating. Many …