scholarly journals A Novel Feature-Engineered–NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8423
Author(s):  
Saddam Hussain ◽  
Mohd Wazir Mustafa ◽  
Khalil Hamdi Ateyeh Al-Shqeerat ◽  
Faisal Saeed ◽  
Bander Ali Saleh Al-rimy
Keyword(s):  
Machine Learning ◽  
Power Consumption ◽  
Second Phase ◽  
Unequal Distribution ◽  
Learning Framework ◽  
Consumption Data ◽  
Whale Optimization ◽  
Competing Models ◽  
Three Stages ◽  
Model Training

This study presents a novel feature-engineered–natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories (“Healthy” and “Theft”). Finally, each input feature’s impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.

scholarly journals Low Power, High Speed MUX Based Area Efficient Dadda Multiplier

2021 ◽  
Author(s):  
Kalaiyarasi.D ◽  
Pritha.N ◽  
Srividhya.G ◽  
Padmapriya.D
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
High Speed ◽  
Propagation Delay ◽  
Vlsi Circuits ◽  
Second Phase ◽  
Fundamental Building Block ◽  
Half Adder ◽  
Current Scenario ◽  
Three Stages

The multiplier is a fundamental building block in most digital ICs’ arithmetic units. The multiplier architecture in modern VLSI circuits must meet the main parameters of low power, high speed, and small area requirements. In this paper, a 4-bit multiplier is constructed using the Dadda algorithm with enhanced Full and Half adder blocks to achieve a smaller size, lower power consumption, and minimum propagation delay. The Dadda Algorithm-designed multiplier is used in the first phase to reduce propagation delay while adding partial products in three stages provided by AND Gates. In the second phase, each stage of the Dadda tree algorithm is built with an enhanced Full and half adders to reduce the design area, propagation delay, and power consumption while still meeting the requirements of the current scenario by using MUX logic. In an average of Conventional array Multipliers, the proposed Dadda multiplier achieved an 84.68% reduction in delay, 70.89% reduction in power, 84.68% increase in Maximum Usable Frequency (MUF), and 95.55% reduction in Energy per Samples (EPS).

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

Performance Evaluation of Fiber Wireless (FiWi) Access Network using Position Optimization of ONUs

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay
Keyword(s):  
Power Consumption ◽  
Optimization Algorithm ◽  
Optical Network ◽  
Access Network ◽  
Whale Optimization Algorithm ◽  
Wireless Access ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Whale Optimization ◽  
Wireless Access Network

: Fiber Wireless Access Network is the blend of passive optical network and wireless access network. This network provides higher capacity, better flexibility, more stability and improved reliability to the users at lower cost. Network component (such as Optical Network Unit (ONU)) placement is one of the major research issues which affects the network design, performance and cost. Considering all these concerns, we implement customized Whale Optimization Algorithm (WOA) for ONU placement. Initially whale optimization algorithm is applied to get optimized position of ONUs, which is followed by reduction of number of ONUs in the network. Reduction of ONUs is done such that with fewer number of ONUs all routers present in the network can communicate. In order to ensure the performance of the network we compute the network parameters such as Packet Delivery Ratio (PDR), Total Time for Delivering the Packets in the Network (TTDPN) and percentage reduction in power consumption for the proposed algorithm. The performance of the proposed work is compared with existing algorithms (deterministic and centrally placed ONUs with predefined hops) and has been analyzed through extensive simulation. The result shows that the proposed algorithm is superior to the other algorithms in terms of minimum required ONUs and reduced power consumption in the network with almost same packet delivery ratio and total time for delivering the packets in the network. Therefore, present work is suitable for developing cost-effective FiWi network with maintained network performance.

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

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