scholarly journals Improving power theft detection using efficient clustering and ensemble classification

2021 ◽  
Vol 11 (5) ◽  
pp. 3704
Author(s):  
Hassan Ghaedi ◽  
Seyed Reza Kamel Tabbakh Farizani ◽  
Reza Ghaemi
Keyword(s):  
High Performance ◽  
Detection Rate ◽  
Cross Validation ◽  
Search Algorithm ◽  
Ensemble Classification ◽  
Improve Performance ◽  
The World ◽  
Clustering And Classification ◽  
Validation Technique ◽  
Power Generation Systems

One of the main concerns of power generation systems around the world is power theft. This research proposes a framework that merges clustering and classification together in order to power theft detection. Due to the fact that most datasets do not have abnormal samples or are few, we have added abnormal samples to the original datasets using artificial attacks to create balance in the datasets and increase the correct detection rate. We improved the crow search algorithm (CSA) and used the weight feature of Crows to improve performance of clustering phase. Also, to create balance between diversification and intensification, we calculated the awareness probability parameter (AP) dynamically at iterations of the algorithm. To evaluate the performance, we used the cross validation technique have used the stacking technique in its training phase. The results of extensive experiments on three reference datasets showed high performance to detect power theft. The evaluation results showed that if the data is collected correctly and sufficiently, this framework can effectively detect power theft in any actual power grid. Also, for new attacks, if their patterns can be detected from the data, it is easily possible to implement these types of attacks.

Sulphonamide: Distribution, Toxicology, Environmental Characteristics, and Analysis - A Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Kirubanandam Grace Pavithra ◽  
Vasudevan Jaikumar ◽  
Ponnusamy Senthil Kumar ◽  
PanneerSelvam SundarRajan
Keyword(s):  
Aqueous Solution ◽  
High Performance Liquid Chromatography ◽  
Capillary Electrophoresis ◽  
Liquid Chromatography ◽  
High Performance ◽  
Uv Spectroscopy ◽  
Relevant Information ◽  
Fluorometric Determination ◽  
Distinctive Features ◽  
The World

Background: Many antibiotics were widely used as medication based on their distinctive features. Among them, sulphonamides were commonly used, however their recalcitrant nature makes them difficult to dispose. Hence, their interaction with environment and analytic technique requires considerable attention globally. Objective: Therefore, this review aimed to provide detailed discussion about environmental as well as human health behaviour and analytic techniques corresponding to sulphonamides. Methods: Various results and discussion were extracted from technical journals and books published by different researchers from all over the world. The cited bibliographic references were intentionally investigated in order to extract relevant information related to proposed work. Results: In this review, the determination techniques such as UV-spectroscopy, Enthalpimetry, Immunosensor, Chromatography, Chemiluminescence, Photoinduced fluorometric determination, Capillary electrophoresis for sulphonamide determination were discussed in detail. Among them, High performance liquid chromatography (HPLC) and UV-spectroscopy was effective and extensively used for screening sulphonamide. Conclusion: Knowing the quantification and behaviour of sulphonamide in aqueous solution is mandatory to opt the suitable wastewater treatment required. Hence, choosing appropriate high precision and feasible screening techniques is necessary, which can be attained with this review.

Analysis of Feature Selection and Ensemble Classifier Methods for Intrusion Detection

2018 ◽  
Vol 7 (1) ◽  
pp. 57-72
Author(s):  
H.P. Vinutha ◽  
Poornima Basavaraju
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Information Gain ◽  
False Positive Rate ◽  
Ensemble Classifier ◽  
Ensemble Classification ◽  
Chi Square ◽  
Traffic Pattern ◽  
Data Mining Algorithms

Day by day network security is becoming more challenging task. Intrusion detection systems (IDSs) are one of the methods used to monitor the network activities. Data mining algorithms play a major role in the field of IDS. NSL-KDD'99 dataset is used to study the network traffic pattern which helps us to identify possible attacks takes place on the network. The dataset contains 41 attributes and one class attribute categorized as normal, DoS, Probe, R2L and U2R. In proposed methodology, it is necessary to reduce the false positive rate and improve the detection rate by reducing the dimensionality of the dataset, use of all 41 attributes in detection technology is not good practices. Four different feature selection methods like Chi-Square, SU, Gain Ratio and Information Gain feature are used to evaluate the attributes and unimportant features are removed to reduce the dimension of the data. Ensemble classification techniques like Boosting, Bagging, Stacking and Voting are used to observe the detection rate separately with three base algorithms called Decision stump, J48 and Random forest.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

scholarly journals Dietary and Ergogenic Supplementation to Improve Elite Soccer Players’ Performance

2021 ◽  
pp. 1-7
Author(s):  
Haniel Fernandes
Keyword(s):  
Dietary Supplements ◽  
High Performance ◽  
Scientific Literature ◽  
Soccer Players ◽  
Competitive Sport ◽  
Beetroot Juice ◽  
Improve Performance ◽  
Sustained Release Tablets ◽  
High Level ◽  
Ergogenic Supplements

<b><i>Background:</i></b> Soccer is an extremely competitive sport, where the most match important moments can be defined in detail. Use of ergogenic supplements can be crucial to improve the performance of a high-performance athlete. Therefore, knowing which ergogenic supplements are important for soccer players can be an interesting strategy to maintain high level in this sport until final and decisive moments of the match. In addition, other supplements, such as dietary supplements, have been studied and increasingly referenced in the scientific literature. But, what if ergogenic supplements were combined with dietary supplements? This review brings some recommendations to improve performance of soccer athletes on the field through dietary and/or ergogenic supplements that can be used simultaneously. <b><i>Summary:</i></b> Soccer is a competitive sport, where the match important moments can be defined in detail. Thus, use of ergogenic supplements covered in this review can improve performance of elite soccer players maintaining high level in the match until final moments, such as creatine 3–5 g day<sup>−1</sup>, caffeine 3–6 mg kg<sup>−1</sup> BW around 60 min before the match, sodium bicarbonate 0.1–0.4 g kg<sup>−1</sup> BW starting from 30 to 180 min before the match, β-alanine 3.2 and 6.4 g day<sup>−1</sup> provided in the sustained-release tablets divided into 4 times a day, and nitrate-rich beetroot juice 60 g in 200 mL of water (6 mmol of NO3<sup>−</sup> L) around 120 min before match or training, including a combination possible with taurine 50 mg kg<sup>−1</sup> BW day<sup>−1</sup>, citrulline 1.2–3.4 g day<sup>−1</sup>, and arginine 1.2–6 g day<sup>−1</sup>. <b><i>Key Messages:</i></b> Soccer athletes can combine ergogenic and dietary supplements to improve their performance on the field. The ergogenic and dietary supplements used in a scientifically recommended dose did not demonstrate relevant side effects. The use of various evidence-based supplements can add up to further improvement in the performance of the elite soccer players.

Short-term Load Forecasting by Using Improved GEP and Abnormal Load Recognition

2021 ◽  
Vol 21 (4) ◽  
pp. 1-28
Author(s):  
Song Deng ◽  
Fulin Chen ◽  
Xia Dong ◽  
Guangwei Gao ◽  
Xindong Wu
Keyword(s):  
Gene Expression ◽  
Probability Distribution ◽  
Detection Rate ◽  
Cross Validation ◽  
Normal Load ◽  
Gene Expression Programming ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Short Term Load Forecasting

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and R 2 .

scholarly journals A study on the numerical modelling of UHPFRC-strengthened members

2018 ◽  
Vol 199 ◽  
pp. 09001
Author(s):  
Renaud Franssen ◽  
Serhan Guner ◽  
Luc Courard ◽  
Boyan Mihaylov
Keyword(s):  
Numerical Modelling ◽  
High Performance ◽  
Concrete Structures ◽  
Concrete Members ◽  
High Durability ◽  
Tension Tests ◽  
The World ◽  
Aging Infrastructure ◽  
New Generation ◽  
Modelling Approach

The maintenance of large aging infrastructure across the world creates serious technical, environmental, and economic challenges. Ultra-high performance fibre-reinforced concretes (UHPFRC) are a new generation of materials with outstanding mechanical properties as well as very high durability due to their extremely low permeability. These properties open new horizons for the sustainable rehabilitation of aging concrete structures. Since UHPFRC is a young and evolving material, codes are still either lacking or incomplete, with recent design provisions proposed in France, Switzerland, Japan, and Australia. However, engineers and public agencies around the world need resources to study, model, and rehabilitate structures using UHPFRC. As an effort to contribute to the efficient use of this promising material, this paper presents a new numerical modelling approach for UHPFRC-strengthened concrete members. The approach is based on the Diverse Embedment Model within the global framework of the Disturbed Stress Field Model, a smeared rotating-crack formulation for 2D modelling of reinforced concrete structures. This study presents an adapted version of the DEM in order to capture the behaviour of UHPFRC by using a small number of input parameters. The model is validated with tension tests from the literature and is then used to model UHPFRC-strengthened elements. The paper will discuss the formulation of the model and will provide validation studies with various tests of beams, columns and walls from the literature. These studies will demonstrate the effectiveness of the proposed modelling approach.

scholarly journals Fall panicum (Panicum dichotomiflorum) toxicosis in three juvenile goats

2018 ◽  
Vol 31 (1) ◽  
pp. 90-93
Author(s):  
Sarah J. Sillman ◽  
Stephen T. Lee ◽  
Jeff Claborn ◽  
Jennifer Boruch ◽  
Seth P. Harris
Keyword(s):  
Bile Ducts ◽  
High Performance ◽  
Small Ruminants ◽  
Clinical Findings ◽  
Steroidal Saponin ◽  
Renal Tubules ◽  
Liquid Chromatography Mass Spectrometry ◽  
Steroidal Saponins ◽  
The World ◽  
Panicum Dichotomiflorum

Consumption of certain grasses belonging to the genus Panicum has been found to cause hepatogenous photosensitization and crystal-associated cholangiohepatopathy in small ruminants, and liver disease in horses, in many areas of the world. We describe herein the clinical findings, microscopic lesions, and steroidal saponin analysis of Panicum dichotomiflorum associated with fatal toxicosis in 3 juvenile goats in Nebraska. The disease presentation in our case was fulminant, with anorexia, marked icterus, and death for all affected animals in less than a week. Photosensitization was not observed. The microscopic lesions consisted of severe crystal-associated cholangiohepatopathy and nephropathy, with aggregates of clear or refractile and birefringent, acicular crystals present within bile ducts, macrophages, hepatocytes, and renal tubules. High-performance liquid chromatography–mass spectrometry of the grass samples demonstrated that dichotomin was the major steroidal saponin present (0.89 µg/mg); protodioscin was also present (0.059 µg/mg). The findings were consistent with ingestion of steroidal saponins, and P. dichotomiflorum was identified as the predominant forage available.

scholarly journals Efficient Technique for Facial Image Recognition With Support Vector Machines in 2D Images With Cross-validation in Matlab

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Support Vector Machines ◽  
Cross Validation ◽  
Facial Recognition ◽  
Kernel Functions ◽  
Efficient Technique ◽  
Support Vector ◽  
Facial Image ◽  
Vector Machines ◽  
Validation Technique ◽  
2D Images

This article presented in the context of 2D global facial recognition, using Gabor Wavelet's feature extraction algorithms, and facial recognition Support Vector Machines (SVM), the latter incorporating the kernel functions: linear, cubic and Gaussian. The models generated by these kernels were validated by the cross validation technique through the Matlab application. The objective is to observe the results of facial recognition in each case. An efficient technique is proposed that includes the mentioned algorithms for a database of 2D images. The technique has been processed in its training and testing phases, for the facial image databases FERET [1] and MUCT [2], and the models generated by the technique allowed to perform the tests, whose results achieved a facial recognition of individuals over 96%.

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