9 research outputs found
Effective Detection and Prevention of Ddos Based on Big Data-Mapreduce
Distributed Denial of Service (DDoS) attacks is large-scale cooperative attacks launched from a large number of compromised hosts called Zombies are a major threat to Internet services. As the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security methodologies do not provide effective defense against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. Therefore, keeping this problem in view author presents various significant areas where data mining techniques seem to be a strong candidate for detecting and preventing DDoS attack. The new proposed methodology can perform detecting and preventing DDoS attack using MapReduce concepts in Big Data.Thus the methodology can implement for both detecting and preventing methodologies
Optimization of water reuse and modelling by saline composition with nanoparticles based on machine learning architectures
Water is a necessary resource that enables the existence of all life forms, including humans. Freshwater usage has become increasingly necessary in recent years. Facilities for treating seawater are less dependable and effective. Deep learning methods have the ability to improve salt particle analysis in saltwater's accuracy and efficiency, which will enhance the performance of water treatment plants. This research proposes a novel technique in optimization of water reuse with nanoparticle analysis based on machine learning architecture. Here, the optimization of water reuse is carried out based on nanoparticle solar cell for saline water treatment and the saline composition has been analyzed using a gradient discriminant random field. Experimental analysis is carried out in terms of specificity, computational cost, kappa coefficient, training accuracy, and mean average precision for various tunnelling electron microscope (TEM) image datasets. The bright-field TEM (BF-TEM) dataset attained a specificity of 75%, kappa coefficient of 44%, training accuracy of 81%, and mean average precision of 61%, whereas the annular dark-field scanning TEM (ADF-STEM) dataset produced specificity of 79%, kappa coefficient of 49%, training accuracy of 85%, and mean average precision of 66% as compared with the existing artificial neural network (ANN) approach.
HIGHLIGHTS
Deep learning methods have the ability to improve salt particle analysis in saltwater's accuracy and efficiency, which will enhance the performance of water treatment plants.;
This research proposes a novel technique in the optimization of water reuse with nanoparticle analysis based on machine learning architecture. Here, the optimization of water reuse is carried out based on nanoparticle solar cells for water treatment.
Anticorrosion Behaviour of SS304 Microgroove Surfaces in Saline Water
The 304 Stainless Steel (SS304) is severely affected by salt water corrosion due to its high surface wettability. By reducing its surface wettability, its corrosion can be reduced. To achieve this, topographical modification of the steel surface is an effective route. In this work, SS304 flat surfaces were topographically modified into microgrooves (ridge width 250 μm to 500 μm, groove width 200 μm, width ratio = ridge width/groove width >1). Wire cut electrical discharge machining was used to fabricate the microgrooves. Long-term wetting characteristics and long-term corrosion behaviour of flat surface and microgrooves were studied. The influence of the nature of wetting of the tested surfaces on their corrosion behaviour was examined. The sessile drop method and potentiodynamic polarization tests in sodium chloride (3.5 wt. % NaCl) solution (intermittent and continuous exposures for 168 h) were studied to characterize their wetting and corrosion behaviours, respectively. Topographical modification imparted long-term hydrophobicity and, as a consequence, long-term anticorrosion ability of the steel surface. Micropatterning reduced the corrosion rate by two orders of magnitude due to reduction in interfacial contact area with the corrosive fluid via composite wetting, i.e., solid–liquid–air interface. Microgrooves showed corrosion inhibition efficiency ≥88%, upon long-term exposure to NaCl solution. By comparing the wetting and corrosion behaviours of the microgrooves with those of the previously studied microgrooves (ridge width/groove width <1), it was found that the surface roughness of their ridges strongly influences their wetting and corrosion properties
Gradient-Boosted Decision Tree with used Slime Mould Algorithm (SMA) for wastewater treatment systems
One way to improve the infrastructure, operations, monitoring, maintenance, and management of wastewater treatment systems is to use machine learning modelling to make smart forecasting, tracking, and failure prediction systems. This method aims to use industry data to treat the wastewater treatment model. Gradient-Boosted Decision Tree (GBDT) algorithms were used gradually to predict wastewater plant parameters. In addition, we used the Slime Mould Algorithm (SMA) for feature extraction and other acceptable tuning procedures. The input and effluent Chemical Oxygen Demand (COD) prediction for effluent treatment systems applies to the GBDT approaches employed in this study. GBDT-SMA employs artificial intelligence to provide precise method modelling for complex systems. Several training and model testing techniques were used to determine the best topology for the neural network models and decision trees. The GBDT-SMA model performed best across all methods. With 500 data, GBDT-SMA achieved an accuracy of 96.32%, outperforming other models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and K-neighbours RF, which reached an accuracy of 82.97, 87.45, 85.98, and 91.45%, respectively.
HIGHLIGHTS
The Slime Mould Algorithm was employed for feature extraction and other suitable tuning techniques.;
The prediction of input and effluent COD for effluent treatment systems is applicable to the GBDT approaches employed in this study.;
To enable precise method modelling for complicated systems, GBDT-SMA leverages artificial intelligence.
Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions
Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their power output often faces uncertainties due to dynamic weather conditions. In this study, a comparative machine learning approach is introduced, utilizing multivariate regression (MR), support vector machine regression (SVMR), and Gaussian regression (GR) techniques for precise solar PV panel power prediction. The investigation into the impact of environmental factors—solar radiation, ambient temperature, and relative humidity—on PV panel output reveals the superior predictive capabilities of SVMR models. With a mean squared error (MSE) of 0.038, a mean absolute error (MAE) of 0.17, and an R-value of 0.99, SVMR outperforms GR and MR models. Conversely, Gaussian regression demonstrates comparatively weaker performance, yielding an R of 0.88, an MSE of 0.49, and an MAE of 0.63. This research underscores the reliability and enhanced accuracy of the proposed SVMR model in forecasting solar PV panel output. The outcomes presented herein carry significant implications for promoting the widespread adoption of PV panels in electricity generation, particularly in challenging environmental conditions. The findings offer valuable insights into optimizing solar PV deployment, ultimately contributing to the expansion of solar power generation in the national energy landscape. Moreover, the comparative analysis provides insights into how anticipated PV power generation can adapt to varying weather conditions, encompassing factors such as temperature, humidity, and solar radiation
Patterning SS304 Surface at Microscale to Reduce Wettability and Corrosion in Saline Water
Stainless steel 304 (SS304) experiences corrosion when it is exposed to a saline atmosphere, which attains severity due to its high surface wettability. Topographical modification of metallic surfaces is an effective route to reduce wettability and thereby mitigate liquid-mediated corrosion. In this work, topographical modification of stainless steel 304 flat surface in the form of micropillars was done (pillar width: 100 μm, inter-pillar distance: 100 μm and height: 80 μm). Micropillars were fabricated by a chemical etching process. Wetting and corrosion of the micropillars was studied over long-time duration in comparison with flat surface, before and after intermittent and continuous exposures to saline water for 168 h. Wetting was characterized by measuring the static water contact angle on the test surfaces and their corrosion by electrochemical polarization tests (electrolyte: 3.5 wt.% sodium chloride solution). The relationship between the nature of wetting of the test surfaces and their corrosion was examined. Micropillars showed predominantly composite wetting over a long time, which imparted an effective resistance against corrosion over a long time to the SS304 surface. When compared to the flat surface, the corrosion rates of the micropillars were lower by two orders of magnitude, prior to and also upon long-time contact with the NaCl solution. Micropillars lowered corrosion due to composite wetting, i.e., solid-liquid-air interface that reduced the area that was in contact with the NaCl solution. The efficiency of corrosion inhibition (η) of micropillars was 88% before long-time contact, 84% after intermittent contact, and 77% after continuous contact with NaCl solution. Topographical modification in the form of micropillars that can impart composite wetting is an effective route to induce long-term anticorrosion ability to the SS304 surface
Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning
Abstract The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment