Performance Assessment and Configuration Analysis in the Study of SCADA System (Supervisory Control and Data Acquisition)

Queuing models are used to assess the functionality and aesthetics of SCADA systems for supervisory control and data collection.Here,the main emphasis is on how the queuing theory can be used in the system’s design and analysis.The analysis’s findings indicate that by using queuing models,cost-performance ratios close to the ideal might be attained.This article discusses a novel methodology for evaluating the service-oriented survivability of SCADA systems.In order to evaluate the state of service performance and the system’s overall resilience,the framework applies queuing theory to an analytical model.As a result,the SCADA process is translated using the M^(X)/G/1 queuing model,and the queueing theory is used to evaluate this design’s strategy.The supplemental variable technique solves the queuing problem that comes with the subsequent results.The queue size,server idle time,utilization,and probabilistic generating factors of the distinct operating strategies are estimated.Notable examples were examined via numerical analysis using mathematical software.Because it is used frequently and uses a statistical demarcation method,this tactic is completely acceptable.The graphical representation of this perspective offers a thorough analysis of the alleged limits.

Wireless Sensor Network-based Detection of Poisonous Gases Using Principal Component Analysis

This work utilizes a statistical approach of Principal Component Ana-lysis(PCA)towards the detection of Methane(CH_(4))-Carbon Monoxide(CO)Poi-soning occurring in coal mines,forestfires,drainage systems etc.where the CH_(4) and CO emissions are very high in closed buildings or confined spaces during oxi-dation processes.Both methane and carbon monoxide are highly toxic,colorless and odorless gases.Both of the gases have their own toxic levels to be detected.But during their combined presence,the toxicity of the either one goes unidentified may be due to their low levels which may lead to an explosion.By using PCA,the correlation of CO and CH_(4) data is carried out and by identifying the areas of high correlation(along the principal component axis)the explosion suppression action can be triggered earlier thus avoiding adverse effects of massive explosions.Wire-less Sensor Network is deployed and simulations are carried with heterogeneous sensors(Carbon Monoxide and Methane sensors)in NS-2 Mannasim framework.The rise in the value of CO even when CH_(4) is below the toxic level may become hazardous to the people around.Thus our proposed methodology will detect the combined presence of both the gases(CH_(4) and CO)and provide an early warning in order to avoid any human losses or toxic effects.

A Novel Optimizer in Deep Neural Network for Diabetic Retinopathy Classification

In severe cases, diabetic retinopathy can lead to blindness. For decades,automatic classification of diabetic retinopathy images has been a challenge. Medical image processing has benefited from advances in deep learning systems. Toenhance the accuracy of image classification driven by Convolutional Neural Network (CNN), balanced dataset is generated by data augmentation method followed by an optimized algorithm. Deep neural networks (DNN) are frequentlyoptimized using gradient (GD) based techniques. Vanishing gradient is the maindrawback of GD algorithms. In this paper, we suggest an innovative algorithm, tosolve the above problem, Hypergradient Descent learning rate based Quasi hyperbolic (HDQH) gradient descent to optimize the weights and biases. The algorithms only use first order gradients, which reduces computation time andstorage space requirements. The algorithms do not require more tuning of thelearning rates as the learning rate tunes itself by means of gradients. We presentempirical evaluation of our algorithm on two public retinal image datasets such asMessidor and DDR by using Resnet18 and Inception V3 architectures. The findings of the experiment show that the efficiency and accuracy of our algorithm outperforms the other cutting-edge algorithms. HDQHAdam shows the highestaccuracy of 97.5 on Resnet18 and 95.7 on Inception V3 models respectively.

An Enhanced Trust-Based Secure Route Protocol for Malicious Node Detection

The protection of ad-hoc networks is becoming a severe concern because of the absence of a central authority.The intensity of the harm largely depends on the attacker’s intentions during hostile assaults.As a result,the loss of Information,power,or capacity may occur.The authors propose an Enhanced Trust-Based Secure Route Protocol(ETBSRP)using features extraction.First,the primary and secondary trust characteristics are retrieved and achieved routing using a calculation.The complete trust characteristic obtains by integrating all logical and physical trust from every node.To assure intermediate node trust-worthiness,we designed an ETBSRP,and it calculates and certifies each mobile node's reputation and sends packets based on that trust.Connection,honesty,power,and capacity are the four trust characteristics used to calculate node repu-tation.We categorize Nodes as trustworthy or untrustworthy according to their reputation values.Fool nodes are detached from the routing pathway and cannot communicate.Then,we use the cryptographic functions to ensure more secure data transmission.Finally,we eliminate the untrustworthy nodes from the routing process,and the datagram from the origin are securely sent to the target,increas-ing throughput by 93.4%and minimizing delay.

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Imagine numerous clients,each with personal data;individual inputs are severely corrupt,and a server only concerns the collective,statistically essential facets of this data.In several data mining methods,privacy has become highly critical.As a result,various privacy-preserving data analysis technologies have emerged.Hence,we use the randomization process to reconstruct composite data attributes accurately.Also,we use privacy measures to estimate how much deception is required to guarantee privacy.There are several viable privacy protections;however,determining which one is the best is still a work in progress.This paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data results.Further-more,this paper investigates the use of arbitrary nature with perturbations in privacy preservation.According to the research,arbitrary objects(most notably random matrices)have"predicted"frequency patterns.It shows how to recover crucial information from a sample damaged by a random number using an arbi-trary lattice spectral selection strategy.Thisfiltration system's conceptual frame-work posits,and extensive practicalfindings indicate that sparse data distortions preserve relatively modest privacy protection in various situations.As a result,the research framework is efficient and effective in maintaining data privacy and security.

Music Genre Classification Using African Buffalo Optimization

In the discipline of Music Information Retrieval(MIR),categorizing musicfiles according to their genre is a difficult process.Music genre classifica-tion is an important multimedia research domain for classification of music data-bases.In the proposed method music genre classification using features obtained from audio data is proposed.The classification is done using features extracted from the audio data of popular online repository namely GTZAN,ISMIR 2004 and Latin Music Dataset(LMD).The features highlight the differences between different musical styles.In the proposed method,feature selection is per-formed using an African Buffalo Optimization(ABO),and the resulting features are employed to classify the audio using Back Propagation Neural Networks(BPNN),Support Vector Machine(SVM),Naïve Bayes,decision tree and kNN classifiers.Performance evaluation reveals that,ABO based feature selection strategy achieves an average accuracy of 82%with mean square error(MSE)of 0.003 when used with neural network classifier.

Region Centric GL Feature Approximation Based Secure Routing for Improved QoS in MANET

Secure routing in Mobile Adhoc Network(Manet)is the key issue now a day in providing secure access to different network services.As mobile devices are used in accessing different services,performing secure routing becomes a challenging task.Towards this,different approaches exist whichfind the trusted route based on their previous transmission details and behavior of different nodes.Also,the methods focused on trust measurement based on tiny information obtained from local nodes or with global information which are incomplete.How-ever,the adversary nodes are more capable and participate in each transmission not just to steal the data also to generate numerous threats in degrading QoS(Quality of Service)parameters like throughput,packet delivery ratio,and latency of the network.This encourages us in designing efficient routing scheme to max-imize QoS performance.To solve this issue,a two stage trust verification scheme and secure routing algorithm named GL-Trust(Global-Local-Trust)is presented.The method involves in route discovery as like popular AODV(Adaptive On-demand Distance Vector)which upgrades the protocol to collect other information like transmission supported,successful transmissions,energy,mobility,the num-ber of neighbors,and the number of alternate route to the same destination and so on.Further,the method would perform global trust approximation to measure the value of global trust and perform local trust approximation to measure local trust.Using both the measures,the method would select a optimal route to perform routing.The protocol is designed to perform localized route selection when there is a link failure which supports the achievement of higher QoS performance.By incorporating different features in measuring trust value towards secure routing,the proposed GL-Trust scheme improves the performance of secure routing as well as other QoS factors.

Secure and Energy Concise Route Revamp Technique in Wireless Sensor Networks

Energy conservation has become a significant consideration in wireless sensor networks(WSN).In the sensor network,the sensor nodes have internal batteries,and as a result,they expire after a certain period.As a result,expanding the life duration of sensing devices by improving data depletion in an effective and sustainable energy-efficient way remains a challenge.Also,the clustering strategy employs to enhance or extend the life cycle of WSNs.We identify the supervisory head node(SH)or cluster head(CH)in every grouping considered the feasible strategy for power-saving route discovery in the clustering model,which diminishes the communication overhead in the WSN.However,the critical issue was determining the best SH for ensuring timely communication services.Our secure and energy concise route revamp technology(SECRET)protocol involves selecting an energy-concise cluster head(ECH)and route revamping to optimize navigation.The sensors transmit information over the ECH,which delivers the information to the base station via the determined optimal path using our strategy for effective data transmission.We modeled our methods to accom-plish power-efficient multi-hop routing.Furthermore,protected navigation helps to preserve energy when routing.The suggested solution improves energy savings,packet delivery ratio(PDR),route latency(RL),network lifetime(NL),and scalability.

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer,resulting in an inaccurate diagnosis.As a result,it needs an efficient despeckling method for ultrasound images in clinical practice and tel-emedicine.This article proposes a novel adaptive fuzzyfilter based on the direc-tionality and translation invariant property of the Non-Sub sampled Contour-let Transform(NSCT).Since speckle-noise causes fuzziness in ultrasound images,fuzzy logic may be a straightforward technique to derive the output from the noisy images.Thisfiltering method comprises detection andfiltering stages.First,image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy image.Then,the system adaptively selects the better-suitedfilter for the specific image region,resulting in significant speckle noise suppression and retention of detailed features.The suggested approach uses a weighted averagefilter to distinguish between noise and edges at thefiltering stage.In addition,we apply a structural similarity mea-sure as a tuning parameter depending on the kind of noise in the ultrasound pic-tures.The proposed methodology shows that the proposed fuzzy adaptivefilter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.

Securing Privacy Using Optimization and Statistical Models in Cognitive Radio Networks

Cognitive Radio Networks(CRN)are the possible and ideal solution for meeting the spectrum needs of next-generation communication systems.CRN is a promising alternative approach that allows spectrum sharing in many applications.The licensed users considered Primary Users(PU)and unlicensed users as Secondary Users(SU).Time and power consumption on security issues are considered degrading factors in performance for improving the Quality of Service(QoS).Irrespective of using different optimization techniques,the same methodology is to be updated for the task.So that,learning and optimization go hand in hand.It ensures the security in CRN,risk factors in spectrum sharing to SU for secure communication.The objective of the proposed work is to preserve the location of the SU from attackers and attain the clustering of SU to utilize the resource.Ant Colony Optimization(ACO)is implemented to increase the overall efficiency and utilization of the CRN.ACO is used to form clusters of SUs in the co-operative spectrum sensing technique.This paper deals with threat detection and classifying threats using parameters such as unlikability,context privacy,anonymity,conditional traceability,and trade-off.In this privacy-preserving model,overall accuracy is 97.4%,and it is 9%higher than the conventional models without Privacy-Preserving Architecture(PPA).

Electrochemical capacitance of nanostructured ruthenium- doped tin oxide Sn1-xRuxO2 by the microemulsion method

Synthesis of nanostructured Ru-doped SnO2 was successfully carried out using the reverse microemulsion method. The phase purity and the crystallite size were analyzed by XRD. The surface morphology and the microstructure of synthesized nanoparticles were analyzed by SEM and TEM. The vibration mode of nanoparticles was investigated using FTIR and Raman studies. The electrochemical behavior of the Ru- doped SnO2 electrode was evaluated in a 0.1 mol/L Na2SO4 solution using cyclic voltammetry. The 5% Ru-doped SnO2 electrode exhibited a high specific capacitance of 535.6 Fig at a scan rate 20 mV/s, possessing good conductivity as well as the electro- cycling stability. The Ru-doped SnO2 composite shows excellent electrochemical properties, suggesting that this composite is a promising material for supercapacitors.

Intense violet-blue emission and paramagnetism of nanocrystalline Gd^(3+) doped ZnO ceramics

Nanocrystalline Zn1-xGdxO(x=0,0.02,0.04,0.06,and 0.08)ceramics were synthesized by ball milling and subsequent solid-state reaction.The transmission electron microscopy(TEM)micrograph of as synthesized samples revealed the formation of crystallites with an average diameter of 60 nm,and the selected area electron diffraction(SAED)pattern confirmed the formation of wurtzite structure.A red shift in the band gap was observed with increasing Gd^(3+)concentration.The photoluminescence of nanocrystalline Gd^(3+)doped ZnO exhibited a strong violet-blue emission.Concentration dependence of the emission intensity of Gd^(3+)in ZnO was studied,and the critical concentration was found to be 4 mol%of Gd^(3+).The Gd^(3+)doped ZnO exhibited paramagnetic behavior at room temperature,and the magnetic moment increased with Gd^(3+)concentration.