CoopAI-Route: DRL Empowered Multi-Agent Cooperative System for Efficient QoS-Aware Routing for Network Slicing in Multi-Domain SDN

The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this context.Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations.However,the existing IP(Internet Protocol)over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators.Conventional inter-domain routing methods,like Border Gateway Protocol(BGP),cannot make routing decisions based on performance,which frequently results in traffic flowing across congested paths that are never optimal.To address these issues,we propose CoopAI-Route,a multi-agent cooperative deep reinforcement learning(DRL)system utilizing hierarchical software-defined networks(SDN).This framework enforces network slicing in multi-domain networks and cooperative communication with various administrators to find performance-based routes in intra-and inter-domain.CoopAI-Route employs the Distributed Global Topology(DGT)algorithm to define inter-domain Quality of Service(QoS)paths.CoopAI-Route uses a DRL agent with a message-passing multi-agent Twin-Delayed Deep Deterministic Policy Gradient method to ensure optimal end-to-end routes adapted to the specific requirements of network slicing applications.Our evaluation demonstrates CoopAI-Route’s commendable performance in scalability,link failure handling,and adaptability to evolving topologies compared to state-of-the-art methods.

An Efficient Approach to Escalate the Speed of Training Convolution Neural Networks

Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentation, and many others. In image and video recognition applications, convolutional neural networks(CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output.Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.

An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.

Microstructure and mechanical behavior of AXM Mg alloy systems—A review

Automobiles are the inevitable mode of transportation.However,increasing fuel prices and carbon dioxide emissions are posing a serious threat to automobile users and the environment.Thus,the development of new lightweight materials has been a key area of research.Magnesium-based commercial alloys(AZ and ZK series alloys)are the lightest among all structural metals.However,there is still a question about the replacement of Aluminum-based alloys due to HCP crystal structure.In this connection,Mg-Al-Ca-Mn(AXM)Mg alloy can be a choice as an alternative to the existing Mg-based commercial alloys for structural applications.It contains(Al,Mg)_(2)Ca,Al_(2)Ca,Mg_(2)Ca,and Al_(8)Mn_(5)as the secondary phases,contributing to the microstructural refinement and property enhancement.However,the formation of those precipitates depends on the amount of Al,Ca,and Mn,especially,the Ca/Al ratio.In addition,the secondary processes influence the grain refinement and property enhancement of texture modifications.Hence,this review article focuses on elaborating on the significance of the Ca/Al ratio for the precipitate formation,secondary process,and texture modifications.The co-segregation behavior of other micro-alloying elements like Cerium,Lanthanum,and Zinc in AXM Mg alloy systems has also been discussed for property enhancement.

Ultrasonic-Assisted Extraction of Fucoxanthin from Marine Macroalga Padina australis:Optimization,Bioactivity,and Structural Characterization

Macroalgae serve as a potential feedstock for fucoxanthin extraction.Fucoxanthin,a bioactive pigment found in the chloroplasts of marine algae,exhibits significant pharmacological properties.As a member of the carotenoid family,fucoxanthin plays a crucial role in both the food and pharmaceutical industries.This research explores the effects of ultrasonics on the extraction of fucoxanthin from the marine macroalga Padina australis.In addition,various extraction techniques and the influence of solvents on the efficient separation of fucoxanthin from algae have been studied and compared.Using methanol,chloroform,and a combination of methanol and chloroform(1:1,v/v),conventional fucoxanthin extraction from Padina australis yielded 8.12 mg of fucoxanthin per gram of biomass.However,the ultrasonic-assisted extraction resulted in a significantly higher yield of 16.9 mg of fucoxanthin per gram of biomass,demonstrating that the use of ultrasonics enhances the extraction rate compared to conventional methods.Therefore,the efficient separation of fucoxanthin from Padina australis is highly dependent on ultrasonic-assisted extraction.The process conditions for the extraction were optimized to maximize the yield of fucoxanthin from seaweeds.The following parameters were selected for optimization studies:moisture content,particle size,mixing speed,extraction temperature,extraction duration,and solid-to-solvent ratio.The extracted fucoxanthin exhibited various biological activities,including antimicrobial and antioxidant properties,and its structure was elucidated through FTIR and NMR spectroscopy.Additionally,thin-layer chromatography of the crude algae extracts confirmed the presence of fucoxanthin in the marine algae.Given these findings,the optimized extraction process holds the potential for scaling up to large-scale fucoxanthin production.Fucoxanthin,as a potent pharmacological agent,offers promising applications in the treatment of various ailments.

Biosynthesis of Silver Nanoparticles with Clerodendron phlomoides Leave Extract:Particle Morphology,Antimicrobial Potential and Application

Silver nanoparticles are versatile nanomaterials that have found numerous applications in various fields.The use of plant extract for the synthesis of silver is a green and sustainable approach.Clerodendron phlomoides leaves extract has been found to contain various phytochemicals,such as phenols,flavonoids,tannins,and alkaloids,which possess reducing and stabilizing properties that can aid the production of silver particles.In this paper,morphological and topographical analyses were performed on silver nanoparticles.The biosynthesized silver nanoparticles showed antimicrobial potential against wound pathogens.SEM and TEM micrographs revealed that the particles were sphere and nanosized,which makes them suitable for various biomedical applications.

In Vivo Studies and Flow Cytometric Investigation on Anticancer Potential of Selenium Nanoparticles Synthesized via Aqueous Extract of Clerodendron phlomidis

Nowadays,doctors and nutritionists recommend individuals incorporate selenium-rich foods such as nuts,cereals,and mushrooms into their regular diet to maintain fitness and overall health.Selenium nanoparticles(SeNPs)exhibit strong chemopreventive capabilities.The anticipations for SeNPs with enhanced and tunable bioactive activities have led to a keen interest in phytofabrication.In this study,the aqueous extract of Clerodendron phlomidis plant leaves was utilized for the synthesis of SeNPs.In traditional Indian medicine,this plant extract is recognized as a significant anti-diabetic agent.The flavonoids tetrahydroxylflavone,7-hydroxyflavanone,and 6,4’-dimethyl-7-acetoxy-scutellarein present in this plant leaf extract demonstrate excellent anticancer activity.These secondary metabolites exhibit the ability to reduce sodium selenite into SeNPs.At a concentration of 13μg/mL,the synthesized SeNPs effectively inhibited the proliferation of the HepG2 cell line.The results suggest that the SeNPs possess promising anti-cancer potential against liver cancer and can be considered as a therapeutic agent for liver cancer treatment.Additionally,the cell cycle arrest induced by SeNPs was further confirmed by the fluorescence-activated cell sorting(FACS)method,indicating that SeNPs could efficiently differentiate cancer cells from normal cells.Notably,it showed a significant improvement in diethylnitrosamine(DEN)-induced Swiss Wistar rat groups.This scientific investigation highlights the high anti-cancer potential of SeNPs,positioning them as a promising therapeutic agent for liver cancer treatment.

SF-CNN: Deep Text Classification and Retrieval for Text Documents

Researchers and scientists need rapid access to text documents such as research papers,source code and dissertations.Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords.An efficient classification algorithm for retrieving documents based on keyword words is required.The traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in keywords.To solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text documents.The proposed SF-CNN is based on deep semantic-based bag-of-word representation for document retrieval.Traditional deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for bag-of-words.The experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN method.SF-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.

A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

The recent development of cloud computing offers various services on demand for organization and individual users,such as storage,shared computing space,networking,etc.Although Cloud Computing provides various advantages for users,it remains vulnerable to many types of attacks that attract cyber criminals.Distributed Denial of Service(DDoS)is the most common type of attack on cloud computing.Consequently,Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks.Since DDoS attacks have become increasingly widespread,it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS attacks.Further,the monitoring pattern of traffic changes and accurate detection of DDoS attacks are most important and urgent.In this research work,DDoS attack detection methods based on deep belief network feature extraction and Hybrid Long Short-Term Memory(LSTM)model have been proposed with NSL-KDD dataset.In Hybrid LSTM method,the Particle Swarm Optimization(PSO)technique,which is combined to optimize the weights of the LSTM neural network,reduces the prediction error.This deep belief network method is used to extract the features of IP packets,and it identifies DDoS attacks based on PSO-LSTM model.Moreover,it accurately predicts normal network traffic and detects anomalies resulting from DDoS attacks.The proposed PSO-LSTM architecture outperforms the classification techniques including standard Support Vector Machine(SVM)and LSTM in terms of attack detection performance along with the results of the measurement of accuracy,recall,f-measure,precision.

Fabrication of chitosan/silica hybrid coating on AZ31 Mg alloy for orthopaedic applications

Biocompatible conversion of chitosan and chitosan/silica hybrid coating were prepared to enhance the biocompatibility and corrosion resistance of biodegradable AZ31 Mg alloy. The coatings were optimized and analysed with potentiodynamic polarization, SEM, ATR-IR and XPS studies. Potentiodynamic polarization studies, revealed that the coatings exhibited high corrosion resistance. The surface morphology of the Ch-3/Si coating showed small globular rough structure. The presence of functional groups was confirmed by ATR-IR. For a better understanding of chitosan/silica hybrid coating, the chemical states were examined by XPS studies. The in-vitro bioactivity of the coated samples was evaluated in Earle’s solution, which formed a dense layer of coral-like structure and calcium-deficient apatite with less stoichiometric ratio than the hydroxyapatite. In-vitro cell culture studies exhibited a good cell proliferation rate and the fabricated Ch-3/Si coating was found to be non-hemolytic. The bacterial studies proved that Ch-3/Si coating possessed inherent antibacterial activity.

Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network(MANET).A MANET’s nodes could engage actively and dynamically with one another.However,MAN-ETs,from the other side,are exposed to severe potential threats that are difficult to counter with present security methods.As a result,several safe communication protocols designed to enhance the secure interaction among MANET nodes.In this research,we offer a reputed optimal routing value among network nodes,secure computations,and misbehavior detection predicated on node’s trust levels with a Hybrid Trust based Reputation Mechanism(HTRM).In addition,the study designs a robust Public Key Infrastructure(PKI)system using the suggested trust evaluation method in terms of“key”generation,which is a crucial component of a PKI cryptosystem.We also concentrate on the solid node authenticating process that relies on pre-authentication.To ensure edge-to-edge security,we assess safe,trustworthy routes to secure computations and authenticate mobile nodes,incorporating uncertainty into the trust management solution.When compared to other protocols,our recommended approach performs better.Finally,we use simulations data and performance evaluation metrics to verify our suggested approach’s validity Our approach outperformed the competing systems in terms of overall end-to-end delay,packet delivery ratio,performance,power consumption,and key-computing time by 3.47%,3.152%,2.169%,and 3.527%,3.762%,significantly.

A Parallel Approach for Sentiment Analysis on Social Networks Using Spark

The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics.As a result,social media has emerged as the most effective and largest open source for obtaining public opinion.Single node computational methods are inefficient for sentiment analysis on such large datasets.Supercomputers or parallel or distributed proces-sing are two options for dealing with such large amounts of data.Most parallel programming frameworks,such as MPI(Message Processing Interface),are dif-ficult to use and scale in environments where supercomputers are expensive.Using the Apache Spark Parallel Model,this proposed work presents a scalable system for sentiment analysis on Twitter.A Spark-based Naive Bayes training technique is suggested for this purpose;unlike prior research,this algorithm does not need any disk access.Millions of tweets have been classified using the trained model.Experiments with various-sized clusters reveal that the suggested strategy is extremely scalable and cost-effective for larger data sets.It is nearly 12 times quicker than the Map Reduce-based model and nearly 21 times faster than the Naive Bayes Classifier in Apache Mahout.To evaluate the framework’s scalabil-ity,we gathered a large training corpus from Twitter.The accuracy of the classi-fier trained with this new dataset was more than 80%.

Triplet Label Based Image Retrieval Using Deep Learning in Large Database

Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wise label similarity is used tofind the matching images from the database.But this method lacks of limited propose code and weak execution of misclassified images.In order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network(PABTN)is introduced to study propose code that gives maximum discriminative ability.To improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are used.From the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average Precision(mAP)in the CIFAR-10 and NUS-WIPE datasets.

Hyperspectral Remote Sensing Image Classification Using Improved Metaheuristic with Deep Learning

Remote sensing image(RSI)classifier roles a vital play in earth observation technology utilizing Remote sensing(RS)data are extremely exploited from both military and civil fields.More recently,as novel DL approaches develop,techniques for RSI classifiers with DL have attained important breakthroughs,providing a new opportunity for the research and development of RSI classifiers.This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification(ISMOGCN-HRSC)model.The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs.In the presented ISMOGCN-HRSC model,the synergic deep learning(SDL)model is exploited to produce feature vectors.The GCN model is utilized for image classification purposes to identify the proper class labels of the RSIs.The ISMO algorithm is used to enhance the classification efficiency of the GCN method,which is derived by integrating chaotic concepts into the SMO algorithm.The experimental assessment of the ISMOGCN-HRSC method is tested using a benchmark dataset.

最小量润滑下搅拌摩擦焊接AA2014合金的组织与力学性能

搅拌摩擦焊(FSW)的余热溶解析出相降低了沉淀强化AA2014合金的接头质量。本研究采用最小量润滑(MQL)技术来散热,将采用两步法制备的石墨烯纳米流体作为冷却剂,用光学显微镜、扫描电镜、透射电镜、X射线衍射和差示扫描量热法研究晶粒结构和析出相的发展。结果表明,在不同转速和焊接速度组合下,不同的热循环对析出相的转变不同,从而对焊缝性能产生调节作用,成功消除无沉淀析出带的形成。采用n-MQL技术产生了稳定的θ相,从而使焊接接头有更高的焊接性能。在1200 r/min的转速和72 mm/min的焊接速度下,建立了具有优异性能的工艺窗口。

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

Mobile Ad-hoc Networks(MANET)usage across the globe is increas-ing by the day.Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes.The trust values are estimated based on the reputation values of each node in the network by using different mechanisms.However,these mechanisms have various challenging issues which degrade the network performance.Hence,a novel Quality of Service(QoS)Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work.Initially,the QoS-based trust estimation is proposed by using a Fuzzy logic scheme.The trust value of each node is estimated by using each node’s reputation values which are deter-mined based on the fuzzy membership function values and utilizing QoS para-meters such as residual energy,bandwidth,node mobility,and reliability.This mechanism prevents only the black hole attack in the network during transmis-sion.But,the gray hole attacks are not identified which in turn increases the pack-et drop rate significantly.Hence,the gray hole attack is also detected based on the Kullback-Leibler(KL)divergence method used for estimating the statistical mea-sures.Additional QoS metrics are considered to prevent the gray hole attack,such as packet loss,packet delivery ratio,and delay for each node.Thus,the proposed mechanism prevents both black hole and gray hole attacks simultaneously.Final-ly,the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.

A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classification problems have received consid-erable interest.At the same time,the detection of anomalies or outliers and feature selection(FS)processes becomes important.This study develops an outlier detec-tion with feature selection technique for streaming data classification,named ODFST-SDC technique.Initially,streaming data is pre-processed in two ways namely categorical encoding and null value removal.In addition,Local Correla-tion Integral(LOCI)is used which is significant in the detection and removal of outliers.Besides,red deer algorithm(RDA)based FS approach is employed to derive an optimal subset of features.Finally,kernel extreme learning machine(KELM)classifier is used for streaming data classification.The design of LOCI based outlier detection and RDA based FS shows the novelty of the work.In order to assess the classification outcomes of the ODFST-SDC technique,a series of simulations were performed using three benchmark datasets.The experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches.

Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

Due to the development of technology in medicine,millions of health-related data such as scanning the images are generated.It is a great challenge to store the data and handle a massive volume of data.Healthcare data is stored in the cloud-fog storage environments.This cloud-Fog based health model allows the users to get health-related data from different sources,and duplicated informa-tion is also available in the background.Therefore,it requires an additional sto-rage area,increase in data acquisition time,and insecure data replication in the environment.This paper is proposed to eliminate the de-duplication data using a window size chunking algorithm with a biased sampling-based bloomfilter and provide the health data security using the Advanced Signature-Based Encryp-tion(ASE)algorithm in the Fog-Cloud Environment(WCA-BF+ASE).This WCA-BF+ASE eliminates the duplicate copy of the data and minimizes its sto-rage space and maintenance cost.The data is also stored in an efficient and in a highly secured manner.The security level in the cloud storage environment Win-dows Chunking Algorithm(WSCA)has got 86.5%,two thresholds two divisors(TTTD)80%,Ordinal in Python(ORD)84.4%,Boom Filter(BF)82%,and the proposed work has got better security storage of 97%.And also,after applying the de-duplication process,the proposed method WCA-BF+ASE has required only less storage space for variousfile sizes of 10 KB for 200,400 MB has taken only 22 KB,and 600 MB has required 35 KB,800 MB has consumed only 38 KB,1000 MB has taken 40 KB of storage spaces.

Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to time.The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time.This work proposes an adap-tive learning algorithm-based framework,Twitter Sentiment Drift Analysis-Bidir-ectional Encoder Representations from Transformers(TSDA-BERT),which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time.The framework also works on static data by converting them to data streams using the Kafka tool.The experiments conducted on real time and simulated tweets of sports,health care andfinancial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model,with accuracies of 91%,87%and 90%,respectively.Though the results have been provided only for a few topics,as a proof of concept,this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.

Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

This paper develops a parallel hybrid electric vehicle(PHEV)propor-tional integral controller with driving cycle.To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles(HEVs)combine an electric motor(EM),a battery and an internal combustion engine(ICE).The electric motor assists the engine when accelerating,driving longer highways or climbing hills.This enables the use of a smaller,more efficient engine.It also makes use of the concept of regenerative braking to maximize energy efficiency.In a Hybrid Electric Vehicle(HEV),energy dissipated while braking is utilized to charge the battery.The proportional integral controller was used in this paper to analyze engine,motor performance and the New European Driving Cycle(NEDC)was used in the vehicle driving test using Matlab/Simulink.The proportional integral controllers were designed to track the desired vehicle speed and manage the vehi-cle’s energyflow.The Sea Lion Optimization(SLnO)methods were created to reduce fuel consumption in a parallel hybrid electric vehicle and the results were obtained for the New European Driving Cycle.