Interference is a key factor in radar return misdetection.Strong interference might make it difficult to detect the signal or targets.When interference occurs in the sidelobes of the antenna pattern,Sidelobe Cancellat...Interference is a key factor in radar return misdetection.Strong interference might make it difficult to detect the signal or targets.When interference occurs in the sidelobes of the antenna pattern,Sidelobe Cancellation(SLC)and Sidelobe Blanking are two unique solutions to solve this problem(SLB).Aside from this approach,the probability of false alert and likelihood of detection are the most essential parameters in radar.The chance of a false alarm for any radar system should be minimal,and as a result,the probability of detection should be high.There are several interference cancellation strategies in the literature that are used to sustain consistent false alarms regardless of the clutter environment.With the necessity for interference cancellation methods and the constant false alarm rate(CFAR),the Maisel SLC algorithm has been modified to create a new algorithm for recognizing targets in the presence of severe interference.The received radar returns and interference are simulated as non-stationary in this approach,and side-lobe interference is cancelled using an adaptive algorithm.By comparing the performance of adaptive algorithms,simulation results are shown.In a severe clutter situation,the simulation results demonstrate a considerable increase in target recognition and signal to noise ratio when compared to the previous technique.展开更多
In recent decades,the generation of Municipal Solid Waste(MSW)is steadily increasing due to urbanization and technological advancement.The col-lection and disposal of municipal solid waste cause considerable environme...In recent decades,the generation of Municipal Solid Waste(MSW)is steadily increasing due to urbanization and technological advancement.The col-lection and disposal of municipal solid waste cause considerable environmental degradation,making MSW management a global priority.Waste-to-energy(WTE)using thermochemical process has been identified as the key solution in this area.After evaluating many automated Higher Heating Value(HHV)predic-tion approaches,an Optimal Deep Learning-based HHV Prediction(ODL-HHVP)model for MSW management has been developed.The objective of the ODL-HHVP model is to forecast the HHV of municipal solid waste,based on its oxy-gen,water,hydrogen,carbon,nitrogen,sulphur and ash constituents.In addition,the ODL-HHVP model contains a Deep Support Vector Machine(DSVM)regres-sion component that can accurately predict the HHV.In addition,the Beetle Swarm Optimization(BSO)method is utilised as a hyperparameter optimizer in conjunction with the DSVM model,resulting in the highest HHV prediction accu-racy.A comprehensive simulation study is conducted to validate the performance of the ODL-HHVP method.The Multiple Linear Regression(MLR),Genetic Pro-gramming(GP),Resilient backpropagation(RP),Levenberg Marquardt(LM)and DSVM approaches have attained an ineffective result with RMSEs of 4.360,2.870,3.590,3.100 and 3.050,respectively.The experimentalfindings demon-strate that the ODL-HHVP technique outperforms existing state-of-art technolo-gies in a variety of respects.展开更多
Android devices are popularly available in the commercial market at different price levels for various levels of customers.The Android stack is more vulnerable compared to other platforms because of its open-source na...Android devices are popularly available in the commercial market at different price levels for various levels of customers.The Android stack is more vulnerable compared to other platforms because of its open-source nature.There are many android malware detection techniques available to exploit the source code andfind associated components during execution time.To obtain a better result we create a hybrid technique merging static and dynamic processes.In this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing system.In the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach.The Android Sensitive Permission is one major key point to be considered while detecting malware.We select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign.Thefinal goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.展开更多
The intact data transmission to the authentic user is becoming crucial at every moment in the current era.Steganography;is a technique for concealing the hidden message in any cover media such as image,video;and audio...The intact data transmission to the authentic user is becoming crucial at every moment in the current era.Steganography;is a technique for concealing the hidden message in any cover media such as image,video;and audio to increase the protection of data.The resilience and imperceptibility are improved by choosing an appropriate embedding position.This paper gives a novel system to immerse the secret information in different videos with different methods.An audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user’s face image.A hidden message is first included in the audio from the multimedia file;using LSB Technique.The Stego-video is created in the second stage by merging the authorized user’s face into the frame of the video;by using PVD technology.Stego-audio is linked again with the stego-video in the third stage.The incorporated perspective techniques(LSB-SS and PVD-SS algorithms)with more significant data immersing capacity,good robustness and imperceptibility are proposed in this research work.The spread spectrum approach is used to increase the complexity of secret data recognition.Two different video files are tested with different voice files with the results such as PSNR,SSIM,RMSE and MSE as 52.3,0.9963,0.0024 and 0.0000059,respectively.展开更多
In recent days the usage of android smartphones has increased exten-sively by end-users.There are several applications in different categories bank-ing/finance,social engineering,education,sports andfitness,and many mor...In recent days the usage of android smartphones has increased exten-sively by end-users.There are several applications in different categories bank-ing/finance,social engineering,education,sports andfitness,and many more applications.The android stack is more vulnerable compared to other mobile plat-forms like IOS,Windows,or Blackberry because of the open-source platform.In the Existing system,malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware.The attackers target the victim with various attacks like adware,backdoor,spyware,ransomware,and zero-day exploits and create threat hunts on the day-to-day basics.In the existing approach,there are various tradi-tional machine learning classifiers for building a decision support system with limitations such as low detection rate and less feature selection.The important contents taken for building model from android applications like Intent Filter,Per-mission Signature,API Calls,and System commands are taken from the manifestfile.The function parameters of various machine and deep learning classifiers like Nave Bayes,k-Nearest Neighbors(k-NN),Support Vector Machine(SVM),Ada Boost,and Multi-Layer Perceptron(MLP)are done for effective results.In our pro-posed work,we have used an unsupervised learning multilayer perceptron with multiple target labels and built a model with a better accuracy rate compared to logistic regression,and rank the best features for detection of applications and clas-sify as malicious or benign can be used as threat model by online antivirus scanners.展开更多
Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique techniq...Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.展开更多
Tuberculosis(TB) is a communicable disease caused by Mycobacterium tuberculosis(M. tuberculosis). WHO estimated that 10.4 million new(incident) TB cases worldwide in year 2016. The increased prevalence of drug resista...Tuberculosis(TB) is a communicable disease caused by Mycobacterium tuberculosis(M. tuberculosis). WHO estimated that 10.4 million new(incident) TB cases worldwide in year 2016. The increased prevalence of drug resistant strains and side effects associated with the current anti-tubercular drugs make the treatment options more complicated. Hence, there are necessities to identify new drug candidates to fight against various sub-populations of M. tuberculosis with less or no toxicity/side effects and shorter treatment duration. Bacteriocins produced by lactic acid bacteria(LAB) attract attention of researchers because of its "Generally recognized as safe" status. LAB and its bacteriocins possess an effective antimicrobial activity against various bacteria and fungi. Interestingly bacteriocins such as nisin and lacticin 3147 have shown antimycobacterial activity in vitro. As probiotics, LAB plays a vital role in promoting various health benefits including ability to modulate immune response against various infectious diseases. LAB and its metabolic products activate immune system and thereby limiting the M. tuberculosis pathogenesis. The protein and peptide engineering techniques paved the ways to obtain hybrid bacteriocin derivatives from the known peptide sequence of existing bacteriocin. In this review, we focus on the antimycobacterial property and immunomodulatory role of LAB and its metabolic products. Techniques for large scale synthesis of potential bacteriocin with multifunctional activity and enhanced stability are also discussed.展开更多
This work examines the effect of butanol as an oxygenated additive to lower carbon monoxide,smoke,nitrogen oxide and hydrocarbon emissions and to improve the performance aspects of Calophyllum inophyllum(Punnai)biodie...This work examines the effect of butanol as an oxygenated additive to lower carbon monoxide,smoke,nitrogen oxide and hydrocarbon emissions and to improve the performance aspects of Calophyllum inophyllum(Punnai)biodiesel.Singlecylinder,oil-cooled compression ignition engines are employed in this work.Neat Punnai biodiesel(P100)is blended with butanol at 10%and 20%by volume and labelled as B10 P90 and B20 P80,respectively.Methanol and alkaline catalyst(KOH)were used for the transesterification process for biodiesel production.The transesterification technique yielded 88%biodiesel from raw Punnai oil.Engine tests resulted in lower CO,smoke,NO_x and HC emissions when fuelled with both butanol blends when compared to P100.In addition,BSFC(brake-specific fuel consumption)reduced and BTE(brake thermal effciency)increased with the inclusion of butanol blends(B10 and B20)to neat Punnai biodiesel.展开更多
Objective: To evaluate the anticancer activity of crude acetone and water leaf extracts of Tulbaghia violacea on a human oral cancer cell line(KB).Methods: The antioxidant activity of the leaf extracts was evaluated b...Objective: To evaluate the anticancer activity of crude acetone and water leaf extracts of Tulbaghia violacea on a human oral cancer cell line(KB).Methods: The antioxidant activity of the leaf extracts was evaluated by using the DPPH assay while the anti-proliferative activity was assessed by using the MTT assay.The morphological characteristics of apoptotic cells were examined by using the dual acridine orange/ethidium bromide staining.Flow cytometry was used to evaluate the induction of multi-caspase activity and changes in the cell cycle.Results: The acetone and water extracts exhibited antioxidant activity in a concentration dependent manner.The extracts inhibited the growth of the KB cell line with IC_(50) values of 0.2 mg/mL and 1 mg/mL, respectively for acetone and water.Morphological changes such as cell shrinkage, rounding and formation of membrane blebs were observed in the treated cells.In acridine orange/ethidium bromide staining, the number of apoptotic cells increased as the concentration of the extracts increased.The activation of multi-caspase activity in KB cells treated with Tulbaghia violacea extracts was concentration dependent, leading to cell death by apoptosis and cell cycle arrest at the G_2/M phase.Conclusions: The acetone and water extracts of Tulbaghia violacea appear to have anti-cancer activity against human oral cancer cells and need to be investigated further.展开更多
Natural fiber-reinforced hybrid composites can be a better replacement for plastic composites since these plastic composites pose a serious threat to the environment.The aim of this study is to analyze the effect of s...Natural fiber-reinforced hybrid composites can be a better replacement for plastic composites since these plastic composites pose a serious threat to the environment.The aim of this study is to analyze the effect of surface modification of the natural fibers on the mechanical,thermal,hygrothermal,and water absorption behaviors of flax,sisal,and glass fiber-reinforced epoxy hybrid composites.The mechanical properties of alkaline treated sisal and flax fibers were found to increase considerably.Tensile,flexural and impact strength of glass/flax-fiber-reinforced hybrid samples improved by 58%,36%,and 51%,respectively,after surface alkaline treatment.In addition,the hygrothermal analysis and water absorption capacity are studied and also the Interfacial bonding properties were analyzed using Scanning Electron Microscopic images.The thermal analysis using thermogravimetric analyzer reveals that the decomposition temperature for hybrid fiber reinforced composites are between 306 and 312℃.In conclusion,surface treatment improves the performance of natural fiber in hybrid fiber-reinforced composites,particularly flax fiber.展开更多
In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human hea...In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death.So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer research.The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment.This paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classification(SMF2NC)to solve the above pro-blem.The predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the dimensionality.The redundant features are processed marginal weight rates for observing similar features’variants and the absolute value.Softmax neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward layers.Further,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classifier produces higher classification accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.展开更多
Developing an automatic and credible diagnostic system to analyze the type,stage,and level of the liver cancer from Hematoxylin and Eosin(H&E)images is a very challenging and time-consuming endeavor,even for exper...Developing an automatic and credible diagnostic system to analyze the type,stage,and level of the liver cancer from Hematoxylin and Eosin(H&E)images is a very challenging and time-consuming endeavor,even for experienced pathologists,due to the non-uniform illumination and artifacts.Albeit several Machine Learning(ML)and Deep Learning(DL)approaches are employed to increase the performance of automatic liver cancer diagnostic systems,the classi-fication accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations.In this work,we present a new Ensemble Classifier(hereafter called ECNet)to classify the H&E stained liver histopathology images effectively.The proposed model employs a Dropout Extreme Learning Machine(DrpXLM)and the Enhanced Convolutional Block Attention Modules(ECBAM)based residual network.ECNet applies Voting Mechanism(VM)to integrate the decisions of individual classifiers using the average of probabilities rule.Initially,the nuclei regions in the H&E stain are seg-mented through Super-resolution Convolutional Networks(SrCN),and then these regions are fed into the ensemble DL network for classification.The effectiveness of the proposed model is carefully studied on real-world datasets.The results of our meticulous experiments on the Kasturba Medical College(KMC)liver dataset reveal that the proposed ECNet significantly outperforms other existing classifica-tion networks with better accuracy,sensitivity,specificity,precision,and Jaccard Similarity Score(JSS)of 96.5%,99.4%,89.7%,95.7%,and 95.2%,respectively.We obtain similar results from ECNet when applied to The Cancer Genome Atlas Liver Hepatocellular Carcinoma(TCGA-LIHC)dataset regarding accuracy(96.3%),sensitivity(97.5%),specificity(93.2%),precision(97.5%),and JSS(95.1%).More importantly,the proposed ECNet system consumes only 12.22 s for training and 1.24 s for testing.Also,we carry out the Wilcoxon statistical test to determine whether the ECNet provides a considerable improvement with respect to evaluation metrics or not.From extensive empirical analysis,we can conclude that our ECNet is the better liver cancer diagnostic model related to state-of-the-art classifiers.展开更多
Controlled thermonuclear reactors require consistent monitoring of plasma in the toroidal chamber.Better working conditions of such machines can be monitored by analyzing its radiations.Various wavelengths such as 656...Controlled thermonuclear reactors require consistent monitoring of plasma in the toroidal chamber.Better working conditions of such machines can be monitored by analyzing its radiations.Various wavelengths such as 656.3,486.1,464.7 nm are quite significant which are used for health monitoring of thermonuclear machines.The optical thinfilmfilters which work on construc-tive and destructive interference are the ideal choices.Thesefilters are multi-layered with a pair of high and low refractive index dielectric materials.Significantly high transmission index at the desired wavelength and relatively low transmission at the other wavelengths are desired.With this as the objective,it is necessary to design thefilter.Various optimization techniques are used for identifying the suitable design of thefilters.To choose the parameter combination that provides the most excellent performance,optimization of the design para-meters is entailed.The goal of this work is to improve the optical bandfilter using the Bald eagle search optimization(BES)method.The ideal design is determined by assessing several characteristics such as thickness,refractive index,Full-Width at Half-Maximum(FWHM),and the impact of choosing optical properties,which increases transmission potential.Initially,an alternate multi-layer stack with 28,30,and 32 layers is created by altering the thickness while keeping the dielectric substances high and low refractive indices constant.By adjusting the thickness of each layer,the BES algorithm achieves the best practical solution.The proposed method is implemented using MATLAB and the outcomes show the efficacy of the proposed technique.The transmittance,reflectance,and FWHM using the pro-posed BES are found to be 99.9356%,0.065%,and 1.2 nm respectively.展开更多
The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Face...The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).展开更多
The output of the fuzzy set is reduced by one for the defuzzification procedure.It is employed to provide a comprehensible outcome from a fuzzy inference process.This page provides further information about the defuzzi...The output of the fuzzy set is reduced by one for the defuzzification procedure.It is employed to provide a comprehensible outcome from a fuzzy inference process.This page provides further information about the defuzzifica-tion approach for quadrilateral fuzzy numbers,which may be used to convert them into discrete values.Defuzzification demonstrates how useful fuzzy ranking systems can be.Our major purpose is to develop a new ranking method for gen-eralized quadrilateral fuzzy numbers.The primary objective of the research is to provide a novel approach to the accurate evaluation of various kinds of fuzzy inte-gers.Fuzzy ranking properties are examined.Using the counterexamples of Lee and Chen demonstrates the fallacy of the ranking technique.So,a new approach has been developed for dealing with fuzzy risk analysis,risk management,indus-trial engineering and optimization,medicine,and artificial intelligence problems:the generalized quadrilateral form fuzzy number utilizing centroid methodology.As you can see,the aforementioned scenarios are all amenable to the solution pro-vided by the generalized quadrilateral shape fuzzy number utilizing centroid methodology.It’s laid out in a straightforward manner that’s easy to grasp for everyone.The rating method is explained in detail,along with numerical exam-ples to illustrate it.Last but not least,stability evaluations clarify why the Gener-alized quadrilateral shape fuzzy number obtained by the centroid methodology outperforms other ranking methods.展开更多
Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business domains.Quality of Service(QoS)has become an important problem in IoT surrounding sinc...Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business domains.Quality of Service(QoS)has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors,information and usage.Sen-sor data gathering is an efficient solution to collect information from spatially dis-seminated IoT nodes.Reinforcement Learning Mechanism to improve the QoS(RLMQ)and use a Mobile Sink(MS)to minimize the delay in the wireless IoT s proposed in this paper.Here,we use machine learning concepts like Rein-forcement Learning(RL)to improve the QoS and energy efficiency in the Wire-less Sensor Network(WSN).The MS collects the data from the Cluster Head(CH),and the RL incentive values select CH.The incentives value is computed by the QoS parameters such as minimum energy utilization,minimum bandwidth utilization,minimum hop count,and minimum time delay.The MS is used to col-lect the data from CH,thus minimizing the network delay.The sleep and awake scheduling is used for minimizing the CH dead in the WSN.This work is simu-lated,and the results show that the RLMQ scheme performs better than the base-line protocol.Results prove that RLMQ increased the residual energy,throughput and minimized the network delay in the WSN.展开更多
The introduction of several small and large-scale industries,malls,shopping complexes,and domestic applications has significantly increased energy consumption.The aim of the work is to simulate a technically viable an...The introduction of several small and large-scale industries,malls,shopping complexes,and domestic applications has significantly increased energy consumption.The aim of the work is to simulate a technically viable and economically optimum hybrid power system for residential buildings.The proposed micro-grid model includes four power generators:solar power,wind power,Electricity Board(EB)source,and a Diesel Generator(DG)set,with solar and wind power performing as major sources and the EB supply and DG set serving as backup sources.The core issue in direct current to alternate current conversion is harmonics distortion,a five-stage multilevel inverter is employed with the assistance of an intelligent control system is simulated and the optimum system configuration is estimated to reduce harmonics and improve the power quality.The monthly demand for residential buildings is 13-15 Megawatts.So,almost 433 Kilo-Watts(KW)of electricity is required every day,and if it is used for 8 h per day,50-60 KW of electricity is needed per hour.The overall micro-grid model’s operation and performance are established using MATLAB/SIMULINK software,and simulation results are provided.The simulation results show that the developed system is both cost-effective and environment friendly resulting in yearly cost reductions.展开更多
Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effect...Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.展开更多
The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this d...The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.展开更多
Synthetic fibers made from nylon or polypropylene have gained application when loose and woven into geo textile form although no information on the matrix’s mechanical performance is obtained so that more understandi...Synthetic fibers made from nylon or polypropylene have gained application when loose and woven into geo textile form although no information on the matrix’s mechanical performance is obtained so that more understanding of their structural contribution to resist cracking can be determined. This paper presents the results of an experimental investigation to determine the performance characteristics of concrete reinforced with a polypropylene structural fiber. In this investigation “Fiber mesh” brand of fibers manufactured by SL Concrete System, Tennessee, USA and marketed by M/S Millennium Building System, Inc., Ban-galore, India are used. The lengths of the fibers used were 24 mm. Fiber dosages used were 0.9, 1.8, 2.7 kg/m3. A total of three mixtures, one for each fiber dosage were made. A standard slump cone test was conducted on the fresh concrete mix with and without fibers to determine the workability of the mix. The test program included the evaluation of hardened concrete properties such as compressive, split tensile, modulus of rupture and flexural strengths. The increase in compressive strength is about 36.25%, 26.20%, and 23.75% respectively that of plain concrete. This increase in strength was directly proportional to amount of fibers present in the mix. The increase in flexural strength for Mixes I^III is about 21%, 16.6%, and 23% respectively that of plain concrete specimens. An experimental investigation was also made to study the behaviors of reinforced fibers concrete beams (with longitudinal reinforcements) under two-point loading. The deflection and crack patterns were also studied. The improvements in strength and ductility characteristics were discussed.展开更多
文摘Interference is a key factor in radar return misdetection.Strong interference might make it difficult to detect the signal or targets.When interference occurs in the sidelobes of the antenna pattern,Sidelobe Cancellation(SLC)and Sidelobe Blanking are two unique solutions to solve this problem(SLB).Aside from this approach,the probability of false alert and likelihood of detection are the most essential parameters in radar.The chance of a false alarm for any radar system should be minimal,and as a result,the probability of detection should be high.There are several interference cancellation strategies in the literature that are used to sustain consistent false alarms regardless of the clutter environment.With the necessity for interference cancellation methods and the constant false alarm rate(CFAR),the Maisel SLC algorithm has been modified to create a new algorithm for recognizing targets in the presence of severe interference.The received radar returns and interference are simulated as non-stationary in this approach,and side-lobe interference is cancelled using an adaptive algorithm.By comparing the performance of adaptive algorithms,simulation results are shown.In a severe clutter situation,the simulation results demonstrate a considerable increase in target recognition and signal to noise ratio when compared to the previous technique.
文摘In recent decades,the generation of Municipal Solid Waste(MSW)is steadily increasing due to urbanization and technological advancement.The col-lection and disposal of municipal solid waste cause considerable environmental degradation,making MSW management a global priority.Waste-to-energy(WTE)using thermochemical process has been identified as the key solution in this area.After evaluating many automated Higher Heating Value(HHV)predic-tion approaches,an Optimal Deep Learning-based HHV Prediction(ODL-HHVP)model for MSW management has been developed.The objective of the ODL-HHVP model is to forecast the HHV of municipal solid waste,based on its oxy-gen,water,hydrogen,carbon,nitrogen,sulphur and ash constituents.In addition,the ODL-HHVP model contains a Deep Support Vector Machine(DSVM)regres-sion component that can accurately predict the HHV.In addition,the Beetle Swarm Optimization(BSO)method is utilised as a hyperparameter optimizer in conjunction with the DSVM model,resulting in the highest HHV prediction accu-racy.A comprehensive simulation study is conducted to validate the performance of the ODL-HHVP method.The Multiple Linear Regression(MLR),Genetic Pro-gramming(GP),Resilient backpropagation(RP),Levenberg Marquardt(LM)and DSVM approaches have attained an ineffective result with RMSEs of 4.360,2.870,3.590,3.100 and 3.050,respectively.The experimentalfindings demon-strate that the ODL-HHVP technique outperforms existing state-of-art technolo-gies in a variety of respects.
文摘Android devices are popularly available in the commercial market at different price levels for various levels of customers.The Android stack is more vulnerable compared to other platforms because of its open-source nature.There are many android malware detection techniques available to exploit the source code andfind associated components during execution time.To obtain a better result we create a hybrid technique merging static and dynamic processes.In this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing system.In the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach.The Android Sensitive Permission is one major key point to be considered while detecting malware.We select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign.Thefinal goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.
文摘The intact data transmission to the authentic user is becoming crucial at every moment in the current era.Steganography;is a technique for concealing the hidden message in any cover media such as image,video;and audio to increase the protection of data.The resilience and imperceptibility are improved by choosing an appropriate embedding position.This paper gives a novel system to immerse the secret information in different videos with different methods.An audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user’s face image.A hidden message is first included in the audio from the multimedia file;using LSB Technique.The Stego-video is created in the second stage by merging the authorized user’s face into the frame of the video;by using PVD technology.Stego-audio is linked again with the stego-video in the third stage.The incorporated perspective techniques(LSB-SS and PVD-SS algorithms)with more significant data immersing capacity,good robustness and imperceptibility are proposed in this research work.The spread spectrum approach is used to increase the complexity of secret data recognition.Two different video files are tested with different voice files with the results such as PSNR,SSIM,RMSE and MSE as 52.3,0.9963,0.0024 and 0.0000059,respectively.
文摘In recent days the usage of android smartphones has increased exten-sively by end-users.There are several applications in different categories bank-ing/finance,social engineering,education,sports andfitness,and many more applications.The android stack is more vulnerable compared to other mobile plat-forms like IOS,Windows,or Blackberry because of the open-source platform.In the Existing system,malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware.The attackers target the victim with various attacks like adware,backdoor,spyware,ransomware,and zero-day exploits and create threat hunts on the day-to-day basics.In the existing approach,there are various tradi-tional machine learning classifiers for building a decision support system with limitations such as low detection rate and less feature selection.The important contents taken for building model from android applications like Intent Filter,Per-mission Signature,API Calls,and System commands are taken from the manifestfile.The function parameters of various machine and deep learning classifiers like Nave Bayes,k-Nearest Neighbors(k-NN),Support Vector Machine(SVM),Ada Boost,and Multi-Layer Perceptron(MLP)are done for effective results.In our pro-posed work,we have used an unsupervised learning multilayer perceptron with multiple target labels and built a model with a better accuracy rate compared to logistic regression,and rank the best features for detection of applications and clas-sify as malicious or benign can be used as threat model by online antivirus scanners.
文摘Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.
基金supported by Sathyabama Institute of Science and Technology,Chennai,Tamil Nadu,India and Indian Council of Medical Research(ICMR),New Delhi,India(Ref.No:5/8/5/19/2014-ECD-I)
文摘Tuberculosis(TB) is a communicable disease caused by Mycobacterium tuberculosis(M. tuberculosis). WHO estimated that 10.4 million new(incident) TB cases worldwide in year 2016. The increased prevalence of drug resistant strains and side effects associated with the current anti-tubercular drugs make the treatment options more complicated. Hence, there are necessities to identify new drug candidates to fight against various sub-populations of M. tuberculosis with less or no toxicity/side effects and shorter treatment duration. Bacteriocins produced by lactic acid bacteria(LAB) attract attention of researchers because of its "Generally recognized as safe" status. LAB and its bacteriocins possess an effective antimicrobial activity against various bacteria and fungi. Interestingly bacteriocins such as nisin and lacticin 3147 have shown antimycobacterial activity in vitro. As probiotics, LAB plays a vital role in promoting various health benefits including ability to modulate immune response against various infectious diseases. LAB and its metabolic products activate immune system and thereby limiting the M. tuberculosis pathogenesis. The protein and peptide engineering techniques paved the ways to obtain hybrid bacteriocin derivatives from the known peptide sequence of existing bacteriocin. In this review, we focus on the antimycobacterial property and immunomodulatory role of LAB and its metabolic products. Techniques for large scale synthesis of potential bacteriocin with multifunctional activity and enhanced stability are also discussed.
文摘This work examines the effect of butanol as an oxygenated additive to lower carbon monoxide,smoke,nitrogen oxide and hydrocarbon emissions and to improve the performance aspects of Calophyllum inophyllum(Punnai)biodiesel.Singlecylinder,oil-cooled compression ignition engines are employed in this work.Neat Punnai biodiesel(P100)is blended with butanol at 10%and 20%by volume and labelled as B10 P90 and B20 P80,respectively.Methanol and alkaline catalyst(KOH)were used for the transesterification process for biodiesel production.The transesterification technique yielded 88%biodiesel from raw Punnai oil.Engine tests resulted in lower CO,smoke,NO_x and HC emissions when fuelled with both butanol blends when compared to P100.In addition,BSFC(brake-specific fuel consumption)reduced and BTE(brake thermal effciency)increased with the inclusion of butanol blends(B10 and B20)to neat Punnai biodiesel.
基金supported by NAM S&T Centre Research Training Fellowship for Developing Country Scientists,Sathyabama University,India and Vaal University of Technology,South Africa
文摘Objective: To evaluate the anticancer activity of crude acetone and water leaf extracts of Tulbaghia violacea on a human oral cancer cell line(KB).Methods: The antioxidant activity of the leaf extracts was evaluated by using the DPPH assay while the anti-proliferative activity was assessed by using the MTT assay.The morphological characteristics of apoptotic cells were examined by using the dual acridine orange/ethidium bromide staining.Flow cytometry was used to evaluate the induction of multi-caspase activity and changes in the cell cycle.Results: The acetone and water extracts exhibited antioxidant activity in a concentration dependent manner.The extracts inhibited the growth of the KB cell line with IC_(50) values of 0.2 mg/mL and 1 mg/mL, respectively for acetone and water.Morphological changes such as cell shrinkage, rounding and formation of membrane blebs were observed in the treated cells.In acridine orange/ethidium bromide staining, the number of apoptotic cells increased as the concentration of the extracts increased.The activation of multi-caspase activity in KB cells treated with Tulbaghia violacea extracts was concentration dependent, leading to cell death by apoptosis and cell cycle arrest at the G_2/M phase.Conclusions: The acetone and water extracts of Tulbaghia violacea appear to have anti-cancer activity against human oral cancer cells and need to be investigated further.
文摘Natural fiber-reinforced hybrid composites can be a better replacement for plastic composites since these plastic composites pose a serious threat to the environment.The aim of this study is to analyze the effect of surface modification of the natural fibers on the mechanical,thermal,hygrothermal,and water absorption behaviors of flax,sisal,and glass fiber-reinforced epoxy hybrid composites.The mechanical properties of alkaline treated sisal and flax fibers were found to increase considerably.Tensile,flexural and impact strength of glass/flax-fiber-reinforced hybrid samples improved by 58%,36%,and 51%,respectively,after surface alkaline treatment.In addition,the hygrothermal analysis and water absorption capacity are studied and also the Interfacial bonding properties were analyzed using Scanning Electron Microscopic images.The thermal analysis using thermogravimetric analyzer reveals that the decomposition temperature for hybrid fiber reinforced composites are between 306 and 312℃.In conclusion,surface treatment improves the performance of natural fiber in hybrid fiber-reinforced composites,particularly flax fiber.
文摘In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death.So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer research.The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment.This paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classification(SMF2NC)to solve the above pro-blem.The predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the dimensionality.The redundant features are processed marginal weight rates for observing similar features’variants and the absolute value.Softmax neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward layers.Further,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classifier produces higher classification accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.
文摘Developing an automatic and credible diagnostic system to analyze the type,stage,and level of the liver cancer from Hematoxylin and Eosin(H&E)images is a very challenging and time-consuming endeavor,even for experienced pathologists,due to the non-uniform illumination and artifacts.Albeit several Machine Learning(ML)and Deep Learning(DL)approaches are employed to increase the performance of automatic liver cancer diagnostic systems,the classi-fication accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations.In this work,we present a new Ensemble Classifier(hereafter called ECNet)to classify the H&E stained liver histopathology images effectively.The proposed model employs a Dropout Extreme Learning Machine(DrpXLM)and the Enhanced Convolutional Block Attention Modules(ECBAM)based residual network.ECNet applies Voting Mechanism(VM)to integrate the decisions of individual classifiers using the average of probabilities rule.Initially,the nuclei regions in the H&E stain are seg-mented through Super-resolution Convolutional Networks(SrCN),and then these regions are fed into the ensemble DL network for classification.The effectiveness of the proposed model is carefully studied on real-world datasets.The results of our meticulous experiments on the Kasturba Medical College(KMC)liver dataset reveal that the proposed ECNet significantly outperforms other existing classifica-tion networks with better accuracy,sensitivity,specificity,precision,and Jaccard Similarity Score(JSS)of 96.5%,99.4%,89.7%,95.7%,and 95.2%,respectively.We obtain similar results from ECNet when applied to The Cancer Genome Atlas Liver Hepatocellular Carcinoma(TCGA-LIHC)dataset regarding accuracy(96.3%),sensitivity(97.5%),specificity(93.2%),precision(97.5%),and JSS(95.1%).More importantly,the proposed ECNet system consumes only 12.22 s for training and 1.24 s for testing.Also,we carry out the Wilcoxon statistical test to determine whether the ECNet provides a considerable improvement with respect to evaluation metrics or not.From extensive empirical analysis,we can conclude that our ECNet is the better liver cancer diagnostic model related to state-of-the-art classifiers.
文摘Controlled thermonuclear reactors require consistent monitoring of plasma in the toroidal chamber.Better working conditions of such machines can be monitored by analyzing its radiations.Various wavelengths such as 656.3,486.1,464.7 nm are quite significant which are used for health monitoring of thermonuclear machines.The optical thinfilmfilters which work on construc-tive and destructive interference are the ideal choices.Thesefilters are multi-layered with a pair of high and low refractive index dielectric materials.Significantly high transmission index at the desired wavelength and relatively low transmission at the other wavelengths are desired.With this as the objective,it is necessary to design thefilter.Various optimization techniques are used for identifying the suitable design of thefilters.To choose the parameter combination that provides the most excellent performance,optimization of the design para-meters is entailed.The goal of this work is to improve the optical bandfilter using the Bald eagle search optimization(BES)method.The ideal design is determined by assessing several characteristics such as thickness,refractive index,Full-Width at Half-Maximum(FWHM),and the impact of choosing optical properties,which increases transmission potential.Initially,an alternate multi-layer stack with 28,30,and 32 layers is created by altering the thickness while keeping the dielectric substances high and low refractive indices constant.By adjusting the thickness of each layer,the BES algorithm achieves the best practical solution.The proposed method is implemented using MATLAB and the outcomes show the efficacy of the proposed technique.The transmittance,reflectance,and FWHM using the pro-posed BES are found to be 99.9356%,0.065%,and 1.2 nm respectively.
文摘The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).
文摘The output of the fuzzy set is reduced by one for the defuzzification procedure.It is employed to provide a comprehensible outcome from a fuzzy inference process.This page provides further information about the defuzzifica-tion approach for quadrilateral fuzzy numbers,which may be used to convert them into discrete values.Defuzzification demonstrates how useful fuzzy ranking systems can be.Our major purpose is to develop a new ranking method for gen-eralized quadrilateral fuzzy numbers.The primary objective of the research is to provide a novel approach to the accurate evaluation of various kinds of fuzzy inte-gers.Fuzzy ranking properties are examined.Using the counterexamples of Lee and Chen demonstrates the fallacy of the ranking technique.So,a new approach has been developed for dealing with fuzzy risk analysis,risk management,indus-trial engineering and optimization,medicine,and artificial intelligence problems:the generalized quadrilateral form fuzzy number utilizing centroid methodology.As you can see,the aforementioned scenarios are all amenable to the solution pro-vided by the generalized quadrilateral shape fuzzy number utilizing centroid methodology.It’s laid out in a straightforward manner that’s easy to grasp for everyone.The rating method is explained in detail,along with numerical exam-ples to illustrate it.Last but not least,stability evaluations clarify why the Gener-alized quadrilateral shape fuzzy number obtained by the centroid methodology outperforms other ranking methods.
基金support by the Deanship of Scientific Research at King Khalid University under research grant number(RGP.2/241/43)。
文摘Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business domains.Quality of Service(QoS)has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors,information and usage.Sen-sor data gathering is an efficient solution to collect information from spatially dis-seminated IoT nodes.Reinforcement Learning Mechanism to improve the QoS(RLMQ)and use a Mobile Sink(MS)to minimize the delay in the wireless IoT s proposed in this paper.Here,we use machine learning concepts like Rein-forcement Learning(RL)to improve the QoS and energy efficiency in the Wire-less Sensor Network(WSN).The MS collects the data from the Cluster Head(CH),and the RL incentive values select CH.The incentives value is computed by the QoS parameters such as minimum energy utilization,minimum bandwidth utilization,minimum hop count,and minimum time delay.The MS is used to col-lect the data from CH,thus minimizing the network delay.The sleep and awake scheduling is used for minimizing the CH dead in the WSN.This work is simu-lated,and the results show that the RLMQ scheme performs better than the base-line protocol.Results prove that RLMQ increased the residual energy,throughput and minimized the network delay in the WSN.
文摘The introduction of several small and large-scale industries,malls,shopping complexes,and domestic applications has significantly increased energy consumption.The aim of the work is to simulate a technically viable and economically optimum hybrid power system for residential buildings.The proposed micro-grid model includes four power generators:solar power,wind power,Electricity Board(EB)source,and a Diesel Generator(DG)set,with solar and wind power performing as major sources and the EB supply and DG set serving as backup sources.The core issue in direct current to alternate current conversion is harmonics distortion,a five-stage multilevel inverter is employed with the assistance of an intelligent control system is simulated and the optimum system configuration is estimated to reduce harmonics and improve the power quality.The monthly demand for residential buildings is 13-15 Megawatts.So,almost 433 Kilo-Watts(KW)of electricity is required every day,and if it is used for 8 h per day,50-60 KW of electricity is needed per hour.The overall micro-grid model’s operation and performance are established using MATLAB/SIMULINK software,and simulation results are provided.The simulation results show that the developed system is both cost-effective and environment friendly resulting in yearly cost reductions.
文摘Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.
基金This work was supported partially by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)Support Program(IITP-2024-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.
文摘Synthetic fibers made from nylon or polypropylene have gained application when loose and woven into geo textile form although no information on the matrix’s mechanical performance is obtained so that more understanding of their structural contribution to resist cracking can be determined. This paper presents the results of an experimental investigation to determine the performance characteristics of concrete reinforced with a polypropylene structural fiber. In this investigation “Fiber mesh” brand of fibers manufactured by SL Concrete System, Tennessee, USA and marketed by M/S Millennium Building System, Inc., Ban-galore, India are used. The lengths of the fibers used were 24 mm. Fiber dosages used were 0.9, 1.8, 2.7 kg/m3. A total of three mixtures, one for each fiber dosage were made. A standard slump cone test was conducted on the fresh concrete mix with and without fibers to determine the workability of the mix. The test program included the evaluation of hardened concrete properties such as compressive, split tensile, modulus of rupture and flexural strengths. The increase in compressive strength is about 36.25%, 26.20%, and 23.75% respectively that of plain concrete. This increase in strength was directly proportional to amount of fibers present in the mix. The increase in flexural strength for Mixes I^III is about 21%, 16.6%, and 23% respectively that of plain concrete specimens. An experimental investigation was also made to study the behaviors of reinforced fibers concrete beams (with longitudinal reinforcements) under two-point loading. The deflection and crack patterns were also studied. The improvements in strength and ductility characteristics were discussed.
