Load Balancing Algorithm for Migrating Switches in Software-Dened Vehicular Networks

In Software-Dened Networks(SDN),the divergence of the control interface from the data plane provides a unique platform to develop a programmable and exible network.A single controller,due to heavy load trafc triggered by different intelligent devices can not handle due to it’s restricted capability.To manage this,it is necessary to implement multiple controllers on the control plane to achieve quality network performance and robustness.The ow of data through the multiple controllers also varies,resulting in an unequal distribution of load between different controllers.One major drawback of the multiple controllers is their constant conguration of the mapping of the switch-controller,quickly allowing unequal distribution of load between controllers.To overcome this drawback,Software-Dened Vehicular Networking(SDVN)has evolved as a congurable and scalable network,that has quickly achieved attraction in wireless communications from research groups,businesses,and industries administration.In this paper,we have proposed a load balancing algorithm based on latency for multiple SDN controllers.It acknowledges the evolving characteristics of real-time latency vs.controller loads.By choosing the required latency and resolving multiple overloads simultaneously,our proposed algorithm solves the loadbalancing problems with multiple overloaded controllers in the SDN control plane.In addition to the migration,our algorithm has improved 25%latency as compared to the existing algorithms.

Plant Disease Diagnosis and Image Classification Using Deep Learning

Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem.Modern farming employs technology to improve productivity.Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity.Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost,approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert’s opinion.Deep learning-based computer vision techniques like Convolutional Neural Network(CNN)and traditional machine learning-based image classification approaches are being applied to identify plant diseases.In this paper,the CNN model is proposed for the classification of rice and potato plant leaf diseases.Rice leaves are diagnosed with bacterial blight,blast,brown spot and tungro diseases.Potato leaf images are classified into three classes:healthy leaves,early blight and late blight diseases.Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study.The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58%accuracy and potato leaves with 97.66%accuracy.The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Decision Tree and Random Forest.

A Formal Testing Model for Operating Room Control System Using Internet of Things

Technological advances in recent years have significantly changed the way an operating room works.This work aims to create a platformto solve the problems of operating room occupancy and prepare the rooms with an environment that is favorable for all operations.Using this system,a doctor can control all operation rooms,especially before an operation,and monitor their temperature and humidity to prepare for the operation.Also,in the event of a problem,an alert is sent to the nurse responsible for the room and medical stuff so that the problem can be resolved.The platformis tested using a Raspberry PI card and sensors.The sensors are connected to a cloud layer that collects and analyzes the temperature and humidity values obtained from the environment during an operation.The result of experimentations is visualized through a web application and an Android application.The platform also considers the security aspects such as authorization to access application functionalities for the Web and the mobile applications.We can also test and evaluate the system’s existing problems and vulnerabilities using the IEEE and owasp IoT standards.Finally,the proposed framework is extended with a model based testing technique that may be adopted for validating the security aspects.

Role of Fuzzy Approach towards Fault Detection for Distributed Components

Component-based software development is rapidly introducing numerous new paradigms and possibilities to deliver highly customized software in a distributed environment.Among other communication,teamwork,and coordination problems in global software development,the detection of faults is seen as the key challenge.Thus,there is a need to ensure the reliability of component-based applications requirements.Distributed device detection faults applied to tracked components from various sources and failed to keep track of all the large number of components from different locations.In this study,we propose an approach for fault detection from componentbased systems requirements using the fuzzy logic approach and historical information during acceptance testing.This approach identified error-prone components selection for test case extraction and for prioritization of test cases to validate components in acceptance testing.For the evaluation,we used empirical study,and results depicted that the proposed approach significantly outperforms in component selection and acceptance testing.The comparison to the conventional procedures,i.e.,requirement criteria,and communication coverage criteria without irrelevancy and redundancy successfully outperform other procedures.Consequently,the F-measures of the proposed approach define the accurate selection of components,and faults identification increases in components using the proposed approach were higher(i.e.,more than 80 percent)than requirement criteria,and code coverage criteria procedures(i.e.,less than 80 percent),respectively.Similarly,the rate of fault detection in the proposed approach increases,i.e.,92.80 compared to existing methods i.e.,less than 80 percent.The proposed approach will provide a comprehensive guideline and roadmap for practitioners and researchers.

On the Detection of COVID-19 from Chest X-Ray Images Using CNN-Based Transfer Learning

Coronavirus disease(COVID-19)is an extremely infectious disease and possibly causes acute respiratory distress or in severe cases may lead to death.There has already been some research in dealing with coronavirus using machine learning algorithms,but few have presented a truly comprehensive view.In this research,we show how convolutional neural network(CNN)can be useful to detect COVID-19 using chest X-ray images.We leverage the CNN-based pre-trained models as feature extractors to substantiate transfer learning and add our own classifier in detecting COVID-19.In this regard,we evaluate performance of five different pre-trained models with fine-tuning the weights from some of the top layers.We also develop an ensemble model where the predictions from all chosen pre-trained models are combined to generate a single output.The models are evaluated through 5-fold cross validation using two publicly available data repositories containing healthy and infected(both COVID-19 and other pneumonia)chest X-ray images.We also leverage two different visualization techniques to observe how efficiently the models extract important features related to the detection of COVID-19 patients.The models show high degree of accuracy,precision,and sensitivity.We believe that the models will aid medical professionals with improved and faster patient screening and pave a way to further COVID-19 research.

SVM and KNN Based CNN Architectures for Plant Classification

Automatic plant classification through plant leaf is a classical problem in Computer Vision.Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like.Many efforts are made to automate plant classification using plant leaf,plant flower,bark,or stem.After much effort,it has been proven that leaf is the most reliable source for plant classification.But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations,like sizes,textures,shapes,and venation.Therefore,it is required to normalize all plant leaves into the same size to get better performance.Convolutional Neural Networks(CNN)provides a fair amount of accuracy when leaves are classified using this approach.But the performance can be improved by classifying using the traditional approach after applying CNN.In this paper,two approaches,namely CNN+Support Vector Machine(SVM)and CNN+K-Nearest Neighbors(kNN)used on 3 datasets,namely LeafSnap dataset,Flavia Dataset,and MalayaKew Dataset.The datasets are augmented to take care all the possibilities.The assessments and correlations of the predetermined feature extractor models are given.CNN+kNN managed to reach maximum accuracy of 99.5%,97.4%,and 80.04%,respectively,in the three datasets.

Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye infections.Air pollution also poses serious issues to the planet.Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions.Thus,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions.The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks.Localization is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not optimal.This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities.These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization.The dataset is divided into training and testing parts based on 10 cross-validations.The evaluation on predicting the air pollutant for localization is performed with the training dataset.Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSN

The Internet of Things(IoT)is gaining attention because of its broad applicability,especially by integrating smart devices for massive communication during sensing tasks.IoT-assisted Wireless Sensor Networks(WSN)are suitable for various applications like industrial monitoring,agriculture,and transportation.In this regard,routing is challenging to nd an efcient path using smart devices for transmitting the packets towards big data repositories while ensuring efcient energy utilization.This paper presents the Robust Cluster Based Routing Protocol(RCBRP)to identify the routing paths where less energy is consumed to enhances the network lifespan.The scheme is presented in six phases to explore ow and communication.We propose the two algorithms:(i)energy-efcient clustering and routing algorithm and (ii)distance and energy consumption calculation algorithm.The scheme consumes less energy and balances the load by clustering the smart devices.Our work is validated through extensive simulation using Matlab.Results elucidate the dominance of the proposed scheme is compared to counterparts in terms of energy consumption,the number of packets received at BS and the number of active and dead nodes.In the future,we shall consider edge computing to analyze the performance of robust clustering.

Automated Identification Algorithm Using CNN for Computer Vision in Smart Refrigerators

Machine Learning has evolved with a variety of algorithms to enable state-of-the-art computer vision applications.In particular the need for automating the process of real-time food item identification,there is a huge surge of research so as to make smarter refrigerators.According to a survey by the Food and Agriculture Organization of the United Nations(FAO),it has been found that 1.3 billion tons of food is wasted by consumers around the world due to either food spoilage or expiry and a large amount of food is wasted from homes and restaurants itself.Smart refrigerators have been very successful in playing a pivotal role in mitigating this problem of food wastage.But a major issue is the high cost of available smart refrigerators and the lack of accurate design algorithms which can help achieve computer vision in any ordinary refrigerator.To address these issues,this work proposes an automated identification algorithm for computer vision in smart refrigerators using InceptionV3 and MobileNet Convolutional Neural Network(CNN)architectures.The designed module and algorithm have been elaborated in detail and are considerably evaluated for its accuracy using test images on standard fruits and vegetable datasets.A total of eight test cases are considered with accuracy and training time as the performance metric.In the end,real-time testing results are also presented which validates the system’s performance.

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social media.Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group.The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools.An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,etc.In addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral tweets.In this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor.To distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

Improved Video Steganography with Dual Cover Medium,DNA and Complex Frames

The most valuable resource on the planet is no longer oil,but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic approaches.The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility.In the previous approach,DNA was used only for frame selection.If this DNA is compromised,then our frames with the hidden and unencrypted data will be exposed.Moreover the frame selected in this way were random frames,and no consideration was made to the contents of frames.Hiding data in this way introduces visible artifacts in video.In the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames.Using chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random locations.Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance.Further,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved results.The proposed methodology has been implemented in Matlab.

Unmanned Aerial Vehicle Assisted Forest Fire Detection Using Deep Convolutional Neural Network

Disasters may occur at any time and place without little to no presage in advance.With the development of surveillance and forecasting systems,it is now possible to forebode the most life-threatening and formidable disasters.However,forestfires are among the ones that are still hard to anticipate beforehand,and the technologies to detect and plot their possible courses are still in development.Unmanned Aerial Vehicle(UAV)image-basedfire detection systems can be a viable solution to this problem.However,these automatic systems use advanced deep learning and image processing algorithms at their core and can be tuned to provide accurate outcomes.Therefore,this article proposed a forestfire detection method based on a Convolutional Neural Network(CNN)architecture using a newfire detection dataset.Notably,our method also uses separable convolution layers(requiring less computational resources)for immediatefire detection and typical convolution layers.Thus,making it suitable for real-time applications.Consequently,after being trained on the dataset,experimental results show that the method can identify forestfires within images with a 97.63%accuracy,98.00%F1 Score,and 80%Kappa.Hence,if deployed in practical circumstances,this identification method can be used as an assistive tool to detectfire outbreaks,allowing the authorities to respond quickly and deploy preventive measures to minimize damage.

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

A Secure Multi-factor Authentication Protocol for Healthcare Services Using Cloud-based SDN

Cloud-based SDN(Software Defined Network)integration offers new kinds of agility,flexibility,automation,and speed in the network.Enterprises and Cloud providers both leverage the benefits as networks can be configured and optimized based on the application requirement.The integration of cloud and SDN paradigms has played an indispensable role in improving ubiquitous health care services.It has improved the real-time monitoring of patients by medical practitioners.Patients’data get stored at the central server on the cloud from where it is available to medical practitioners in no time.The centralisation of data on the server makes it more vulnerable to malicious attacks and causes a major threat to patients’privacy.In recent days,several schemes have been proposed to ensure the safety of patients’data.But most of the techniques still lack the practical implementation and safety of data.In this paper,a secure multi-factor authentication protocol using a hash function has been proposed.BAN(Body Area Network)logic has been used to formally analyse the proposed scheme and ensure that no unauthenticated user can steal sensitivepatient information.Security Protocol Animator(SPAN)–Automated Validation of Internet Security Protocols and Applications(AVISPA)tool has been used for simulation.The results prove that the proposed scheme ensures secure access to the database in terms of spoofing and identification.Performance comparisons of the proposed scheme with other related historical schemes regarding time complexity,computation cost which accounts to only 423 ms in proposed,and security parameters such as identification and spoofing prove its efficiency.

A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019.It affects the whole world through personto-person communication.This virus spreads by the droplets of coughs and sneezing,which are quickly falling over the surface.Therefore,anyone can get easily affected by breathing in the vicinity of the COVID-19 patient.Currently,vaccine for the disease is under clinical investigation in different pharmaceutical companies.Until now,multiple medical companies have delivered health monitoring kits.However,a wireless body area network(WBAN)is a healthcare system that consists of nano sensors used to detect the real-time health condition of the patient.The proposed approach delineates is to fill a gap between recent technology trends and healthcare structure.If COVID-19 affected patient is monitored through WBAN sensors and network,a physician or a doctor can guide the patient at the right timewith the correct possible decision.This scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein,a Monte Carlo algorithm guided protocol is developed to probe a secured cipher output.Security cipher helps to avoid wireless network issues like packet loss,network attacks,network interference,and routing problems.Monte Carlo based covid-19 detection technique gives 90%better results in terms of time complexity,performance,and efficiency.Results indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity,performance,and efficiency and thus,is advocated as a significant application for lessening hospital expenses.

SmartCrawler:A Three-Stage Ranking Based Web Crawler for Harvesting Hidden Web Sources

Web crawlers have evolved from performing a meagre task of collecting statistics,security testing,web indexing and numerous other examples.The size and dynamism of the web are making crawling an interesting and challenging task.Researchers have tackled various issues and challenges related to web crawling.One such issue is efficiently discovering hidden web data.Web crawler’s inability to work with form-based data,lack of benchmarks and standards for both performance measures and datasets for evaluation of the web crawlers make it still an immature research domain.The applications like vertical portals and data integration require hidden web crawling.Most of the existing methods are based on returning top k matches that makes exhaustive crawling difficult.The documents which are ranked high will be returned multiple times.The low ranked documents have slim chances of being retrieved.Discovering the hidden web sources and ranking them based on relevance is a core component of hidden web crawlers.The problem of ranking bias,heuristic approach and saturation of ranking algorithm led to low coverage.This research represents an enhanced ranking algorithm based on the triplet formula for prioritizing hidden websites to increase the coverage of the hidden web crawler.

Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

Comparative Design and Study of A 60 GHz Antenna for Body-Centric Wireless Communications

In this paper performance of three different designs of a 60 GHz highgain antenna for body-centric communication has been evaluated. The basic structure of the antenna is a slotted patch consisting of a rectangular ring radiator withpassive radiators inside. The variation of the design was done by changing theshape of these passive radiators. For free space performance, two types of excitations were used—waveguide port and a coaxial probe. The coaxial probe signifi-cantly improved both the bandwidth and radiation efficiency. The centerfrequency of all the designs was close to 60 GHz with a bandwidth of more than5 GHz. These designs achieved a maximum gain of 8.47 dB, 10 dB, and 9.73 dBwhile the radiation efficiency was around 94%. For body-centric applications,these antennas were simulated at two different distances from a human torsophantom using a coaxial probe. The torso phantom was modeled by taking threelayers of the human body—skin, fat, and muscle. Millimeter waves have lowpenetration depth in the human body as a result antenna performance is lessaffected. A negligible shift of return loss curves was observed. Radiation efficiencies dropped at the closest distance to the phantom and at the furthest distance, theefficiencies increased to free space values. On the three layers human body phantom, all three different antenna designs show directive radiation patterns towards offthe body. All three designs exhibited similar results in terms of center frequency andefficiency but varied slightly by either having better bandwidth or maximum gain.

Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

Autism Spectrum Disorder (ASD) is a developmental disorderwhose symptoms become noticeable in early years of the age though it canbe present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completelybut can be reduced if detected early. An early diagnosis is hampered by thevariation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergenceof deep learning approaches in various fields, medical experts can be assistedin early diagnosis of ASD. It is very difficult for a practitioner to identifyand concentrate on the major feature’s leading to the accurate prediction ofthe ASD and this arises the need for having an automated approach. Also,presence of different symptoms of ASD traits amongst toddlers directs tothe creation of a large feature dataset. In this study, we propose a hybridapproach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and preciseprediction of ASD. The proposed framework gives more accurate predictionalong with the recommendations of predicted results which will be a vital aidclinically for better and early prediction of ASD traits amongst toddlers.

Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm

CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.