Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work,we design a multisensory IoT-based online vitals monitor(hereinafter referred to as the VITALS)to sense four bedside physiological parameters including pulse(heart)rate,body temperature,blood pressure,and periph-eral oxygen saturation.Then,the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery.The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment,a powerful microcontroller,a reliable wireless communication module,and a big data analytics system.It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis.We use Apache Kafka(to gather live data streams from connected sen-sors),Apache Spark(to categorize the patient vitals and notify the medical pro-fessionals while identifying abnormalities in physiological parameters),Hadoop Distributed File System(HDFS)(to archive data streams for further analysis and long-term storage),Spark SQL,Hive and Matplotlib(to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals).In addition,we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely.Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing,data processing,and data transmission mechanisms.To validate the system accuracy,we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor,the Welch Allyn®Spot Check.Our pro-posed system provides improved care solutions,especially for those whose access to care services is limited.

Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world tasks.Given the scope of this area,various techniques are found in the literature.One such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each other.The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed.As more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance issues.Taking a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL tasks.It uses multiple binary action DQNs having shared state and reward.The benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based approaches.The approach can potentially be extended to any type of DQN by forming its ensemble.Conducting extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent techniques.The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.

Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present significant challenges,necessitating efficient data collection and reliable transmission services.This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs.The primary goal is to enhance the reliability of data collection and transmission services,ensuring a comprehensive and practical approach.Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability.Additionally,it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops.These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios.Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes.The evaluation spans varying sensor densities,wireless channel conditions,and packet transmission rates,showcasing the protocol’s superiority in ensuring reliable and efficient data transfer.Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs.By prioritizing fairness,reliability,and efficiency,the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services,thereby offering a valuable contribution to the field of medical event-drivenWSNs.

Investigation of current collapse and recovery time due to deep level defect traps inβ-Ga2O3 HEMT

In this paper,drain current transient characteristics ofβ-Ga2O3 high electron mobility transistor(HEMT)are studied to access current collapse and recovery time due to dynamic population and de-population of deep level traps and interface traps.An approximately 10 min,and 1 h of recovery time to steady-state drain current value is measured under 1 ms of stress on the gate and drain electrodes due to iron(Fe)–dopedβ-Ga2O3 substrate and germanium(Ge)–dopedβ-Ga2O3 epitaxial layer respectively.On-state current lag is more severe due to widely reported defect trap EC–0.82 e V over EC–0.78 e V,-0.75 e V present in Iron(Fe)-dopedβ-Ga2O3 bulk crystals.A negligible amount of current degradation is observed in the latter case due to the trap level at EC–0.98 e V.It is found that occupancy of ionized trap density varied mostly under the gate and gate–source area.This investigation of reversible current collapse phenomenon and assessment of recovery time inβ-Ga2O3 HEMT is carried out through 2 D device simulations using appropriate velocity and charge transport models.This work can further help in the proper characterization ofβ-Ga2O3 devices to understand temporary and permanent device degradation.

Analysis of Radio over Fiber system for mitigating four-wave mixing effect

In this paper,an efficient 8-channel 32Gbps RoF(Radio over Fiber)system incorporating Bessel Filter(8/32 RoFBF)has been demonstrated to reduce the impact of non-linear transmission effects,specifically Four-Wave Mixing(FWM).The simulation results indicate that the proposed 8/32 RoF-BF system provides an optimum result w.r.t.channel spacing(75 GHz),input source power(0 dBm)and number of input channels(8).In comparison with the existing RoF system,the proposed 8/32 RoF-BF system has been validated analytically and it is found that the performance of the proposed system is in close proximity particularly in FWM sideband power reduction of the order of 4 dBm for the 8-channel 32Gbps RoF system.

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.

RF performance evaluation of p-type NiO-pocket based β-Ga_(2)O_(3)/ black phosphorous heterostructure MOSFET

The radio-frequency (RF) performance of the p-type NiO-pocket based β-Ga_(2)O_(3)/black phosphorous heterostructureMOSFET has been evaluated. The key figure of merits (FOMs) for device performance evaluation include the transconductance(gm) gate dependent intrinsic-capacitances (Cgd and Cgs), cutoff frequency (fT), gain bandwidth (GBW) product and output-conductance(gd). Similarly, power-gain (Gp), power added efficiency (PAE), and output power (POUT) are also investigated for largesignalcontinuous-wave (CW) RF performance evaluation. The motive behind the study is to improve the β-Ga_(2)O_(3) MOS deviceperformance along with a reduction in power losses and device associated leakages. To show the applicability of the designeddevice in RF applications, its RF FOMs are analyzed. With the outline characteristics of the ultrathin black phosphorous layer belowthe β-Ga_(2)O_(3) channel region, the proposed device results in 1.09 times improvement in fT, with 0.7 times lower Cgs, and 3.27dB improved GP in comparison to the NiO-GO MOSFET. The results indicate that the designed NiO-GO/BP MOSFET has betterRF performance with improved power gain and low leakages.

Empirical Assessment of Bacillus Calmette-Guérin Vaccine to Combat COVID-19

COVID-19 has become one of the critical health issues globally,which surfaced first in latter part of the year 2019.It is the topmost concern for many nations’governments as the contagious virus started mushrooming over adjacent regions of infected areas.In 1980,a vaccine called Bacillus Calmette-Guérin(BCG)was introduced for preventing tuberculosis and lung cancer.Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory.This paper’s initial research shows that the countries with a longtermcompulsory BCGvaccination system are less affected by COVID-19 than those without a BCG vaccination system.This paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality rates.The paper has tackled numerous analytical challenges to realize the full potential of heterogeneous data.An analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only,such as age groups affected,other diseases,and stringency index.The data of Spain,Portugal,and Germany have been taken for a case study of BCG impact analysis.

Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices

Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise.Moreover,it is widely utilizing for preventing injuries of athletes during a practice session and in few cases,it leads to muscle fatigue.At present,emerging technology like the internet of things(IoT)and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity.In this study,an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram(sEMG)sensor.Normally,the EMG signal is utilized to display muscle activity.Arduino controller,Wi-Fi module,and EMG sensor are utilized in developing the wearable device.The Time-frequency domain spectrum technique is employed for classifying the threemuscle fatigue conditions including meanRMS,mean frequency,etc.A real-time experiment is realized on six different individuals with developed wearable devices and the average RMS value assists to determine the average threshold of recorded data.The threshold level is analyzed by calculating the mean RMS value and concluded three fatigue conditions as>2V:Extensive);1–2V:Moderate,and<1V:relaxed.The warning alarm system was designed in LabVIEW with three color LEDs to indicate the different states of muscle fatigue.Moreover,the device is interfaced with the cloud through the internet provided with a Wi-Fi module embedded in wearable devices.The data available in the cloud server can be utilized for forecasting the frequency of an individual to muscle fatigue.

A Post-Processing Algorithm for Boosting Contrast of MRI Images

Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images.Drastic changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings.To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well.This method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression.After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are computed.Enhanced grey level values are computed from the resultant cumulative histograms.The performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.

Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.

Dealing with the Class Imbalance Problem in the Detection of Fake Job Descriptions

In recent years,the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age.Identifying fraud in job descriptions can help jobseekers to avoid many of the risks of job hunting.However,the problem of detecting fake job descriptions comes up against the problem of class imbalance when the number of genuine jobs exceeds the number of fake jobs.This causes a reduction in the predictability and performance of traditional machine learning models.We therefore present an efficient framework that uses an oversampling technique called FJD-OT(Fake Job Description Detection Using Oversampling Techniques)to improve the predictability of detecting fake job descriptions.In the proposed framework,we apply several techniques including the removal of stop words and the use of a tokenizer to preprocess the text data in the first module.We then use a bag of words in combination with the term frequency-inverse document frequency(TF-IDF)approach to extract the features from the text data to create the feature dataset in the second module.Next,our framework applies k-fold cross-validation,a commonly used technique to test the effectiveness of machine learning models,that splits the experimental dataset[the Employment Scam Aegean(ESA)dataset in our study]into training and test sets for evaluation.The training set is passed through the third module,an oversampling module in which the SVMSMOTE method is used to balance data before training the classifiers in the last module.The experimental results indicate that the proposed approach significantly improves the predictability of fake job description detection on the ESA dataset based on several popular performance metrics.

Measurement Analysis of Specific Absorption Rate in Human Body Exposed to a Base Station Antenna by Using Finite Difference Time Domain Techniques

The system analysis of specific absorption rate(SAR)in human body ex­posed to a base station antenna by using finite difference time domain tech­niques was presented in this research works.The objectives of this work are to evaluate the knowledge and awareness about SAR among human body and mobile base station.The paper investigates the electromagnetic wave absorption inside a human body.The human body has been identified us­ing dataset based on 2D object considering different electrical parameters.The SAR convinced inside the human body model exposed to a radiating base station antenna(BSA)has been considered for multiple numbers of carrier frequencies and input power of 20 W/carrier at GSM 900 band.The distance(R)of human body from BSA is varied in the range of 0.1 m to 5.0 m.For the number of carrier frequency equal to one and R=0.1 m,the concentrated value of whole-body average SAR obtained by FDTD technique is found to be 0.68 W/kg which decreases either with increase of R or decrease of number of carrier frequencies.Safety distance for general public is found to be 1.5 m for number of carrier frequencies equal to one.The performance accuracy of this analysis meets the high level condition by comparing with the relevant system development in recent time.

Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

A Thorough Investigation on Image Forgery Detection

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information.It cannot be easy to distinguish themodified region fromthe original image in somecircumstances.The demand for authenticity and the integrity of the image drive the detection of a fabricated image.There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files,including re-sampling or copy-moving.This work presents a high-level view of the forensics of digital images and their possible detection approaches.This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness.These methods have identified forgery and its type and compared it with state of the art.This work will help us to find the best forgery detection technique based on the different environments.It also shows the current issues in other methods,which can help researchers find future scope for further research in this field.

Energy Efficient Load Balancing and Routing Using Multi-Objective Based Algorithm in WSN

In wireless sensor network(WSN),the gateways which are placed far away from the base station(BS)forward the collected data to the BS through the gateways which are nearer to the BS.This leads to more energy consumption because the gateways nearer to the BS manages heavy traffic load.So,to over-come this issue,loads around the gateways are to be balanced by presenting energy efficient clustering approach.Besides,to enhance the lifetime of the net-work,optimal routing path is to be established between the source node and BS.For energy efficient load balancing and routing,multi objective based beetle swarm optimization(BSO)algorithm is presented in this paper.Using this algo-rithm,optimal clustering and routing are performed depend on the objective func-tions routingfitness and clusteringfitness.This approach leads to decrease the power consumption.Simulation results show that the performance of the pro-posed BSO based clustering and routing scheme attains better results than that of the existing algorithms in terms of energy consumption,delivery ratio,through-put and network lifetime.Namely,the proposed scheme increases throughput to 72%and network lifetime to 37%as well as it reduces delay to 37%than the existing optimization algorithms based clustering and routing schemes.

Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI

Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,temperature,heart rate variability(HRV),humidity,and blood pressure are used to assess stress levels with the use of instruments.With the development of sensor technology and wireless connectivity,people around the world are adopting and using smart devices.In this study,a bio signal detection device with Internet of Things(IoT)capability with a galvanic skin reaction(GSR)sensor is proposed and built for real-time stress monitoring.The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system,physical stress is created on 10 different participants with three distinct tasks namely reading,visualizing the timer clock,and watching videos.MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e.,relaxed for<1.75 volts;Normal:between 1.75 and 1.44 volts and stressed:>1.44 volts.In addition,LabVIEW is used as a data acquisition system,and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.