Federated Learning on Internet of Things:Extensive and Systematic Review

The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.

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.

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

Salt Tolerance Mechanisms and Approaches:Future Scope of Halotolerant Genes and Rice Landraces

All rice plant developmental stages are severely affected by soil salinity. Salinity-induced ionic and osmotic stresses affect stomata closure and gaseous exchange, and reduce transpiration and the rate of carbon assimilation, and hence decrease plant yield. Understanding the response of rice plants toward salinity stress at the genetic level and developing salt-tolerant varieties are the vital mandates for its effective management. This review described the present status of salt-tolerance achieved in rice by various mechanisms including the ion homeostasis(Na^+/H^+, OsNHX antiporters), compatible organic solutes(glycine betaine and proline), antioxidative genes(OsECS, OsVTE1, OsAPX and OsMSRA4.1), salt responsive regulatory elements(transcription factors, cis-acting elements and miRNAs) and genes ecoding protein kinases(MAPKs, SAPKs and STRKs). Further, the future perspective of developing salt-tolerant varieties lies in exploring halotolerant gene homologs from rice varieties, especially the landraces. Genetic diversity among rice landraces can serve as a valuable resource for future studies toward variety improvement through breeding and genome editing. Further, identification, multiplication, preservation and utilization of biodiversity among landraces are the urgent buffers to be saved as a heritage for future generations to come.

Fabrication characteristics and tensile strength of novel Al2024/SiC/red mud composites processed via stir casting route

The stir casting technique was used to fabricate aluminum2024matrix hybrid composites reinforced with SiC(5%,mass fraction)and red mud(5%-20%,mass fraction)particles.The developed composites were characterized by using scanning electron microscopy(SEM)and electron dispersive spectrum(EDS)techniques.Further,Taguchi’s approach of experimental design was used to examine the tensile strength of the hybrid composites(with minimum number of experiments).It was found that the reinforcing particles were well dispersed and adequately bonded in the hybrid composites.The density and porosity of the hybrid composites were reduced with the increase in reinforcement content.The tensile strength of the composites increased with the increase in the red mud content and the ageing time.The developed model indicated that the red mud content had the highest influence on the tensile strength response followed by the ageing time.Overall,it was found that Al2024/SiC/red mud composites exhibited superior tensile strength(about34%higher)in comparison to the Al2024alloy under optimized conditions.

Medical Image Fusion Based on Anisotropic Diffusion and Non-Subsampled Contourlet Transform

The synthesis of visual information from multiple medical imaging inputs to a single fused image without any loss of detail and distortion is known as multimodal medical image fusion.It improves the quality of biomedical images by preserving detailed features to advance the clinical utility of medical imaging meant for the analysis and treatment of medical disor-ders.This study develops a novel approach to fuse multimodal medical images utilizing anisotropic diffusion(AD)and non-subsampled contourlet transform(NSCT).First,the method employs anisotropic diffusion for decomposing input images to their base and detail layers to coarsely split two features of input images such as structural and textural information.The detail and base layers are further combined utilizing a sum-based fusion rule which maximizes noise filtering contrast level by effectively preserving most of the structural and textural details.NSCT is utilized to further decompose these images into their low and high-frequency coefficients.These coefficients are then combined utilizing the principal component analysis/Karhunen-Loeve(PCA/KL)based fusion rule independently by substantiating eigenfeature reinforcement in the fusion results.An NSCT-based multiresolution analysis is performed on the combined salient feature information and the contrast-enhanced fusion coefficients.Finally,an inverse NSCT is applied to each coef-ficient to produce the final fusion result.Experimental results demonstrate an advantage of the proposed technique using a publicly accessible dataset and conducted comparative studies on three pairs of medical images from different modalities and health.Our approach offers better visual and robust performance with better objective measurements for research development since it excellently preserves significant salient features and precision without producing abnormal information in the case of qualitative and quantitative analysis.

Sustainable remediation of failed slope using helical soil nails

The performance of a helical soil nailed structure is dependent on the installation torque required and the consequent pullout resistance developed.The present research work aims at proposing theoretical models to estimate the required torque during installation of helical soil nails.Moreover,theoretical models are also developed to predict the pullout capacity of single and group of the helical nail for uniform and staggered arrangements.The proposed model predicts the pure-elastic and elastic-plastic pullout behavior of different helical nails.An equation for estimating the capacity-totorque Ratio(Kt)has also been developed for different nail shaft diameters.The results from the proposed models are validated with experimental results obtained from model testing of both single and group of helical nails.The predicted results are also compared for validation with the published literature.The results for installation torque and pullout load depict that the developed models predict values which are in accordance with the experimental results and are also found in good agreement with the published literature.Thus,the proposed models can effectively be used by the filed engineers for estimating the required installation torque and corresponding pullout capacities for single or double plate helical soil nails in cohesionless soil under surcharge pressure range of 0–50k Pa.

Robust Image Watermarking Using LWT and Stochastic Gradient Firefly Algorithm

Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. However, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak Signalto Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. Hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A Stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PSNR and SSIM are obtainedusing the Stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PSNR and SSIM as quality metrics.

Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed promptly.In this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)model.The ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL models.For fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of faults.At the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the work.To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are applied.The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%.

Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques.Computer-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor detected.The application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation tasks.This research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold segmentation.The Honey hook process categorizes lung cancer based on characteristics retrieved using several classifiers.Considering this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using DNN.The proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)classifier.The study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal patients.In contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the network.It is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

Skin cancer is one of the most dangerous cancer.Because of the high melanoma death rate,skin cancer is divided into non-melanoma and melanoma.The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions.Sometimes,pathology and biopsy examinations are required for cancer diagnosis.Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images.With recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature learning.Therefore,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)technique.The major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic images.Primarily,Dull razor approach-related hair removal and median filtering-based noise elimination are performed.Moreover,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic images.Furthermore,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for classification.Finally,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the work.The simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion dataset.The comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.

An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.

Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications

Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases.However,recent image fusion techniques have encountered several challenges,including fusion artifacts,algorithm complexity,and high computing costs.To solve these problems,this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance.First,the method employs a cross-bilateral filter(CBF)that utilizes one image to determine the kernel and the other for filtering,and vice versa,by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing edges.The outputs of CBF are then subtracted from the original images to obtain detailed images.It further proposes to use edge-preserving processing that combines linear lowpass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural properties.These regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate size.The outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the edges.In addition,weight computations are performed using these reconstructed images,and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical details.Numerous standard quality evaluation metrics with complementary properties are used for comparison with existing,well-known algorithms objectively to validate the fusion results.Experimental results from the proposed research article exhibit superior performance compared to other competing techniques in the case of both qualitative and quantitative evaluation.In addition,the proposed method advocates less computational complexity and execution time while improving diagnostic computing accuracy.Nevertheless,due to the lower complexity of the fusion algorithm,the efficiency of fusion methods is high in practical applications.The results reveal that the proposed method exceeds the latest state-of-the-art methods in terms of providing detailed information,edge contour,and overall contrast.

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.

Performance Estimation of Machine Learning Algorithms in the Factor Analysis of COVID-19 Dataset

Novel Coronavirus Disease(COVID-19)is a communicable disease that originated during December 2019,when China officially informed the World Health Organization(WHO)regarding the constellation of cases of the disease in the city of Wuhan.Subsequently,the disease started spreading to the rest of the world.Until this point in time,no specific vaccine or medicine is available for the prevention and cure of the disease.Several research works are being carried out in the fields of medicinal and pharmaceutical sciences aided by data analytics and machine learning in the direction of treatment and early detection of this viral disease.The present report describes the use of machine learning algorithms[Linear and Logistic Regression,Decision Tree(DT),K-Nearest Neighbor(KNN),Support Vector Machine(SVM),and SVM with Grid Search]for the prediction and classification in relation to COVID-19.The data used for experimentation was the COVID-19 dataset acquired from the Center for Systems Science and Engineering(CSSE),Johns Hopkins University(JHU).The assimilated results indicated that the risk period for the patients is 12–14 days,beyond which the probability of survival of the patient may increase.In addition,it was also indicated that the probability of death in COVID cases increases with age.The death probability was found to be higher in males as compared to females.SVM with Grid search methods demonstrated the highest accuracy of approximately 95%,followed by the decision tree algorithm with an accuracy of approximately 94%.The present study and analysis pave a way in the direction of attribute correlation,estimation of survival days,and the prediction of death probability.The findings of the present study clearly indicate that machine learning algorithms have strong capabilities of prediction and classification in relation to COVID-19 as well.

Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

Biomedical image analysis has been exploited considerably by recent technology involvements,carrying about a pattern shift towards‘automation’and‘error free diagnosis’classification methods with markedly improved accurate diagnosis productivity and cost effectiveness.This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images.The proposed model has four convolutional layers,two maxpool layers,one fully connected layer and three dense layers.All the convolutional layers are using the kernel size of 3∗3 whereas the maxpool layer is using the kernel size of 2∗2.The dermoscopy images are taken from the HAM10000 dataset.The proposed model is compared with the three different models of ResNet that are ResNet18,ResNet50 and ResNet101.The models are simulated with 32 batch size and Adadelta optimizer.The proposed model has obtained the best accuracy value of 0.96 whereas the ResNet101 model has obtained 0.90,the ResNet50 has obtained 0.89 and the ResNet18 model has obtained value as 0.86.Therefore,features obtained from the proposed model are more capable for improving the classification performance of multiple skin disease classes.This model can be used for early diagnosis of skin disease and can also act as a second opinion tool for dermatologists.

Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

In Agriculture Sciences, detection of diseases is one of the mostchallenging tasks. The mis-interpretations of plant diseases often lead towrong pesticide selection, resulting in damage of crops. Hence, the automaticrecognition of the diseases at earlier stages is important as well as economicalfor better quality and quantity of fruits. Computer aided detection (CAD)has proven as a supportive tool for disease detection and classification, thusallowing the identification of diseases and reducing the rate of degradationof fruit quality. In this research work, a model based on convolutional neuralnetwork with 19 convolutional layers has been proposed for effective andaccurate classification of Marsonina Coronaria and Apple Scab diseases fromapple leaves. For this, a database of 50,000 images has been acquired bycollecting images of leaves from apple farms of Himachal Pradesh (H.P)and Uttarakhand (India). An augmentation technique has been performedon the dataset to increase the number of images for increasing the accuracy.The performance analysis of the proposed model has been compared with thenew two Convolutional Neural Network (CNN) models having 8 and 9 layersrespectively. The proposed model has also been compared with the standardmachine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimentalresults, it has been observed that the proposed model has outperformed theother CNN based models and machine learning models with an accuracy of99.2%.

Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of clustering and classification.In the first phase,the clustering(k-means and FCM)algorithms were employed independently and the clustering accuracy was evaluated using different computationalmeasures.During the second phase,the non-clustered data obtained from the first phase were preprocessed with TLBO.TLBO was performed using k-means(TLBO-KM)and FCM(TLBO-FCM)(TLBO-KM/FCM)algorithms.The objective function was determined by considering both minimization and maximization criteria.Non-clustered data obtained from the first phase were further utilized and fed as input for threshold optimization.Five benchmark datasets were considered from theUniversity of California,Irvine(UCI)Machine Learning Repository for comparative study and experimentation.These are breast cancer Wisconsin(BCW),Pima Indians Diabetes,Heart-Statlog,Hepatitis,and Cleveland Heart Disease datasets.The combined average accuracy obtained collectively is approximately 99.4%in case of TLBO-KM and 98.6%in case of TLBOFCM.This approach is also capable of finding the dominating attributes.The findings indicate that TLBO-KM/FCM,considering different computational measures,perform well on the non-clustered data where k-means and FCM,if employed independently,fail to provide significant results.Evaluating different feature sets,the TLBO-KM/FCM and SVM(GS)clearly outperformed all other classifiers in terms of sensitivity,specificity and accuracy.TLBOKM/FCM attained the highest average sensitivity(98.7%),highest average specificity(98.4%)and highest average accuracy(99.4%)for 10-fold cross validation with different test data.

Analytical Modeling of Source-to-Drain Tunneling in Nanoscale Silicon MOSFET

Sub-10-nm bulk n-MOSFET （metal-oxide -semiconductor field effect transistor） direct source-to- drain tunneling current density using Wentzel- Krammers-Brillouin 0NKB） transmission tunneling theory has been simulated. The dependence of the source-to-drain tunneling current on channel length and barrier height is examined. Inversion layer quantization, band-gap narrowing, and drain induced barrier lowering effects have been included in the model. It has been observed that the leakage current density increases severely below 4 nm channel lengths, thus putting a limit to the scaling down of the MOSFETs. The results match closely with the numerical results already reported in literatures.

A Secure and Efficient Signature Scheme for IoT in Healthcare

To provide faster access to the treatment of patients,healthcare system can be integrated with Internet of Things to provide prior and timely health services to the patient.There is a huge limitation in the sensing layer as the IoT devices here have low computational power,limited storage and less battery life.So,this huge amount of data needs to be stored on the cloud.The information and the data sensed by these devices is made accessible on the internet from where medical staff,doctors,relatives and family members can access this information.This helps in improving the treatment as well as getting faster medical assistance,tracking of routine activities and health focus of elderly people on frequent basis.However,the data transmission from IoT devices to the cloud faces many security challenges and is vulnerable to different security and privacy threats during the transmission path.The purpose of this research is to design a Certificateless Secured Signature Scheme that will provide a magnificent amount of security during the transmission of data.Certificateless signature,that removes the intricate certificate management and key escrow problem,is one of the practical methods to provide data integrity and identity authentication for the IoT.Experimental result shows that the proposed scheme performs better than the existing certificateless signature schemes in terms of computational cost,encryption and decryption time.This scheme is the best combination of high security and cost efficiency and is further suitable for the resource constrained IoT environment.