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In nanoregime, heterojunction diodes have efficient applications in design and fabrication of sensors. Heterojunction diodes are formed by fusing two dissimilar semiconductors. Based on the combination of the materials used, these heterostructures find applications in bioelectronics and miniaturized sensors. In the literature, many different combinations are prevailing –GaN/AlGaN, Au/ZnO, NiO/ZnO. In this paper, a heterojunction diode is designed using Si/ ZnO combination and for the first time,the device is discovered to be used as pressure sensors.. The IV characteristics, energy band profile and CV curves of Si/ZnO is studied, analyzed and validated using TCAD-ATLAS software. This paper also proves that the performance of the device fits well for pressure sensing applications using COMSOL software.

The promising capability of Triple Material Surrounding Gate Junctionless Tunnel FET (TMSG – JL – TFET)based 6T SRAM structure is demonstrated by employing Germanium (Ge)and High-K gate dielectric material. The high – K insulation guarantees the proposed device to be used in low leakage memory systems. The corresponding analytical model is developed to extract various device parameters such as surface potential, electric field and threshold voltage. The results yield minimization of hot carrier effects at the drain end, when compared to conventional Silicon (Si) based tunnel FETs (TFETs). Further, the ambipolar characteristics of the proposed device is explored and 6T Ge – TMS – SG – JL – TFET based SRAM design is proposed. The results are compared with CMOS based SRAM and the analytical model presented is validated using 3D-TCAD ATLAS simulation, which ensures the accuracy and exactness of the developed model.

Aims: The paper has introduced an algorithmic modification of M.L.Das's previous work done of " IoT Mutual Authentication". Background: IoT has proven that if there exists a thing on the earth then it is bound to be connected to the internet to tell its feature on its own. IoT plays a remarkable role in all aspects of our daily lives it covers entertainment, sports, healthcare, education, security, automobiles, industrial as well as home appliances and many more real time applications. To ease our everyday activities, it reinforcing the way people interact with their surroundings. This holistic view brings some major concerns in terms of security and privacy. Objective: The objective of the work is to increase security and protect the algorithm from various attackers so to use with real time application. Methods: In this we have used XoR (Exclusive OR) operation and because of this we can protect the algorithm from DoS attacks, bypass attack, intruder attack and allow user to change password. Conclusion: The proposed work has secured all the connected IoT devices to work inde-pendently and secured because every device has to verify at each step in the IoT system before initiating its operation.

In this study, we present an ambipolar conduction and RF stability performance for a Germanium Source Dual Halo Dual Dielectric Triple Material Surrounding Gate Tunnel FET (Ge(SRC)-DH-DD-TM-SG-TFET). The energy band diagram of the device will be derived using the Stern Stability Factor. The minimum tunneling length is expressed using low-k Silicon dioxide (SiO2) and high-k Hafnium oxide (HfO2) as dual dielectric material. Then the band to band tunneling (BTBT) rate tunneling generation rate will be derived using Kane’s model. In terms of frequency efficiency the proposed device with SiO2/HfO2 as gate dielectric is good enough to attain high cut-off frequency (fT) of 94.22 GHz is observed at drain to source voltage of VDS = 1 V. Efforts have also been made to properly assess the potential of the two halo doped regions regions. An expression for transconductance is proposed and the model is validated using 3-D Silvaco Atlas TCAD device simulator. In addition, to demonstrate the advantage of using Ge(SRC)-DH-DD-TM-SG-TFET dual dielectric halo doping in circuit applications, the transient response of the conventional TFET germanium source is compared with the low and high-k dielectric halo doping Ge(SRC) TFET.

Focused ultrasound has been widely acclaimed to be and used by many to generate highly localized heating to treat cysts and cancerous tumours through a process called Magnetic Resonance guided Focused Ultrasound (MRgFUS) or High Intensity Focused Ultrasound (HIFU). These procedures guided by Magnetic Resonance Imaging (MRI) generally works on lower frequencies than medical diagnostic ultrasound with significantly higher energies. By tweaking, the parameters of the focused ultrasound treatment can induce the immune response through enhanced dendritic cell (DCs) infiltration and activation. Dendritic cells can capture and present activating signals delivered by necrotic tumour cells that remain in-situ after HIFU treatment. The other beneficial side effects of HIFU leads to higher CD4+/CD8+ ratio and increasing CD3+, CD4+, and CD8+ lymphocytes in the circulation. HIFU based therapy ultrasound shown to potentially boost the body’s immune response thus combating the local recurrence of the targeted tumour.

A simple physics-based three-dimensional (3-D) analytical model for AlInSb/AlSb/InSb double-gate high-electron-mobility transistors (DG-HEMTs) is presented. The model accurately predicts the short-channel effects (SCEs) in the channel region for various device dimensions, viz. channel length and width, by solving the three-dimensional Poisson equation. The effects of the barrier layer (AlInSb) thickness and the high doping concentration on the threshold voltage are also considered. Analytical expressions for the surface potential and threshold voltage are derived, and the analytical results closely match those obtained from Sentaurus technology computer-aided design (TCAD) simulations. The drain current and transconductance of the AlInSb/AlSb/InSb double-gate HEMT device are compared with experimental data obtained for a quantum-well field-effect transistor (QWFET). The proposed AlInSb/AlSb/InSb double-gate HEMT shows excellent properties for use in high-speed and low-power applications.

The Gaussian-doped junctionless FinFET (GD-JLFinFET) is investigated for radiofrequency (RF)/analog applications. The performance of the device is analyzed by computing the transconductance (gm), transconductance-to-drain current ratio (gm/Ids), cutoff frequency (fT), and maximum oscillation frequency (fmax). The stability factor (K) is analyzed based on the Y-parameters, and the linearity of the proposed device is studied based on the gm3, second-order voltage intercept point (VIP2), third-order voltage intercept point (VIP3), third-order intermodulation intercept point (IIP3), third-order intermodulation distortion (IMD3), and 1-dB compression point. The impact of device geometrical parameters such as the fin width, fin height, channel length, voltage biases, and temperature on the RF/stability characteristics is also studied. The proposed device exhibits fT = 2.96 THz and fmax = 9.13 THz, compared with the values of fT = 0.97 THz and fmax = 5.88 THz for the uniformly doped junctionless FinFET (UD-JLFinFET). It is observed that proposed device exhibits better RF/analog characteristics.

In this article, a two dimensional (2-D) threshold voltage modeling based gate and channel engineering are developed analytically for Dual Halo Gate Stacked Triple Material Dual Gate Tunnel FET (DH-GS-TM-DG-TFET) with effective surface charge. The model is derived by solving the 2-D Poisson equation in Silicon graded channel region using suitable boundary conditions. The proposed model incorporates the effects of various device parameters such as channel potential, electric field, DIBL, threshold voltage roll-off and drain current. Also, the fringing capacitance characteristics of the proposed DH-GS-TM-DG-TFET demonstrate superior performance over Triple material double gate and Single material double gate TFET structures. It is evident that the proposed device structure DH-GS-TM-DGTFET provides poor outflow current IOFF (10−16A/μm), and remarkable betterment in ON current (10−6A/μm). Moreover, the ION/IOFF ratio is 1010. To validate the robustness of model, the numerical results are compared with those obtained using Sentaurus TCAD.

This paper investigates the RF Stability performance of the Germanium Source Dual Halo Dual Dielectric Triple Material Surrounding Gate Tunnel FET Ge(SRC)-DH-DD-TM-SG-TFET using 3D - Silvaco Atlas TCAD device simulator. The impact of the geometrical parameter, high-k dielectric material and bias conditions on the key figure of merit (FoM) like Transconductance (gm), Gate capacitance (Cgg) and RF parameters like Stern Stability Factor (K), Critical Frequency (fk) are investigated. The analytical model provides the relation between fk and small signal parameters which provide guidelines for optimizing the device geometrical parameter. The results show improvement in ION current, gm, ft and fk for the optimized device structure. The optimized Ge(SRC)-DH-DD-TM-SG-TFET exhibits fk of 75.0 GHz.

Electronic health records (EHRs) are health information of patients that are saved digitally in a network. Various opportunities to enhance patient care, performance measures in clinical practice and contribute to clinical research in the future are provided by EHRs. The schemes used to store EHRs have been very insecure in the present era of smart cities and homes. The data can be easily breached by hackers and unauthorized external parties. Also, the data is not accessible to patients and care providers. These schemes are unable to create a balance between data security and data accessibility. But blockchain can resolve these issues. Blockchain creates a ledger system that is immutable and allows the transactions to take place in a decentralized manner. The three main features of blockchain technology - Security, Decentralization, and Transparency make any application built using it secure and not accessible by unauthorized parties. The manipulation of data is almost impossible to do in a blockchain network. In this project, we propose a system to implement EHRs using blockchain technology and make EHRs more secure and private. The blockchain technology will keep control over access to information using its cryptographic techniques and decentralization. It will also maintain the balance between data privacy and data accessibility. Our main objective of this project is the framing of data privacy and security issues in electronic healthcare

This paper proposes a 2-D analytical model developed for Double Gate Junctionless Transistor with a SiO2/HfO2 stacked oxide structure. The model is solved by Poisson’s equation using the variable separation method. The proposed model gives analytical expressions for electrostatic potential distribution, threshold voltage and drain current with the effects of depletion regions at source/drain side. Furthermore, the potential and drain current models are used to evaluate the Short Channel Effects (SCEs) of the proposed device. The electrical characteristics and SCEs are analyzed by different possible definitions of channel length, silicon thickness, equivalent oxide thickness, and depletion length variations. The developed model results are validated through comparison with Sentarus TCAD simulator results. In addition, the proposed device is also studied for the digital circuit performance of CMOS inverter circuit by the voltage transfer characteristics, transient analysis, and AC small signal analysis.

The technological advancements make changes during availability of knowledge in a huge way. As the volume of data is increasing exponentially, there is a need for better management of data to research and industry. This data, referred to as Big Data, is now employed by various organizations to extract valuable information which may reanalyzed computationally to reveal patterns, trends and associations revealing the human interaction and behavior for making various industrial decisions But the data must be optimized, integrated, secured and visualized to make any effective decision. Analyzing of the large volume of data is not beneficial always unless it is analyzed properly. The existing techniques are insufficient to analyze the large Data and identify the frequent services accessed by the cloud users. Various services can be integrated to provide a better environment to work in emergency cases pretty earlier. Using these services, people become widely vulnerable to exposure. The data is large and provides an insight in to future predictions, which could definitely prevent maximum medical cases from happening. But without big data analytics techniques and therefore the Hadoop cluster, this data remains useless. Through this paper, we'll explain how real time data may be useful to research and predict severe Index Terms - Big Data, Medical Big Data mining, EHR, EMR, Hadoop, Hbase, Mapreduce, HDFS, Hive, Cassandra

An improved subthreshold analytical model of Germanium source Dual Halo Dual Dielectric Triple Material Surrounding Gate Tunnel FET Ge(SRC)-DH-DD-TM-SG-TFET is proposed. The dielectric gate oxide structure is comprised of Silicon-dioxide and Hafnium oxide. The high-K dielectric materials overcomes the Short Channel Effects caused by ultrathin silicon devices. The subthreshold analysis is carried out by solving a 2-D Poisson’s equation using the parabolic approximation method. The electrical characteristics of Ge(SRC)-DH-DD-TM-SG-Tunnel FET are analyzed using a 3-D Sentaurus TCAD device simulator and compared with the silicon based single halo and triple material surrounding gate TFET structures. The proposed model shows a lower ambipolar current and a better ION/IOFF ratio of 106. Moreover, the influence of germanium/silicon in dual dielectric materials has reduced the tunneling barrier width and the ON current (10−4 A/μm) of the proposed device and improved at the level of CMOS transistors.

An accurate two dimensional subthreshold modeling of Germanium based Dual Halo Gate stacked Triple Material Surrounding Gate (Ge-DH-GS-TM-SG) tunnel field effect transistor is proposed for the first time in this paper. The model is solved by Poisson's equation using parabolic approximation method. The developed model gives analytical expressions for surface potential, electric field, threshold voltage, drain current, trans conductance to drain current ratio, subthreshold current and subthreshold swing are used to evaluate the short channel effects. The subthreshold swing of the proposed Ge-DH-GS-TM-SG-TFET is compared with Silicon based Single Halo Triple Material Surrounding gate TFET(Si-SH-TM-SG-TFET)and Triple Material Surrounding Gate TFET(TM-SG TFET). It is noticable that the proposed device Ge-DH-GS-TM-SG-TFET structure provides. poor outflow current IOFF (10−15A/μm), and remarkable improvement in ON current. (10−5A/μm).Moreover, the ION/IOFF ratio is 108. The developed model results are validated through comparison with 3-D sentaurus TCAD simulator results.

The prototype of Fe doped Dual Material Gate AlGaN/AlN/GaN High Electron Mobility Transistors(HEMTs) developed by a Finite Difference Method(FDM) is addressed in this paper. The equations are illustrated in a way that the dual material gate disintegrates into two distinct single material gates by discreting one 2-D equation into two isolated equations. The boundary conditions conclude in the clubbed model of two 1D equations. The work functions of two metal gates, their length difference and applied drain voltage, the proposed model estimates the Channel potential, Electric field, Threshold voltage and Drain current. A momentous increase of the drain current is witnessed when the δ-doping is integrated in the AlN layer. These features are also validated by TCAD simulations.

The objective of this paper is to measure the influence of user to other user’s opinion in online medical social forum (http://www.medhelp.org/) or twitter about a disease/health condition/surgery/medications and so on. And to help online healthcare community by identifying positive/negative influences. Positive influence on medical condition/treatment/drugs may be favorable to aggrieved people while adverse influence may cause undesirable impacts to other peoples of the same community. Therefore, this paper aims to assess people’s opinion and identify influential users in online healthcare forum using conversation content and network-based properties such as reply relationship and response immediacy which acts as an explicit and implicit measure of collaboration between users. The result of the proposed scheme is evaluated based on two online benchmark medical databases PubMed and WebMD. The experimental results show that the accuracy of predicting influential users is reasonably good in terms of Similarity Index measured between contents written by Influential users and contents available in medical database.

In this paper, an analytical model for modified Surrounding Gate Tunnel FET with gate stack engineering and different gate metals has been developed. Further, considering the scaling advantageous of Gate stack engineering and high degree performance of dual material engineering, the both has been integrated into a novel structure known as Surrounding Gate (SG) Tunnel FET with stacked oxide SiO2/high-k and dual material (DM) has been proposed. The two dimensional (2D) potential at the surface and electric field mathematical models for the DMSG TFET are developed by solving 2D Poisson's equation with matching device boundary conditions. Based on the Kane's formula, mathematical expression for the band-to-band (BTB) tunneling generation rate is derived and then used to calculate the drain current. The impact on the proposed device performance due to the variation of different device parameters has also been studied. It has been found from the presented results that the ON current of the DMSG TFET with stack is 10-6A, OFF current is 10-13A and ON/OFF ratio is 107. The mathematical results have been verified using the simulated results obtained from TCAD, a 3-D device simulator from ATLAS.

Several classifiers are prevalent which act as a major drive for almost all supervised machine learning applications. These classifiers, though their objective working looks similar, they vary drastically in their performances. Some of the important factors that cause such variations are the scalability of the dataset, dataset nature, training time estimation, classifying time for the test data, prediction accuracy and the error rate computation. This paper focuses mainly on analyzing the performance of the existing four main classifiers: IF-THEN rule, C4.5 decision trees, naïve Bayes, and SVM classifier. The objective of this research article is to provide the complete statistical performance estimates of the four classifiers to the authenticated cloud users. These users have the access facility in obtaining the essential statistical information about the classifiers in study from the cloud server. Such statistical information might be helpful in choosing the best classifier for their personal or organizational benefits. The classifiers follow the traditional underlying algorithms for classification that is performed in the cloud server. These classifiers are tested on three different datasets namely PIMA, breast-cancer and liver-disorders dataset for performance analysis. The performance analysis indicators used in this research article to summarize the working of the various classifiers are training time, testing time, prediction accuracy and error rate computation. The proposed comparative analysis framework can be used to analyze the performances of the classifiers with respect to any input dataset.

A subthreshold analytical model for Dual Material Double Gate Junctionless Tunnel FET (DMDG JLTFET) is developed. To analyze the behavior of short channel device, relevant parameters such as surface potential, electric field, threshold voltage, drain current, transconductance-to-drain current ratio, subthreshold slope and subthreshold swing are extracted from the 2-D analytical solution of Poisson's equation. Relating DMDG JLTFET to Junctionless MOSFETs, the results show significant reduction of Short Channel Effects (SCEs) at the drain end. The analytical model for DMDG JLTFET is effective due to the combined supremacy of uniform doping in the channel, usage of high-K gate dielectric material and work function engineering in the gate metal. The results obtained from the analytical model are simulated and validated using 2-D Sentaurus TCAD device simulator.

In the recent times, the performance of MOSFET in the nanoscaled region attains improvisations through several alternative device structures. Amongst many advanced MOSFET structures, the Double Gate (DG) MOSFET is one such structure which mitigates short channel effects because of its excellent scalability. Among the various structures, FET- based biosensors have shown an accelerated growth recently. Even though many analytical models are available for the Dual Material DG (DMDG) MOSFET structures, this work endeavours to introduce an analytical modeling of nanocavity embedded DMDG structure for the first time. The expression for surface potential is obtained by solving the 2-D Poisson’s equation using a parabolic-potential approach. The threshold voltage is determined from the minimum surface potential model. Sensitivity is computed in terms of relative change in the threshold voltage and it is derived using the model. The influence of various device geometrical parameters like length and thickness of the nanocavity on the sensitivity has been investigated. Further, a comparison of the sensitivity of DG MOSFET and DMDG MOSFET has also been made and the derived results are validated against TCAD simulation results.

In the age of internet, everything about our personal identity can be found on the Web. Billions of email account details to millions of credit and identity data (Equifax) had compromised in the massive data breaches recently. So one of the greatest challenges faced today is identity management (IDM). Blockchain technology offers a way to prevent this problem by providing a secure solution without a central authority. People can store data on a blockchain by their own, thereby avoiding intermediaries and vulnerable servers. Information once stored is secured cryptographically and cannot be altered or deleted, thus making it secure from data breaches. This technique of IDM enhances decentralization, transparency, fault tolerance and user control in transactions that involve sensitive identity informations. The tremendous development of Bitcoin had given widespread attention and high-end research on blockchain technology. In the age of internet, everything about a person's identity can be found on the web. Billions of email account details to millions of credit and identity data (Equifax) had compromised in the massive data breaches recently. So one of the greatest challenges faced today is IDM. Blockchain technology offers a way to prevent this problem by providing a secure solution without a central authority. Blockchain is a distributed database system similar to an open, shared ledger that stores and manages transactions. Each record in the database is called a block and is cryptographically linked to the previous block. People can store data on a block by their own, thereby avoiding intermediaries and vulnerable servers. Information once stored is secured cryptographically and cannot be altered or deleted, thus making it secure from data breaches. This technique of IDM enhances decentralization, transparency, fault tolerance and user control in transactions that involve sensitive identity informations. In spite of these features, this distributed ledger technology based IDM lacks proper identity verification and classification of individuals.

Food losses due to plant disease from pathogens such as fungi, bacteria, and viruses are important problems in the farming field across the globe. Plant diseases determine the growth of their respective species; hence, it becomes important to identify the diseases in the early stage. Artificial intelligence and machine learning have been employed to detect and classify the diseases. Later the advancement in the subset of machine learning and the deep Learning made this research area into high potential in terms of precise prediction and accuracy. Many versions of deep learning–based architecture are implemented along with various nonvisualization and visualization techniques to classify and detect the symptoms of plant disease with several performance metrics. This chapter illustrates a comprehensive review of deep learning models used to detect plant diseases, and in some cases, the diseases have been identified before the symptoms appear clearly.

This chapter reviews the history of biosensors, its principles, materials used, and its application in glucose monitoring and respiratory airflow monitoring.

Over the decade, blockchain and big data has been one of the most emerging technologies of development across industries. There is no end in the long run for blockchain. Blockchain technology is a way to evolve automation along with business process management. Blockchain along with big data increases transparency and privacy of data. Data is stored in blocks that contain the hash of that particular block and hash of the previous block. Hash is executed using SHA256. On a large scale environment, blockchain is a decentralized system (independent system) for storing and transferring information. To increase the security and quality of data, blockchain uses secured cryptographic distributed database technology, thereby increasing the efficiency. Blockchain can be very helpful in preventing data leaks, to make any changes in data we need to have multiple permissions, this becomes difficult for cybercriminals to hack the server. Blockchain has many applications in every field like medical record system, banking, social media, etc., in the field of medical record system it witnesses for utilizing safe and secure health care data management[]. Space giants like the U.S. National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) are studying ways to employ blockchain for their mission to make it more powerful. Blockchain can be an efficient mode for online data storage. Blockchain along with big data provides a unique solution to how banks can improve the way they protect their customer’s digital privacy. It also acts as a filter role in detecting fraud in financial management and act as efficient barrier to data theft. In the intersection of big data and blockchain, the overall improvement of the data being captured, and the quick transfer of the data which is causing quite a stir. Blockchains will give businesses to confidently, identify the integrity of the data being generated. Consensus driven timestamping, proper audit trails and immutable entries all become better as blockchain starts becoming more emerging onto the world. Accelerated progress and major changes are being made daily when it comes to new blockchain technologies, ushering in new developments in the big data analytics field. As more people start using blockchain-driven services, analytics also must be capable of generating high-quality data insights from the existing data.it also serves in different domains of big data analytics with blockchain as Stoj, Rublix, file coin, etc.

One of the essential challenges to Fog computing security is authentication between service offering Fog servers and Fog users. This security confirmation is critical as a large number of users are offered services in Fog Computing. To address this challenge, in this paper, we first propose an efficient mutual authentication method between Clouds-Fogs-Edges. The solution enables authentication between edge users and the Fog servers that newly enter the system. The method makes the Fog servers to store one secret key per user and the user performs hash-based encryptions/decryptions. The proposed system is supported by Blockchain technique to achieve data integrity when confidential data are transferred between edge devices and nodes. The transactions between the nodes are recorded in the blocks which are hashed so that all edge and fog nodes are encircled by a secured system. Whenever any malicious edge server enters the network, it is identified based on ledger maintained at the nodes. The security and performance analysis shows the scheme is applicable for the Fog computing environment.

Internet of Things (IoT) helps in creating innovation in pharma industry that will benefit manufacturers and as well as patients. The purpose of pharmaceutics is to improve patients’ lives so that they can live confidently and actively with the condition they suffer from. The IoT has already made tremendous changes in the pharma industry, and the technology is still in its infancy. This chapter aims to provide the various techniques related to IoT for pharmaceutical related data and further assist in the analysis of the data generated from pharmaceutical field. The various pharmaceutical concepts based on IoT are being investigated, and the clinical data is being investigated for the body movements, and an analysis is done based on that. The analysis of the proposed system shows that it is 4% better in reducing the error rate of the results.

Digitization era is altering several industries which include the way in which the data is analyzed and it is inferred that about 2.7 Zettabytes of data exist in the digital world today. By 2020 the data generated per second for every human being will approximate amount to 1.7 megabytes and the volume of data would double every 2 years thus reach the 40 ZB point by 2020. Interactive Data Corporation (IDC) estimated that by the end of year 2020, the e-commerce transactions B2B and B2C will hit 450 billion per day on the internet. The advent of Big and real time Data has triggered disruptive changes in many fields and the exploding volume of different sources of data like heterogeneous data, data integration, spatio-temporal correlation of data, batch analytics and real-time analytics, data sharing, semantic interoperability requires the development of a scalable platform that can fuse multiple data layers to handles the data intelligently. In Big Data approaches, the challenge is not anymore to collect the data, but to draw valuable conclusions by properly analyzing them. The growth in Unstructured Data generated by business is irrefutable and they are under more pressure to preserve it for longer periods of time. To be clear, exploiting the collected data has been always considered by practitioners and researchers, but the huge velocity, heterogeneity and enormity of massive stream of real-time data shove the limits of the current storage, management and processing capabilities. Admittedly, the traditional method of Extract, Transform and Load (ETL) are challenged and cannot be applied on the emerging opportunistically and crowed sensed data streams. Some of these data streams are structured in a way that serve only one predefined purpose and cannot be directly used for other means. Yet, there are emerging unstructured data such as context-based data from the internet and social media as well as credit card transactions that is not clear if they can be used to better understand the mobility patterns. The analytical company Gartner states that by 2020 there will be over 26 billion interconnected devices. It is obvious, that they will produce massive amounts of meaningful data. Those data can be used for many applications such as real-time industrial equipment monitoring, traffic planning, automated maintenance, etc. Therefore, it is essential to develop modern system abstractions that allow us to resourcefully process huge and new data streams. This enormous amount of data urges the growth of integrated and insightful big and real-time data analytics Platforms. The upcoming contemporary technology like digital twin, integrates historical data from past machine usage to the current data. It uses sensors to collect the real-time data, working status and other operational data attached to the physical model. These components send the relevant data via a cloud-based system to the other side of the bridge with the help of data analytics platform which produces the required insights. The big and real-time data analytics Platforms assist to perform useful operations on data analytics as a complete package. For this purpose, data analytics platform are used to acquire constructive insight from the huge volume of data. Data analytics platform is an ecosystem of technologies and services that can help the businesses in increasing revenues, enhance operational efficiency, stabilize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals. The data analytics platform finds the pattern and relationships in data by applying statistical techniques and communicates the results generated by analytical models to executives and end users to make decisions with the help of data visualization tools that display data on a single screen and can be updated in real time as new information becomes available. Big data and real-time data analytics platform supports the full spectrum of data types, protocols and integration to speed up and simplify the data wrangling process. The big data and real time platform provides accurate data, increase efficiency in the workspace, gives answers to complex questions along with security and hence it plays the key role in business analytics.

This paper presents an approximate solution of a 2D Poisson’s equation within the channel region for Double-Gate AlInSb/InSb High Electron Mobility Transistors (DGHEMTs), using variable separation technique. The proposed model is used to obtain the surface potential, electric field, threshold voltage and drain current of both tied and separated gate bias conditions for Double-Gate AlInSb/InSb HEMTs. The surface potential and electric field are derived for both effective conduction paths of front and back heterointerface by a simple analytical expression and an analytical solution is verified with sentarus TCAD device simulator.

The impact of temperature in carrier transport of scattered SiNW MOSFET has been developed. Scattered SiNW MOSFET model includes the effects of elastic scattering, optical phonon emission, surface roughness scattering and random discrete dopants. The temperature effect of above mentioned scatterings in the device limits electron mobility, decreasing device current and transconductance. This work discusses the detailed behavior of analog parameters like transconductance (gm) and early voltage (VA). The validity of the proposed model has been confirmed by comparing the analytical results with the technology computer aided design simulation results.

The E-commerce zone is crowded with many Internet users. Medical E-commerce has had significant growth in part because of a great deal of growth in the Indian E-commerce field. Medical E-commerce sites use cloud computing to guarantee a high quality of service anywhere and anytime in the world. For online access, the customer's expectations are very high. Medical E-commerce retailers are directed towards cloud service providers based on their quality of service. During online shopping, impatient customers may abandon a specific medical E-commerce shopping cart due to slow response. This is quite difficult to endure for a medical E-commerce firm. The research described herein observed the effect of shopping cart abandonment on medical E-commerce websites deployed in cloud computing. The impact of the idle virtual machine on customer impatience during medical E-commerce shopping was also studied. The ultimate aim of this study was to propose a stochastic queueing model and to yield results through probability generating functions. The results of the model may be highly useful for a medical E-commerce firm facing customer impatience, so as to design its service system to offer satisfactory quality of service. Keywords: Cloud computing, Queueing, Virtual machine, E-commerce, Cart abandonment, Quality of Service

In current years, Internet of Things (IoT) has attained tremendous growth which includes all the aspects of private and public sectors. The resource constrained nature of IoT devices enables the IoT devices to store and access their sensitive data across the cloud computing platforms. The outsourced data do not only include users personal information's but also contain various information's such as sensor data, device data and several other confidential information's. Thus, the property of security remains to be the major concern across IoT based cloud system. In this article, we describe a secure privacy preserving proxy re- encryption scheme for IoT security using near-ring. The proposed approach solves DLP based factor problem using near-ring. It is observed from the security analysis that the proposed approach is highly secure with lesser computational overheads

In this chapter, an analytical model for single gate AlInSb/InSb HEMT device has been developed. A 2D sub threshold analysis of the proposed device is done to evaluate the channel potential, electric field, sub threshold drain current which exhibits superior performance with AlInSb/InSb in terms of short channel effects and electron mobility in the channel. In addition to the above advantages, the AlInSb/InSb material is extensively used in high gain and low noise applications. Expression for channel potential, electric field, threshold voltage, drain current have been obtained. From the simulative study, we observe that the step function in channel potential suppresses the SCEs. The study of AlInSb/InSb HEMTparameters of the device by considering the variation of the first subband ,the second subband and sheet carrier charge density( with applied gate voltage() and the performance of the device was compared for various temperatures. The scaling theory for single gate AlInSb/InSb HEMT is derived with Effective conducting path effect. The potential in the effective conducting path and a simple scaling equation has been developed and on solving this equation the minimum channel potential Фdeff, min and the new scaling factor αis obtained to model the subthreshold behavior of high electron mobility transistors

Nowadays internet is the basic requirement of many people to implementing their task through networks. With the increasing demand for access wireless internet, the wireless spectrum is blocked very frequently and are going to make it increasingly difficult getting onto a reliable signal. Consequently, there is a need to find new means of wireless communication. A new technology introduced to cope up with this problem by sending data through a LED light bulb. It called Li-Fi technology- Light Fidelity. Li Fi is a fast and cheap wireless-communication system. But one of the main challenges for Li-Fi technology is there should be a line of sight between sender and receiver, visible light cannot penetrate through solid objects and doesn’t work in the dark. This leads to interruption of the signal from the user and thus it becomes isolated from the world. In this research, we will try to overcome these challenges by building a hybrid network and studies the load balancing in hybrid Li-Fi & Wi-Fi network. To achieve that we consider cell breathing technique to reduce congestion cells and we consider the min-max technique for load balancing between access points in this hybrid network. All the discussions are under the following sections of introduction, literature survey, system design, implementation, and conclusion.

The paper discusses the standard frameworks and conceptual system architecture suitable for cloud computing environment. The major focus of this paper is to provide a clear description to advanced security features that supports better access provision to the cloud data users. This is accomplished through extending the support for the properties such as confidentiality, integrity, security factors. This paper does not provide any description of cryptographic phases and cloud deployment models associated with system frame work establishment. It provides a clear description to integrity and security assurance over the outsourced data. It acts as a supporting factor to access control models defined in previous papers of the domain.

Healthcare acquired its current influence regarding big data technology due to the fact that the data sources involved in healthcare are well-known for their volume, heterogeneous complexity, and high dynamism. In the context of big data, the success of healthcare applications depends solely on the underlying architecture and utilization of appropriate tools, as evidenced in pioneering research attempts. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics in medicine and healthcare covers integration and analysis of large amounts of complex heterogeneous biomedical data and electronic health records data. Since the volume of data is huge, it must be used as an advantage to provide the right intervention at the right time and establish personalized care for the patients. Thus this benefits all components of the healthcare system. This chapter aims to provide a better healthcare framework harnessing the benefits of big data analytics. The huge medical data is processed effectively by using various analytical tools, thereby arriving at deeper insights into the data. The existing systems employ algorithms like artificial neural networks, logistic regression, and fuzzy based algorithms. Some techniques used swarm optimization for classification. The clustering involved usage of self-organizing maps and k-means. This chapter proposes a new framework for healthcare using support vector machines and analyzes the performance for big data in terms of accuracy, sensitivity, error rate, and area under the ROC curve. The proposed system is compared with the conventional system in terms of RMSE and MAPE value and the results prove promising for the newly proposed system.