Cloud computing and IoT are two great technologies which have the potential to reform the healthcare industry and enhance the systems that are in place to offer the best care to the frustrated patients, to manage a large number of data and to change the working processes. This chapter explained the role of cloud and IoT in healthcare domain and shifted focus to the key issues and challenges such as data security and privacy or, IoT and cloud integration and standardization and the issues with the big data management and analytics. Patient's personal information is quite high and it is critical to protect the data of patients in contemporary healthcare. Additionally, establishing connectivity between IoT devices and Clouds, programs possess certain limitations pertaining to various encoding and data transmission methodologies. Finally, the chapter about the challenge of managing and understanding big data ends with the assertion that it can only be done if a big-cloud structure and expensive sophisticated analytical methods are used to extract useful information out of it.

This paper describes the design and development of a high Power Gallium Nitride (GaN) based Solid State Pulsed Power Amplifier (SSPPA). The paper dwells on the nonlinear simulation of the power amplifier with special emphasis on planar high power splitting and combining techniques. The unique charge storage mechanisms adopted in the realization is explained. The self-protection features incorporated are illustrated in detail. This amplifier is designed in the X-Band to generate a peak power of 250W at a duty of 20%. It is purely convection cooled and is a remotely operated system. This amplifier can be used as a building block to generate higher power amplifiers.

Medical image analysis has become a key technology for medical improvement. It involves using and studying images of the human body, usually from an X-ray, a CT scan, or an MRI, imaging technology has come a long way to detect diseases, schedule surgeries, and conduct research. Medical image analysis allows extensive, non-invasive anatomy investigation. With the rapid growth of artificial intelligence (AI), deep learning (DL) based techniques for analyzing medical images are becoming more widespread. Deep neural network, powerful strategy for computers to learn by themselves that can automatically analyze medical images for disease detection and assessment. Numerous AI-based solutions automate medical picture processing by detecting, segmenting, classifying, and analyzing lesions, tumors, clots, and obstructions. DL models can recognize patterns and relationships in medical images using large amounts of data. DL allows higher abstraction and better dataset prediction. Therefore, DL has had a major impact and gained popularity recently. In this chapter, common medical imaging modalities and deep learning-based technologies for medical image processing tasks like classification, detection, segmentation, severity grading, and prediction are explained.

This paper describes the detailed design of a Low Phase Noise Local Oscillator Signal Generation for Block Up Converters. This paper uses a single chip integrated PLL and VCO (PLL/VCO) based synthesizer for generating the LO signal for BUCs which helps in reducing the size and part count, thereby enhancing the reliability. This will also ease the generation of different LO signals with minimal hardware and program changes for different frequency band BUCs. In addition, this paper describes the detection and monitoring circuit used to realize an infallible LO frequency generation circuit. This paper also provides insight on LO frequency generation using external or internal reference signal (auto/manually selected). The paper talks about continuous monitoring of the lock status of the Synthesizer which is auto locked in case of unlock and the analog sample of the LO which can be tapped to check the spectral purity of signal before mixing.

This paper describes the design and development of of a Gallium Nitride (GaN) based Solid State Pulsed Power Amplifier (SSPPA). Pulsed Power amplifiers are the main building blocks for all pulsed RADAR systems. This paper dwells on the design of the RF section and an innovative design of a microcontroller and multivibrator based circuits to monitor, display and protect high power solid state amplifiers. This amplifier works in the X-band, generating a peak pulsed power of 50W, up to a duty cycle of 20%. This amplifier can be used as a basic building block to realize higher power amplifiers, which can replace microwave tubes.

One of the main factors causing suicide is depression. However, many cases of depression go undiagnosed because they are not correctly diagnosed. An increasing number of people with mental illnesses express their emotions online using tools like social media (SM) and specialized websites. Recently, efforts have been made to use Machine Learning (ML) and deep learning (DL) models to predict depression from SM platforms. However, it is problematic that most ML algorithms now provide no explanation. As a result, this study proposes a novel Deep Learning (DL) model called residual network 50, which includes optimal long short-term memory (RNT-OLSTM) for Depression Detection (DD) on Twitter data. In addition, to address the issue of data imbalance in the Twitter data, a cluster-based oversampling approach is used, which considerably reduces the possibility of bias towards the dominant class (non-depressed).. Finally, the embedding layers are inputted to RNT-OLSTM for DD, in which the hyperparameters of the network are tuned using the Sine Chaotic map and constriction factor-based Coyote Optimization Algorithm (SCCOA) to minimize the prediction loss. The out-comes prove that the proposed system performs better than the existing schemes for the DD of imbalanced Twitter data with higher detection rates.

This paper describes the design & development of a Ku band Block Up Converter. Block Up Converter is one of the important blocks in the ground segment of a Satcom terminal. The L band input signal is single up converted to Ku Band and amplified for transmission. Bandwidth of operation is 750MHz. The module has a gain of 70dB (min). This forced-air-cooled Ku-band BUC has many integral RF & power supply protection features for 24x7 outdoor operation in Satcom uplink systems.

This paper describes the design and development of a 500 W solid state pulsed power amplifier (SSPPA) module based on Gallium Nitride (GaN) technology. Pulsed power amplifiers are the main building blocks for all pulsed RADAR systems. This paper mainly focuses on the complete design of the RF transmitter with DC sequence card. A high-power combiner is used to combine two 350 W pulsed power amplifier to achieve the output power of more than 500 W. This amplifier works in the C-band, generating a peak pulsed power of 500 W up to a duty cycle of 10% with maximum pulse width of 100µs. This amplifier can be used as a basic building block to realize higher power amplifiers, which can replace microwave tubes. Roger 6035 HTC with design dielectric constant of 3.6 with 20mils thickness is used to implement full module.

The everyday eating habits and lifestyle choices that people make have a significant impact on how long they live on the planet. Ancient people ate food that had an acceptable ratio of fat, vitamins, minerals, and carbohydrates, which helped them live a long life. Nowadays, individuals live shorter lives and experience many crises like heart attacks and mental despair that cause them to drive carelessly and cause accidents. This is due to our current diets of junk food and style of life. For the people and the individuals, this results in a tremendous loss. Here, saving people's lives depends largely on the passage of time. The extent of the injury or the patient's emergency situation, the amount of traffic that makes it difficult for the ambulance to reach its destination, and the hospital's capacity to accept patients and save lives are just a few of the many factors that affect the time limitations. In the current situation, hospitals are using the available services to meet time restrictions, which correctly route the ambulance. The main disadvantage of this system is that hospitals handle all the data, making it easy to tamper with medical records and risk losing the integrity of the data. The goal of intelligent ambulances is to forecast the shortest amount of time needed to admit the patient to the local hospitals that have the resources to care for them, preventing the need to transfer patients to other hospitals, as well as to determine the most efficient route to the destination. The patient's life can be saved as a result. The aforementioned can be accomplished by using a deep learning algorithm to predict the injury and the time limit to admit the patient to the hospital, matching the injury with the treatment options available in the hospital and mapping the appropriate hospital, as well as by finding the quickest route with the least amount of traffic to get to the destination within the allotted time limit, giving first aid in the ambulance, and handling the data transfer of health records in a secure manner. Therefore, in a smart city, the smart ambulance can quickly save lives.

This paper describes the design and development of a compact outdoor Ku Band down Converter with ultra-low Noise Figure and inbuilt synthesizer with low phase noise which converts a Ku Band signal to L-Band signal which is then fed to a signal processing unit. The bandwidth of operation in Ku-band is 0.8 GHz with a frequency step resolution of 10 MHz The self-generated reference frequency input to the module is a very low phase noise 10 MHz source.

A recirculating fixed bed reactor was adopted to study the mineralization efficiency of phenol and trisubstituted phenols like 2,4,6-trichlorophenol (TCP), 2,4,6-trinitrophenol (TNP), and 2,4,6-trimethylphenol (TMP) using Granular Ferric Hydroxide (GFH) as a heterogeneous catalyst in the presence of solar light. The effect of operating parameters for the fixed bed reactor namely column bed height and recirculating flow rate were optimized for 2.12 mM of phenol as a model compound at pH 3.0 and 18.3 mM of hydrogen peroxide. Maximum mineralization efficiency of around 96% was attained within 2 h of the reaction time at the optimum condition of 5 cm bed height and a recirculating rate of 210 ml h(-1). The mineralization rate of the trisubstituted phenols follows the following order: TNP > Phenol > TMP > TCP. Higher mineralization efficiency was observed due to the synergetic effect of both photocatalysis and photo-Fenton reaction. The electron withdrawing group in TNP facilitates the higher mineralization efficiency and its rate constant is increased by a factor of 2 compared to the electron donating group in TMP. The electronegativity of chlorine together with the scavenging effects of chloride ions decreases the efficiency of TCP by a factor of 2.7 as compared to TNP.

Cloud computing is a vast revolution in information technology (IT) that inhibits scalable and virtualized sources to end users with low infrastructure cost and maintenance. They also have much flexibility and these resources are supervised by various management organizations and provided over the Internet by known standards, formats, and networking protocols. Legacy protocols and underlying technologies consist of vulnerabilities and bugs which open doors for intrusion by network attackers. Attacks as distributed denial of service (DDoS) are one of most frequent attacks, which impose heavy damage and affect performance of the cloud. In this research work, DDoS attack detection is easily identified in an optimized way through a novel algorithm, namely, the proposed gradient hybrid leader optimization (GHLBO) algorithm. This optimized algorithm is responsible to train a deep stacked autoencoder (DSA) that detects the attack in an efficient manner. Here, fusion of features is carried out by deep maxout network (DMN) with an overlap coefficient, and augmentation of data is carried out by the oversampling process. Furthermore, the proposed GHLBO is generated by integrating the gradient descent and hybrid leader-based optimization (HLBO) algorithm. Also, this proposed method is assessed by various performance metrics, such as the true positive rate (TPR), true negative rate (TNR), and testing accuracy with values attained as 0.909, 0.909, and 0.917, accordingly.

Skin tumor acts as a premier factor for high death rate throughout the world. To segment the skin tumor the radiologists find it impotent. Various research works find it to be an ineffective manifestation. The intention of this research work is to provide an efficient enhancement method and tumor detection from other unaltered regions. This work relies mainly on computed tomography (CT) tumor images of the skin, benign or malignant, that has been implemented efficiently. In this research paper, a novel methodology called Online Tiger Claw Region Based Segmentation (OTCRBS) is proposed which is used mainly to detect the boundary of unaffected Skin Cell, similar to tiger which uses its claws to tear off the skin of its prey during the search for its food. By using metric for the region, various properties can be formulated for the detection of anomalous skin cells. 98.68 and 97.71% accuracy is produced for procurement of benign and malignant nodule in MATLAB 2018a, respectively. Computation time was only 7.65 s. Comparative analysis is made with different segmentation methods. Experimental results establish that the proposed flow outperforms all the existing segmentation methods for the proper detection of tumor cells.

This paper describes the design & development of a RF Front End module that operates in S Band. The RF Front End module is situated in the receive section of the Doppler Weather Radar, it down converts the S band input signal to IF for next level signal processing. Bandwidth of operation is 300MHz. Image rejections are achieved using Image Rejection Mixer and filters. The module has a gain of 31dB, Noise Figure of 2dB (Max.) & an excellent dynamic range from −10dBm to −110dBm. The module is light weight and is designed to operate over temperature range of +55°C to −10°C.

COVID-19 caused a dramatic change in the lifestyle of people around the globe and has had an impact on all sectors, including mental health, the economy, and social behavior. Mental health is of great concern for the survival of the young people in achieve their goals. This chapter concentrates on mental health in the education sector during the pandemic. Students and faculty members experienced a high amount of frustration, stress, anxiety, fear, and loneliness during the pandemic. The implementation of online classes was a burden to faculty and students and led to an unsatisfactory mode of teaching in which eye-to-eye contact was missing. Although experience was gained for both teacher-centric and student-centric modes of teaching, mental health resulting from online classes is analyzed in this chapter. Mental health during the pandemic period we analyzed by collecting data from students and staff in the higher education sector from the point of view of undergraduates, postgraduates, and research scholars. Deep learning algorithms pave the way to analyzing mental health for people in the education sector. It predicts the percentage of staff and students who are disturbed in their profession and study. This analysis helps to reduce the gap of interaction between staff and students in the blended mode of teaching. It also provides insight into government policies related to future modes of education.

Heart disease causes major death across the entire globe. Hence, heart disease prediction is a vital part of medical data analysis. Recently, various data mining and machine learning practices have been utilized to detect heart disease. However, these techniques are inadequate for effectual heart disease prediction due to the deficient test data. In order to progress the efficacy of detection performance, this research introduces the hybrid feature selection method for selecting the best features. Moreover, the missed value from the input data is filled with the quantile normalization and missing data imputation method. In addition, the best features relevant to disease detection are selected through the proposed hybrid Congruence coefficient Kumar–Hassebrook similarity. In addition, heart disease is predicted using SqueezeNet, which is tuned by the dwarf mongoose optimization algorithm (DMOA) that adapts the feeding aspects of dwarf mongoose. Moreover, the experimental result reveals that the DMOA-SqueezeNet method attained a maximum accuracy of 0.925, sensitivity of 0.926, and specificity of 0.918.

Enormous reports, like scanned medical pictures and electronic patient records, are generated by modern healthcare equipment, and these reports must be secured for future access. Conventional storage technologies are incapable of storing large amounts of data. Cloud storage systems, fortunately, satisfy because of their features, like availability and scalability. Although medical images can be saved in the cloud, majority of cloud service providers store them in plain text format. Because of hackers and the increasing computer power, current image encryption methods are vulnerable to attacks. Therefore, a Block chain based Chaotic Deep Generative Adversarial Network (GAN) Encryption Scheme is proposed. The BCDGE uses blockchain technology to protect personal information and verify the authenticity of the data. Secondly, the Chaotic Deep GAN Encryption method uses confusion, substitution, and diffusion principles to encrypt the medical image. The Deep GAN creates image-specific secret keys to improve the resilience against hackers, produced keys utilized as an input for confusion and diffusion phases. The sender transfers the encrypted medical picture to the cloud server, signs the ciphertext ID, and saves it to block chain. The security and performance analysis of the proposed scheme gives better results than the existing schemes.

In today's world, the transaction through the smart device has received greater attention and configures numerous applications which can efficiently process huge traffic records on a growing demand for service centers from the edge of the networks. Due to these immense growths, the concern raises in critical transaction records in terms of system security threats and efficiency issues in the smart devices. However, existing methods failed due to security attacks during the tenure of access transactions and aggregated services. Recently, blockchain technology enables service centers depends on various platforms to share transaction records. But, it is difficult to store the transaction record because of its size. To address these issues, we proposed a SECure LearningChain (SEC‐ LearningChain) design based on the integration of blockchain technology, machine learning (ML), and cloud computing primitives are applied together for a secure data transaction in a Peer to Peer network as well as efficient data sharing service. This approach consists of four design models: First, an attack detection model detects the attack using threshold‐based anomalous traffic detector in the transaction network. Second, a mold blockchain transaction network model is designed based on the cryptographic hash and encryption to deal with threats and validate the identity verification process for a secure transaction. Next, the large‐scale transaction record is optimized and trains the ML model for the output prediction. Finally, the cloud assessment model manages the stored transaction records and easily share the accessed services across different cloud platforms for each service center. Furthermore, we prove that the SEC‐ LearningChain design resists transmission control protocol flooding attack, denial of service attack, and falsify attack. Experimental results demonstrate that the performance of the SEC‐ LearningChain achieves more number of transactions in each blocks over existing schemes.

A microgrid is an powerful alternative promising solution, which fosters a reliable power supply to supplement conventional electrical framework. However, microgrid encounters various challenges subjected to energy flow, power quality, and net profit due to the high integration of distributed energy resources (DERs) in the network. Hence, the Mimosa pudica-based energy management scheme has been designed for optimal scheduling of microgrid to reduce the production overheads with underlying system constraints. Besides, the proposed approach is formulated to manage the power balancing among the distributed resources and utility through a standard communication protocol. Mainly, the optimization process is developed through the sensitive and intelligent behavior of mimosa plants that enable the adaptation of dynamic context by reusing past information through memory, and maintaining variation across the solution leads to good solution accuracy. Moreover, the proposed algorithm corroborates the potency of microgrid over isolated, grid-tied, and resynchronized conditions. Numerical results illustrate that the proposed technique has obtained more profit (8%) than the existing approaches with superior optimization ability and fast convergence.

The Cloud Environment provides storage for files as well as sharing services for disseminated clients. This technology helps users in the cloud to outsource their data and get good quality services from the cloud. Often, Data Integrity is a serious issue in Cloud Storage because the service provider is a semi-trusted one. Consequently, a self-determining auditing service is essential to check data integrity. In this paper, we developed a secure Cloud Storage environment without a third-party system and proposed an Improved Rivest-Shamir-Adleman Encryption Algorithm (IREA) along with the Flexible Capacity Cuckoo Filter (FCCF) auditing technique without Third Party Auditing. In this proposed system, the data owner encrypts the data using Improved RSA and stores it in the Cloud Service, and users alone can decrypt the data with the knowledge of the data owner. Furthermore, the FCCF ensures the integrity of the verification and secure data transactions can be provided by using the Two Level Quick Response (2LQR) code. The experimental results validate that the proposed IREA-FCCF with the 2LQR technique for data security in a CE has less time compared to that of the existing schemes in terms of encryption time, decryption time, signature generation, signature verification, and cost of the dynamic records.