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In today’s digital world, data protection is extremely important. Every company’s data is a valuable asset, so it’s important to ensure that it's secured from outside threats. Information security is not only an effective but also a necessary element to protect data from unauthorized access. The confidentiality of any communication system is strengthening with the help of random number generators along with some analog circuitry. This type of analog models demands more power and area. So analog circuit-based hardware Random Number Generators (RNG) are least preferred over digital RNGs. To improve the security every industry depends on the one-time password (OTP). Which gives the security but generation of the OTP is very easy. Random number generator is used for the generation of OTP. Similarly, hacking such OTP is easier than creating them. This paper introduces the Anti-Aging controller TRNG, a highly stable high-performance random number generator Anti-Aging Cryptographically Secured True Random Number Generator (AACTRNG). Implementation of this work can be done by using TANNER EDA Tools and ModelSim-Altera 6.4a (Quartus-II 9.0) used for the simulation to retrieve random numbers.

Purpose For the first time in a decade, a new form of pneumonia virus, coronavirus, COVID-19, appeared in Wuhan, China. To date, it has affected millions of people, killed thousands and resulted in thousands of deaths around the world. To stop the spread of this virus, isolate the infected people. Computed tomography (CT) imaging is very accurate in revealing the details of the lungs and allows oncologists to detect COVID. However, the analysis of CT scans, which can include hundreds of images, may cause delays in hospitals. The use of artificial intelligence (AI) in radiology could help to COVID-19-positive cancer in this manner is the main purpose of the work. Design/methodology/approach CT scans are a medical imaging procedure that gives a three-dimensional (3D) representation of the lungs for clinical purposes. The volumetric 3D data sets can be regarded as axial, coronal and transverse data sets. By using AI, we can diagnose the virus presence. Findings The paper discusses the use of an AI for COVID-19, and CT classification issue and vaccination details of COVID-19 have been detailed in this paper. Originality/value Originality of the work is, all the data can be collected genuinely and did research work doneown methodology.

Wireless networks (WSN) are sometimes inaccessible to humans and can be found in deep forests, diverse risky fields, high-mountainous areas and even underwater environments at least. Owing to continuous or instant changes in environmental parameters, failures in sensor networks are unavoidable. A fault can lead to a misread, which can harm the environment economically and physically. In several primary applications, wireless sensor networks (WSNs), including battlefield control, environmental monitoring and forestry fire monitoring are widely used. Research is being conducted to reduce energy usage, improve the longevity and life of the WSN network. This paper provides an ANFIS method for the summation, based on the neuro-fuzzy optimization model estimator of defect-tolerant WSNs. For WSNs, the scheme proposes the use of an intra-cluster and inter-cluster failure detection estimator adaptive neuro-fuzzy inference system (ANFIS). Traditional detection strategies for malfunction include a centralized mechanism for avoiding failure detection when the principal defect detector fails. In typical methods, the fault nodes are discarded. To solve these problems, we propose the identification and classification of distributed fault nodes by means of adaptive neuro-fuzzy inference method.

Purpose The unexpected epidemic of the latest coronavirus in 2019, known as COVID-19 by the Globe, a number of governments worldwide have been put in a vulnerable situation by the World Health Organization. The effect of the COVID-19 outbreak, previously experienced by China’s citizens alone, has now become more pronounced. For practically every nation in the world, this is a matter of grave concern. The lack of assets to withstand the infection of COVID-19, mixed with the perception of overwhelmed medical mechanisms, pressured a number of places in a state of partial or absolute lockdown. Design/methodology/approach The medical photos such as computed tomography (CT) and X-ray playa key role in the worldwide battle against COVID-19, while artificial intelligence (AI) has recently appeared. The power of imaging is further increased by technology tools and support for medical specialists. In comparison to the related direct health effects because of the COVID-19 disaster, this research identifies its impacts on the overall society. Findings This paper hereby examines the rapid answers in the medical imaging community toward COVID-19 (empowered by AI). For example, the acquisition of AI-empowered images will significantly assist automate the scanning process and reshape the procedure as well. AI, too, may improve the quality of the job by correctly delineating X-ray and CT image infections, promoting subsequent infections, quantification. In addition, computer-aided platforms support radiologists make medical choices, i.e. for illness tracking, diagnosis and prognosis. Originality/value This research encompasses the whole medical imaging pipeline and methods for research related to COVID-19, include a collection of images, segmentation, diagnosis and monitoring. In drawing stuff to minimize the effects of the COVID-19 epidemic, this paper is investigating the use of technologies such as the internet of things, unmanned aerial vehicles, blockchain, AI, big data and 5G.

Cybercrime has several antecedents, including the rapid expansion of the internet and the wide variety of users around the world. It is now possible to use this data for a variety of purposes, whether for profit, non-profit, or purely for the benefit of the individual. As a result, tracing and detecting online acts of terrorism requires the development of a sound technique. Detection and prevention of cybercrime has been the subject of numerous studies and investigations throughout the years. An effective criminal detection system based on face recognition has been developed to prevent this from happening. Principle component analysis (PCA) and linear discriminant analysis (LDA) algorithms can be used to identify criminals based on facial recognition data. Quality, illumination, and vision are all factors that affect the efficiency of the system. The goal of this chapter is to improve accuracy in the facial recognition process for criminal identification over currently used conventional methods. Using proposed hybrid model, we can get the accuracy of 99.9.5%

The basic operation involved in any analog, digital, control system, DSP’s is addition. Performance and reliability of almost every digital system is depends on performance of adder. Over the decade, many adder architectures are proposed and still research work is going on adder to obtain the best results in power, delay and power delay product (PDP). In this paper we proposed one of the fastest adder architecture called Carry Select adder (CSA) and optimization is done for performance parameters like delay and power using GDI (Gate Diffused Input) and MTCMOS techniques. Implementation has been done in standard gpdk 90nm technology using Cadence tool.

A number of problems such as diagnosis, decision-making and optimisation were successfully solved through the methods of artificial intelligence (AI). However, any AI algorithm used for creative problems needs some replacement mechanism for human creative spark, as the computer has no creative potential. Randomness may not be the only mechanism to construct a creative structure, as created by a random number generator. This paper examines AI for music composition. In particular, the paper introduces MAGMA, which uses three different AI algorithms for music generation.

With the limitations of CMOS technology scaling, rigorous research of alternate and competent technologies is emerged to impel the boundaries of digital computing. In this article, we proposed a simple, yet power and performance efficient methods of memory implementation. Present research attempts have been faithful to studying and performing these memory implementation techniques. In this article, we addressed the issues and described the current advanced methods of data storage. Conventionally, the WRITE operation in static memory consumes more power than the dynamic power associated with it because of the high-bit line voltage swing during the WRITE operation. This work presents the designing and characterization of ultra-scalable SRAM in terms of power and performance. The simulations are carried out using CMOS technology in cadence tool. The simulation results and functionality are distinguished with conventional memory units.

Present day's data security plays a vital role in digital human life. Data is a valuable asset to any organization and hence its security from external attacks is very important. Information security is not only an important aspect but essential, to secure data from unapproved access. Data encryption, decryption and key management are the key factors in data protection. It is very important to have the right data security solution to meet the challenging threats. Cryptosystem implementation and random number generators are crucial for Cryptosystem applications such as security applications, space applications, military applications and smart cards et al. In this paper, we present the implementation of hybrid cryptosystem based on the True Random number Generator, pseudo Random number Generator and whitening the data by using the ASIC design flow.

As technology advances towards lower nodes, chip industry facing severe challenges from its counterpart node with the narrow interconnects. Density of current becomes more affected with these higher device currents and increasing on-chip temperature. The interconnect reliability and their possible degradation from electro migration is a severe concern. Perhaps the problem of carrier migration requires attention from fabrication industry at higher technology nodes, more specifically electro migration becomes a severe concern at 28nm and below. Beside power dissipation, speed of the IC and area on the chip, electro migration also becomes severe at technology nodes 28nm and below. In this brief a comprehensive analysis of electro migration and inter connect parasitic are described for various trends of nanometer technology.

Operational amplifiers used in voltage follower, switched-capacitor, digital to analog converter is the basic blocks of analog systems. An operational amplifier design depends on the supply voltage and gain. The digital circuits greatly benefits from reduction of size and supply voltage while analog circuits depend on the parameters like offset, noise, gain, power consumption and bandwidth for greater benefits. Current mode circuits have high slew rate and wider bandwidth. Therefore, current mode circuits are more preferable than voltage mode circuits as it has a high slew rate and wider bandwidth. The dynamic range of low power operational amplifier is limited by small currents. One of the techniques to make Op-amp very efficient with low power supply voltage without degrading the characteristics use of effective current mirrors. In this paper the various Operational amplifiers with different current mirror loads are analysed and compared. The parameters of Operational amplifier are considered and compared in cadence virtuoso using 180nm technology.

Lung cancer progresses rapidly, so early detection is crucial the initially use the Internet of Things for early-stage lung cancer diagnosis (IoT). Training for the IoT devices is in real time is crucial they have IoT connectivity all over the world, and great trust in the model's accuracy, both of data. Early detection of disease will save hundreds of thousands of lives every year it completely seals off the airways, which could lead to infection. Malignant growth was first observed with picture-processing and learning systems. The signs of lung cancer usually don't appear until the disease has spread. This is where medical attention becomes most challenging Initially you might find your fingers curving, or ends rising, or pain when trying to swallow followed by a whistling sound, a hoarseness, a weight gain in your face, and/upper chest swelling Some cancer causes key signs include regular or worsening chest pain, bloody or rust-colored sputum, worsened hunger, and shortness of breath. A lung CT image can be used to classify and organize the lung knobs, and also to assess their threat level ECNN features parameters like precision, time intricacy, but is not as sensitive in the preparation as previous frameworks They have irregular cells that form a tumour. Uncontrolled lung growth and destruction Types of lung cancer grow as this happens; they begin to develop until they form a tumour. Lung cells are damaged when they come into contact with airborne toxins. The novel proposed + approach is ECNN.

Mining user's search context promises major improvements in several key aspects of Web search. Usage information such as search click-through has been a valuable source of information to determine what users want. However, such server side information is limited to query and corresponding clicks. Such query logs often do not contain sufficient information to determine user's search intent. Therefore, it is important to capture user's other activities such as the documents read or URLs opened before submitting a query. In this paper, we propose a framework to capture information about client side activities and share them with Web search engine effectively using Proxy server. From extensive experiments based on real user's experience, it is evident that user's search needs are influenced by the user's Desktop and Web activities. From further analysis, we observe that with an overhead of 2.32ms Proxy server can extract and send the additional information to a Web search engine along with a user's search request.