Search

The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.

The recent advancement in mobile technologies has led to opening a new paradigm in the field of medical healthcare systems. The development of WBAN sensors, wearable devices, and 5G/6G wireless technology has made real-time monitoring and telecare of the patient feasible. The complex framework to secure sensitive data of the patient and healthcare professionals is critical. The fast computation of health data generated is crucial for disease prediction and trauma-related services; the security of data and financial transactions is also a major concern. Various models, algorithms, and frameworks have been developed to tame critical issues related to healthcare services. The efficiency of these frameworks and models depends on energy and time consumption. Thus, the review of recent emerging technologies in respect of energy and time consumption is required. This paper reviews the developments in recent mobile technologies, their application, and the comparative analysis of their performance parameters to explicitly understand the utility, capacity, and limitations. This will help to understand the shortcomings of the recent technologies for the development of better frameworks with higher performance capabilities as well as higher quality of services.

Wireless sensor networks (WSNs) based on the Internet of Things (IoT) are now one of the most prominent wireless sensor communication technologies. WSNs are often developed for particular applications such as monitoring or tracking in either indoor or outdoor environments, where battery power is a critical consideration. To overcome this issue, several routing approaches have been presented in recent years. Nonetheless, the extension of the network lifetime in light of the sensor capabilities remains an open subject. In this research, a CUCKOO-ANN based optimization technique is applied to obtain a more efficient and dependable energy efficient solution in IoT-WSN. The proposed method uses time constraints to minimize the distance between sources and sink with the objective of a low-cost path. Using the property of CUCKOO method for solving nonlinear problem and utilizing the ANN parallel handling capability, this method is formulated. The resented model holds significant promise since it reduces average execution time, has a high potential for enhancing data centre energy efficiency, and can effectively meet customer service level agreements. By considering the mobility of the nodes, the technique outperformed with an efficiency of 98% compared with other methods. The MATLAB software is used to simulate the proposed model.

Due to the high amount of electronic health records, hospitals have prioritized data protection. Because it uses parallel computing and is distributed, the security of the cloud cannot be guaranteed. Because of the large number of e-health records, hospitals have made data security a major concern. The cloud’s security cannot be guaranteed because it uses parallel processing and is distributed. The blockchain (BC) has been deployed in the cloud to preserve and secure medical data because it is particularly prone to security breaches and attacks such as forgery, manipulation, and privacy leaks. An overview of blockchain (BC) technology in cloud storage to improve healthcare system security can be obtained by reading this paper. First, we will look at the benefits and drawbacks of using a basic cloud storage system. After that, a brief overview of blockchain cloud storage technology will be offered. Many researches have focused on using blockchain technology in healthcare systems as a possible solution to the security concerns in healthcare, resulting in tighter and more advanced security requirements being provided. This survey could lead to a blockchain-based solution for the protection of cloud-outsourced healthcare data. Evaluation and comparison of the simulation tests of the offered blockchain technology-focused studies can demonstrate integrity verification with cloud storage and medical data, data interchange with reduced computational complexity, security, and privacy protection. Because of blockchain and IT, business warfare has emerged, and governments in the Middle East have embraced it. Thus, this research focused on the qualities that influence customers’ interest in and approval of blockchain technology in cloud storage for healthcare system security and the aspects that increase people’s knowledge of blockchain. One way to better understand how people feel about learning how to use blockchain technology in healthcare is through the United Theory of Acceptance and Use of Technology (UTAUT). A snowball sampling method was used to select respondents in an online poll to gather data about blockchain technology in Middle Eastern poor countries. A total of 443 randomly selected responses were tested using SPSS. Blockchain adoption has been shown to be influenced by anticipation, effort expectancy, social influence (SI), facilitation factors, personal innovativeness (PInn), and a perception of security risk (PSR). Blockchain adoption and acceptance were found to be influenced by anticipation, effort expectancy, social influence (SI), facilitating conditions, personal innovativeness (PInn), and perceived security risk (PSR) during the COVID-19 pandemic, as well as providing an overview of current trends in the field and issues pertaining to significance and compatibility.

Accidental overexposure to non-standard mobile phone radiation can occur in many situations. The overpower limit of mobile phone radiation interacts with the human body which could result in an adverse effect on human health. It is envisaged that the severity of the physiological effect can take place with small temperature increase in the delicate organs or tissues such as eyes, brain, skin, etc. However, the resulting thermo-physiological response of the body tissues to overpower limit of mobile phone radiation is still not well implemented. The aim of this study is to analyze the effect of overexposure of mobile phone radiation on the specific absorption rate (SAR) and temperature increase in three-dimensional heterogeneous human head models. The study focuses attention on the differences in the electromagnetic (EM) absorption characteristics with higher power level among different usage pattern. The effect of three different usage patterns - voice calling, video calling, and texting- on SAR and temperature distributions in different types of head tissues is systematically investigated. This paper also investigates the effects of different user ages, radiated powers, and gap distances between mobile phone and human heads, on SAR and temperature distributions. Results obtained from this analysis considering the safety guidelines show a high impact of mobile phone radiation in the voice calling position. Hence, comparisons of the absorption of mobile phone radiation are calculated between an adult and a 7-year-old child head model, for the voice calling position at different gap distances. In addition, the results indicate that child head always has a higher absorption rate of mobile phone radiation than the adult head. The rate of absorption in tissue increases as the distance between mobile phone and head decreases and the radiated power increases, depending on their dielectric and thermal properties. The obtained results can be helpful in determining exposure limits for the power output of the mobile phone, and the distance a user should maintain from the mobile phone in thermo-physiological aspects.

With advancements in technology in recent years, implementation of smart healthcare practices has increased. The data collected from patient treatment have been utilized to analyse health problem patterns in order to identify further similar cases. Big data analytics and cognitive computing are contributing considerable value to the healthcare system. The data volumes in the healthcare industry are massive and dynamic, with a great sense of complexity. Big data analytics and cognitive computing enable the healthcare system to store and formulate voluminous data for treating patients and computing uncertainty, with the help of existing databases. These intensive systems enhance healthcare systems and improve their efficiency in the existing environment [1]. A smart healthcare system requires a secure network infrastructure and cloud environments for advanced and reliable results. It will definitely require human intervention in most cases. In some general cases, patients can be treated with machine monitoring only at healthcare centres or hospitals. Elsewhere, the need to visit a hospital can be minimized and treatment of patients can be done online with help from some general monitoring equipment at the patient’s home.

This chapter covers various algorithms for developing smart healthcare apps and software for implementation. The chapter begins with software for telehealth. The subsequent sections cover different algorithms for healthcare, such as data science, artificial intelligence (AI), machine learning (ML), big data, the Internet of Things (IoT) and the cloud infrastructure. The chapter concludes by listing popular algorithms that are now used to transform healthcare.

This chapter covers the concepts related to scalable smart healthcare systems, large-scale distributed computing in smart healthcare, and scalable and emerging smart healthcare systems. The chapter begins with review of scalable emerging smart healthcare systems like IBM® Watson™, Open mHealth, HDSS, SoDA and Energy-efficient system. The chapter concludes with the concept of secure and scalable architecture using Mist Computing, blockchain, fog computing and cloud-enabled WBAN.

Chapter 1 discussed the basic concepts of smart health and allied areas such as eHealth, digital health, mHealth, telehealth, and various components of smart healthcare.

Smart technologies have been in existence for years, but most of them are still in the testing phases. Data-oriented gadgets, sensors and technologies create a very personalized, customized and simulated approach to health and well-being. Smart technologies have not featured in the field of healthcare until recently but are now playing a vital role in improving health and quality of life. This chapter explores the challenges, opportunities and future trends in smart health that will change and update the traditional healthcare system as needed over time. This is vital because the population is increasing very rapidly and traditional healthcare systems are not fully able to provide fast and efficient diagnostics or treatment. Thus, with the involvement of human supervision, smart health can take the healthcare system to a higher level to support better, more efficient, more reliable and more rapid treatments, which will contribute great value to human life.

The recent evolution of healthcare systems has enhanced the overall experience of smart health by creating an infrastructure equipped with smart devices. Smart healthcare devices make crucial and valuable contributions in health monitoring and diagnostics. Smart health is all about tracking and monitoring the health status of the patient with use of smart devices. The information collected from these devices needs to be reliable, secure and based on real-time measurements that are successful with a positive rate. A critical problem for a healthcare system is that it has fewer remedial amenities for the purpose of tracking a patient’s past history, which can be quite helpful for providing an actual cure. Thus, it is necessary to optimize the overall health system by implementing smart healthcare practices. Optimized systems can provide more accurate diagnoses and can monitor the condition of the patient with transparency. The changes that occur continuously in the human body require real-time interpretation. Medical researchers, healthcare scientists and biologists are continuously working to improve predictive algorithms for smart healthcare to minimize the risks of severe health problems developing. Smart health solutions provide efficient monitoring of specific factors such as the blood pressure, blood oxygen and sugar levels, the heart rate and body temperature, which do not require a doctor’s specific attention in normal cases. Smart healthcare aids the purpose of exploiting accessible resources to their full potential [1]. The response time of smart healthcare is very short, as it can analyse the body’s activities over a brief period and generate alerts about the person’s health status. However, along with all of these advantages and values, there are enormous challenges in smart health that can increase risks. The complete realization on the sensor-based results may risk to the patient but in least cases. The responsibility involved in creating these devices is huge because any malfunctions can have important adverse consequences. These connected technologies enable healthcare facilities to operate remotely when there is a need for assistance from a healthcare professional. Security breaches involving patients’ data can make them very vulnerable; for example, inappropriate use of personal data can put a patient or their family at very high risk.

Health is the most important aspect of human life, and smart health can be considered a current revolution in health. There is an increasing need for smart technologies to provide disease diagnosis and healthcare treatment, using the most efficient and reliable sources of information, and with rapid increases in technology, smart health systems are being upgraded. Nowadays, a detailed report on the human body can be obtained by collecting data with particular Internet of Things (IoT) devices, and many important tests on the human body can be done easily by a few sensors in a very short period of time. Reports can also be analysed remotely by experts when no appropriate specialist is available locally. Smart healthcare systems are also cost effective for provision of medical services. Nowadays, IoT devices and sensor systems are used widely in smart healthcare systems [1]. A radical upgrade of the traditional healthcare system is the need of the hour, and that gap can be filled only by implementation of smart health services. These services involve smart infrastructure to build smart systems with smart IoT devices to track the health of patients. Such devices help to provide efficient, fast and precise disease diagnoses and determine what treatment should be used. Sensor-based devices create a customized system for a patient or user by formulating the results obtained from the data sets provided by these smart devices. This whole system creates a responsive framework and a smart infrastructure for smart health. This infrastructure is based purely on the feedback given by the sensors in the form of data; the data are then analysed by algorithms, and suitable actions can then be taken as a result of these sequences. Moreover, IoT devices used in smart health can provide full 24/7 observations of the patient, tracking and measuring progress in terms of data sets, which can then be further utilized by doctors to provide better treatment for the patients.

Cyber-physical systems (CPSs) for healthcare are also known as medical cyberphysical systems (MCPSs) and can be thought of as mechanisms that integrate physical and computational methods. They are deployed via monitoring of computer-based algorithms fused with the Internet. They also serve as a bridge between the virtual and physical worlds [1]. These new-generation systems are combined engineered systems consisting of electrical technology, communications technology, information technology and control systems. CPSs play important roles in the area of healthcare and information systems. There are numerous applications utilizing CPS, such as healthcare, traffic management systems, information systems and transportation systems. CPSs have revolutionized many areas such as healthcare monitoring, responses to emergency situations and delivery systems. CPSs integrate physical and logical areas by putting digitized, physical and human elements together in unified processes. CPSs includes technologies such as the Internet of Things (IoT), smart cities, smart healthcare and, indeed, ‘smart’ anything (buildings, vehicles etc.)

In the second century, the Sushruta Samhita text offered the most comprehensive explanation of health. A similar definition of health, given by the World Health Organization (WHO) in 1948, states that

Data Cleaning is a process to improve the quality of the data by adding few factors like removing missing values from the database, removing delicacy, correcting inconsistent data, data acquit ion ...

The aim of data mining is to extract useful information from huge source of multiple data. But during the process of data mining, intentionally or unintentionally the data becomes visible and thus ...

The study of emotions is very tough job which sounds in the form to recognize the facial expressions of human face. This problem is quite challenging because of various face architecture of the fac...

In this digital era consumers prefer online shopping to save time. But in their busy schedules it can be time-consuming to choose items as per their requirements. There is a need to have a smart shopping cart that gets filled with products as per the customer’s criteria in lesser time. To adhere this issue, this paper proposed an expert system where the shopping cart management is considered as a Knapsack Problem. The Shopping Cart Management is considered as a 0/1 Knapsack Problem, where the problem is to prepare an optimized shopping cart as per the customer requirement bounded with the budget. This problem is solved through the procedure based Expert System which uses the Dynamic Programming approach.

With the advent of Artificial Intelligence, Supercomputers and Smart devices, the education community is witnessing an unprecedented era of technological and innovational revolution. The prevalence of intelligent 'robots' in lieu of the teacher in the class will become a reality in most schools if teachers start believing that these machines may respond to or adjust their responses according to the behavioral and emotional state of the learner. Albeit the degree of complexity and sophistication of the computer or supercomputer driving this intelligence, the machine can never be equated to the inherent intelligence of an average intelligent human being. Therefore, the human teacher will continue to have a say in the learning process of his/her learner This paper is aimed at presenting a realistic, robust and sustainable alternative to technology-supported teaching and learning in education.

With the rapid advancement of technology, everything is transforming into smarter versions. The term smart city means a technologically strengthened and advanced version of the city. Smart cities utilize digital information and techniques for improving services like performance, quality, etc. Big data technology and methods are utilized for handling the vast volume, high velocity and wide variety of data related to cities. This chapter discusses the big data utilization for making smart cities and also throws light on various applications where efficient analysis of services can be carried out using Big Data techniques. The main objective of this chapter will be to provide knowledge of big data implementation for the smart city and its services. This chapter will also investigate various prospects, benefits, and challenges of absorbing big data utilization for smart cities. It will also discuss some case studies related to big data applications for smart city services. It will also propose some open issues related to big data implementation for the smart city.

Currently, the immense growth of internet usage has become a bottleneck situation for web developers to meet the customer requirements. To analyse this changing scenario, the developers need to mee...

Wearable metal objects with high electromagnetic reflection characteristics can cause interference with the incident waves during exposure to the electromagnetic (EM) radiation. Therefore, it is of interest to investigate the effect of metal objects capable of increasing the absorption of EM energy and temperature within the tissues when get exposed to EM radiation. A numerical analysis of increase in specific absorption rate (SAR) and temperature distribution in a human head model when metal objects are placed between the head and radiating source is performed. A realistic three-dimensional heterogeneous human head model, metal objects of different shapes and sizes, and spectacles with different lenses are used. A half-wavelength dipole antenna operating at 1800 MHz served as an EM radiation source. Results show that the presence of metal objects in proximity to the head alters SAR and temperature increase within the tissues. In most cases, metal objects redistribute the EM field incident upon them to a smaller region increasing power absorption, thereby increasing SAR and temperature in that region. The power absorption in head layers is found to be sensitive to metal object's size and shape, and distance of the antenna from the objects.

In recent years, continuous progress in wireless communication has opened a new research field in computer networks. Now a day"s wireless ad-hoc networking is an emerging research technology that needs attention of the industry people and the academicians. A vehicular ad-hoc network uses vehicles as mobile nodes to create mobility in a network. Simulation is the reproduction of the method of real-world practices. The computer simulation runs on a single or a network of computers to model and reproduce the behaviour of a system. This is based upon the conceptual model to simulate the system. In this research paper, we will discuss the coupling simulator VanetMobiSim and NS2 for vehicular ad-hoc networks. This output will be useful in implementing efficient tools on the realistic highway scenario, especially for four-wheeler traffic types in VANETs. Index Terms—VANETs, NS-2, CanuMobiSim, VanetMobiSim, TCL, AWK.

"With the development of high throughput sequencing technology, massive amount of genomic data are generated every day. To efficiently handle these data for storage, processing and transmission, some specialized genomic data compression strategies are need of today. In this paper a hybrid approach based on substitution and statistics is proposed for genome compression (WBSC).WBSC can handle the genomic data files in raw forms as well as Fasta/Multi Fasta formats. WBSC is a loss less algorithm in which searching is possible without fully decompression. Experiments show that proposed algorithm (WBSC) outperform in comparison to other state of art algorithms in terms of compression ratio, compression time and decompression time."

Bio Inspired computation is the part of Artificial intelligence which was inspired by the biological behaviors of biological systems. Swarm intelligence is the collective behavior of an organized group in day-to-day life. Common examples of swarm intelligence include ant colony, bee colony, etc. and some are non-swarm intelligence like bat algorithm, etc. This study mainly focuses on application areas of various bio inspired computing based swarm and non-swarm intelligence. This review also discusses the newly developed algorithms. Specific application areas of such algorithms have been discussed in this research. This research highlighted the future scope of present algorithms

Dining philosophers problem is a typical example of inter process synchronization problem where various resources need to be allocated between different processes to avoid deadlock as well as starvation. There are different solutions are proposed but they have few limitations. This paper proposes an agent based solution wherein an agent will grant the permission to acquire and release the resources (i.e. chopsticks). An agent will be added to the system. The agent will release a TOKEN to a philosopher to begin a state like Eating, Thinking or Waiting. The purpose is to obtain a deadlock and starvation free solution to the problem.

Over decades new technologies, algorithms and methods are evolved and proposed. We can witness a paradigm shift from typewriters to computers, mechanics to mechnotronics, physics to aerodynamics, chemistry to computational chemistry and so on. Such advancements are the result of continuing research; which is still a driving force of researchers. In the same way, the research in the field of artificial intelligence (Russell, Stuart & Norvig, 2003) is major thrust area of researchers. Research in AI have coined different concepts like natural language processing, expert systems, software agents, learning, knowledge management, robotics to name a few. The objective of this chapter is to highlight the research path from software agents to robotics. This chapter begins with the introduction of software agents. The chapter further progresses with the discussion on intelligent agent, autonomous agents, autonomous robots, intelligent robots in different sections. The chapter finally concluded with the fine line between intelligent agents and autonomous robots.

Science & Technology Asia, 22, 3

We investigate the effects of Cr, Ru and Sn substitution on electrical resistivity and magnetoresistance property of polycrystalline samples La0.67Sr0.33Mn1-xBxO3(x = 0, 0.05; B= Ru, Cr and Sn) compounds. The value of M-I transition temperature (TP) decreases while resistivity increases with Cr, Ru and Sn substitution, moreover, the largest low-temperature magnetoresistance (MR %) is found at magnetic field dependent (Isotherm), which suggest that the spin-dependent scattering from internal grain regions is also responsible for the low-temperature MR %. Resistivity data have been fitted with the variable range hopping model to estimate the density of state at Fermi level. It was observed that the substitution of various transition metals in the Mn-site leads to a decrease in conductivity of the doped manganite samples, with conduction being controlled by the disorder induced localization of charge carriers.

The computer industry is getting advanced. In a short span of years, industry is growing high with advanced techniques. Robots have been replacing humans, increasing the efficiency, accessibility and accuracy of the system and creating man-machine interaction. Robotic industry is developing many new trends. However, they still need to be controlled by humans itself. This paper presents an approach to control a motor like a robot with hand gestures not by old ways like buttons or physical devices. Controlling robots with hand gestures is very popular now-a-days. Currently, at this level, gesture features are applied for detecting and tracking the hand in real time. A principal component analysis algorithm is being used for identification of a hand gesture by using open CV image processing library. Contours, convex-hull, and convexity defects are the gesture features. PCA is a statistical approach used for reducing the number of variables in hand recognition. While extracting the most relevant information (feature) contained in the images (hand). After detecting and recognizing hand a servo motor is being controlled, which uses hand gesture as an input device (like mouse and keyboard), and reduces human efforts.

The effect of Mn site doping by small amount Ru (5 %) on the transport and magneto-transport properties of the Ln0.67Sr0.33Mn1−x Ru x O3 (where Ln = La, Pr, and Nd, x = 0, 0.05) has been studied. Polycrystalline samples were synthesized by the conventional solid state reaction method. The samples of Pr0.67Sr0.33Mn1−x Ru x O3 and Nd0.67Sr0.33Mn1−x Ru x O3 have shown single phase with orthorhombic structure as well as La0.67Sr0.33Mn1−x Ru x O3 has rhombohedral are revealed from XRD measurement. The temperature dependence of the resistivity is measured from 5 to 400 K. La0.67Sr0.33Mn0.95Ru0.05O3 shows the metal–insulator (M–I) transition at 379 K while Pr0.67Sr0.33Mn0.95Ru0.05O3 and Nd0.67Sr0.33Mn0.95Ru0.05O3 have shown M–I transition at 294 and 249 K, respectively. The magnetoresistance (MR) was measured with an applied magnetic field of 1, 3 and 5 T. Though the M–I transition temperature decreases marginally, MR up to 70 % has been observed.