The ongoing pandemic, COVID-19, has received unprecedented global attention. Though a pathogen is essentially the causative agent, the dynamics of the cause-effect in major events like the ongoing COVID-19 pandemic is not well understood. It is not even clear if the focused attention of the normal population impacts the safety and health of the exposed victims and in what way the different groups interact with each other, is also a big question. The idea of panpsychism and hierarchical consciousness suggests that consciousness functions beyond an individual and works at distinct levels such as family, community, state, territories, nations, and world. In this perspective, keeping in mind, the idea of hierarchical consciousness, the impact of higher consciousness in major events like the COVID-19 pandemic, on the well-being and fate of the exposed population along with the probable role of quantum entanglement, has been discussed.

INTRODUCTION: Heart diseases are the prominent human disorders that have significantly affected the lifestyle and lives of the victims. Cardiac arrhythmia (heart arrhythmia) is one of the critical heart disorders that reflects the state ofheartbeat among individuals. ECG (Electrocardiogram) signals are commonly used in the diagnostic process of this cardiac disorder.

OBJECTIVES: In this manuscript, an effort has been made to employ and examine the performance of emerging Swarm Intelligence (SI) techniques in finding an optimal set of features used for cardiac arrhythmia diagnosis.

METHODS: A standard benchmark UCI dataset set comprises of 279 attributes and 452 instances have been considered. Five different SI-based meta-heuristic techniques viz. binary Grey-Wolf Optimizer (bGWO), Ant Lion Optimization(ALO), Butterfly optimization algorithm (BOA), Dragonfly Algorithm (DA), and Satin-B ird Optimization(SBO) have been also employed for the same. Additionally, five novel chaotic variants of SBO have been designed to solve the feature selection problem for diagnosing a cardiac arrhythmia. Different performance metrics like accuracy, fitness value, optimal set of features and execution time have been computed.

CONCLUSION: It has been observed from the experimentation that in terms of accuracy and fitness value of cardiac arrhythmia, the SBO outperformed other SI algorithms viz. bGWO, DA, BOA, and ALO. Additionally, BOA and ALO seem to be the best fit when the emphasis is on dimension size only.

INTRODUCTION: Having justice for easy access to health services may probably be a standard feature and indisputable right of all health policies. The health policy of Iran enunciates this right. Unfortunately, as may happen in many countries, the execution of this policy depends on different factors. Among these parameters should be quoted as the suitable distribution of professionals, hospitals, and medical facilities. On the other hand, in Iran, there are many other problems linked to accessing areas with natural hindrances.

OBJECTIVE: In this study, we analyzed the primary studies that report specialty and subspecialty challenges that had been faced by Iran to improve pediatric assistance.

METHODS: Conduction of literature search in PubMed and CINAHL libraries was done, especially studies from 2010 to 2019. A Boolean operated ‘MeSH’ term was used for search. Newcastle–Ottawa Scale (NOS) scoring was adopted to assess the quality of each study.

RESULTS: A total of 118 studies were displayed, and among them, 102 were excluded due to duplication and study relevance. Study selection was madebased on its content were classified into two groups (1) shortage and unsuitable distribution of specialist and sub-specialist physicians in Iran, and (2) studies that explain the status of degradation in different areas of Iran. Outcomes demonstrated that Iran is generally suffering the shortage and unsuitable distribution of specialists and sub-specialists. This lack is particularly crucial in deprived and far-away areas from the cities.

CONCLUSION: The present study analyzed in detail the current data regarding pediatric health services (specialist and subspecialty) and health-specific facilities distribution in the country.

A solution to help victims against phishing is anticipating and leveraging impacts related to phisher actions.In this regard, this work reshapes game theoretical logic between Intrusion Detection System (IDS) agents andinsiders to email spear-phishing interactions. The email spear-phishing attack is designed as a non-cooperativeand repeated game between opponents. Additionally, this work relies on Quantal Response Equilibrium (QRE)to build a game theoretical approach to predict the phisher’s future intent based on past actions of bothplayers. This approach is coupled with a recommendation strategy of appropriate allocation of resources toinvest to strengthen user protection. Simulations on spear-phishing scenarios demonstrate the ability of thefinal system to intuitively guess the most likely phisher decisions. This work provides intelligence to spear-phishing detectors and humans such that they can anticipate next phisher actions.

Due to the ubiquitous nature of mobile devices, they are now considered as an emerging platform for facilitating both teaching and learning experiences. In this paper, we presented a tailored mobile learning system, namely the Integrated English and Programming Language Learning System (IEPLS), which aims at learning English vocabulary before studying programming language concepts. The IEPLS supports programming language learning in three ways; (a) Recommending to learn specific English vocabulary used in programming language concepts (b) Adaptation to the learning flow of the learner and (c) Motivating and encouraging learners to learn items based on individual learner's performance. The IEPLS was used by one hundred and fifty undergraduate students for six months. Evaluation results revealed the attainment of IEPLS in supporting learners in learning programming languages backed by precise English vocabulary.

In the present situation, it is worthy for all that the computerized information for example big data is quickly growing in all requests and turning out to be difficulties in convenience forms. Age of the valuable data from the multiplied information is a fascinating procedure might be called as preparing of the information. Presently a day's prepared informational collectionshave an imperative situation in discovering information through machine learning. The Authors may need to discover new thoughts of machine learning or profound learning methods for machine in the field of preparing information for superior figuring. This paper speaks to the review of different machine procedures or strategies applied before train data sets for information extraction of information in enormous information investigation to improve performance computing like cloud computing or grid computing. This paper could be as starting point and base of examination and has a key an incentive in the field of machine learning.

As we move into a more digitized society, the collection and use of data continues to increase. This influx in data, partnered with challenges complying with data protection and privacy regulations and the absence of a comprehensive global data protection and privacy strategy, has contributed to data breaches and data misusage. In order to reduce these incidents, updates must be made to existing regulations and included in future regulations. A global agency should also be created to identify the main data protection and privacy objectives to develop a comprehensive strategy and oversee data protectionand privacy. Our paper presents an overview of existing data protection and privacy regulations, the challenges of complying with the regulations, and recommendations to achieve long-term data protect and privacy.

The advances in 5G, 6G, artificial intelligence (AI), edge computing, are expected to accelerate the future Internet of Things (FIoT) by providing intelligent, secure, and automatic connectivity for IoT services. However, there are still a number of challenges in FIoT needs to be addressed. This editorial introduces four key techniques solutions in 5G/6G communications, network model, network managements, and IoT applications in education.

Emergency service is the most important research field for welfare of human kind. Some examples are ambulances and fire control truck. However, these conventional emergency services are not equipped with appropriate equipment to provide high QoS. For instance, conventional medical emergency services include ambulances with drivers and oxygen supplies only. Another example is the conventional fire control systems that only include fire control truck, which is not suitable for current scenario due to various challenges, for instance, road traffic. In addition, accidental services are unsatisfactory in current and future emergency services. Emergency service is required by anyone at anytime and anywhere. Therefore, it is the time to redefine and restructure the conventional emergency service for saving more lives. Due to the advent of wireless communication technology, it is possible to provide the emergency service on the spot in nearly real-time to save lives. Moreover, integration of Artificial Intelligence with smart devices can change the definition of emergency services. In this chapter, we envision the various emergency services to advance the healthcare services. Besides, we envision the impact of emergency services on healthcare with the help of Artificial Intelligence, 5G and 6G communication technology.

INTRODUCTION: Pneumonia is most significant disease in today’s world. It resulted around 15 % of the total deaths of children of the same age group.

OBJECTIVES: This paper proposes Depth Wise Convolution Neural Network (DW-CNN) using the SWISH Activation and Transfer Learning (VGG16) to reliably diagnose pneumonia.

METHODS: The proposed model contains 10 layers of convolutional neural networks. Also, three dense layers with the Swish activation function with a dropout of 0.7 and 0.5 respectively in each layer. The model was trained on 5216 augmented with weighted contrast and brightened radiograph Images and tested on 624 radiogram images using Deep Learning and Transfer Learning (VGG16).

RESULT: The model was trained on 5216 augmented radiograph Images and tested on 624 radiogram images using Deep Learning and Transfer Learning (VGG16) and the final results obtained with training accuracy of 98.5%, testing accuracy of 79.8% and validation accuracy of 75%.

CONCLUSION: The model can be improved by using different transfer learning models and hyperparameter tuning parameters.