Sufi Method of Treatment & Physical Illness Healing in Hindu Pak Sufis

Every aspect of human experience, including health and illness, has a spiritual component. Spirituality is now recognized as one of the key factors influencing health, and it is no longer just the domain of mysticism and religion. Spirituality has become a focus of neuroscience study in recent years, and it appears to have great promise for improving therapeutic therapies as well as our understanding of psychiatric morbidity. Sufism has been a well-known spiritual movement in Islam, drawing inspiration from major world faiths like Christianity and Hinduism and making a significant contribution to the spiritual health of many people both inside and outside the Muslim world.Sufism began in the early days of Islam and had many notable Sufis, but it wasn’t until the mediaeval era that it rose to its greatest height, culminating in a number of Sufi groups and its leading proponents. The Sufism promotes God as the sole source of genuine existence as well as the cause of all existence, and it seeks communication with God through spiritual realization, with the soul serving as the medium for this communion. It might offer a crucial connection for comprehending the origin of religious experience and how it affects mental health. In this connection author has attempted to address the Sufi of 18 century to 19 century, well-known Sufi Sain baba RA was benefited by haji Ali shah Buskhari

Random neural network based epileptic seizure episode detection exploiting electroencephalogram signals

Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation

Portable UWB RADAR Sensing System for Transforming Subtle Chest Movement into Actionable Micro-Doppler Signatures to Extract Respiratory Rate Exploiting ResNet Algorithm

Contactless or non-invasive technology for the monitoring of anomalies in an inconspicuous and distant environment has immense significance in health-related applications, in particular COVID-19 symptoms detection, diagnosis, and monitoring. Contactless methods are crucial specifically during the COVID-19 epidemic as they require the least amount of involvement from infected individuals as well as healthcare personnel. According to recent medical research studies regarding coronavirus, individuals infected with novel COVID-19-Delta variant undergo elevated respiratory rates due to extensive infection in the lungs. This appalling situation demands constant real-time monitoring of respiratory patterns, which can help in avoiding any pernicious circumstances. In this paper, an Ultra-Wideband RADAR sensor “XeThru X4M200” is exploited to capture vital respiratory patterns. In the low and high frequency band, X4M200 operates within the 6.0-8.5 GHz and 7.25-10.20 GHz band, respectively. The experimentation is conducted on six distinct individuals to replicate a realistic scenario of irregular respiratory rates. The data is obtained in the form of spectrograms by carrying out normal (eupnea) and abnormal (tachypnea) respiratory. The collected spectrogram data is trained, validated, and tested using a cutting-edge deep learning technique called Residual Neural Network or ResNet. The trained ResNet model’s performance is assessed using the confusion matrix, precision, recall, F1-score, and classification accuracy. The unordinary skip connection process of the deep ResNet algorithm significantly reduces the underfitting and overfitting problem, resulting in a classification accuracy rate of up to 90%

Quality of Underground Water of Tehsil Khanewal- An Overview

Agriculture is the back bone of Pakistan’s economy of Pakistan with 21 % contribution to GDP and providing livelihood to about 45 % of the total labor force of the country. The industry of Pakistan is mainly agro based (Economic survey of Pakistan, 2009-10). Due to change in climate and thereby extended drought, surface water resources of the country had reduced by 70% in 2003, compared with normal years (Kahlown et al., 2003). Unfortunately, canal water is not sufficient to meet requirements of soil and crop under intensive cropping system. A water quality study has shown that out of 560,000 tube wells in Indus Basin, about 70% are pumping sodic water which in turn is affecting the soil health and crop yield (Kahlown et al., 2003).The ground waters of different areas and depths have different types of salts which deteriorate the soil accordingly (Masood and Gohre, 2000). It is also reported that 73.38% (681) of the 922 water samples analyzed by the soil and water laboratory Vehari during the year 2006-07, were unfit for irrigation purpose, while 11.93% (110) were marginally fit and only14.21 (131) were found fit for irrigation purpose (Ashraf et al., 2008). According to the estimates, discharge of 50-60 % of the existing wells was brackish in nature (Ashraf et al., 2009) and still more formidable figures of Lahore district declaring that groundwater of 76.6% villages of the district was detrimental for crops and soil health (Ali et al., 2009).According to Shakir et al. (2002), 64 water samples were collected from new tube well bores from various locations of district Kasur to check the quality of under-groundwater for irrigation purpose. The results show that electrical conductivity of the samples varied from 524 to 5700 μS cm-1, sodium adsorption ration of the samples ranged from 0.49 to 26.00, while residual sodium carbonate ranged from 0.00 to 17.00 meL-1. Out of 64 samples, 26 samples were fit, 8 marginally fit and 30 unfit for irrigation.The successful crop production on sustainable basis, mainly depends on the quality of groundwater. The common characteristics considered are electrical conductivity (EC), sodium adsorption rations (SAR) and residual sodium carbonate (RSC) (Idris and Shafiq, 1999). The concentration and composition of dissolved constituents in water determine its quality for irrigation use. It is difficult to define the critical limits of EC, RSC and SAR because the effect of different qualities of water of soil health and crop yield is also governed by the type of soil, climate and management practices (Singh et al., 1992).Gravity of the situation of groundwater of the majority districts of Pakistan implies that something will have to be done without further loss of time to prevent the rapid conversion of productive fertile lands of Pakistan into unproductive barren lands. Besides, making investment on creating awareness among farming community about bio-saline technology/ saline agriculture by the private and public sectors, a watchful eye on the quality and quantity of ground water of every district of Pakistan by all the stakeholders and timely tackling the detrimental impact of brackish groundwater by using the available technology to the possible extent is imperative

Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication

Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person's body. The Wi-Fi signals received using non-wearable devices are converted into time-frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%

Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using Modified Logistic and Dynamic Newton Leipnik Maps

The health status of an elderly person can be identified by examining the additive effects of aging along disease linked to it and can lead to the ’unstable incapacity’. This health status is essentially determined by the apparent decline of independence in Activities of Daily Living (ADLs). Detecting ADLs provide possibilities of improving the home life of elderly people as it can be applied to fall detection systems.. This article looks at Radar images to detect large scale body movements. Using a publicly available Radar spectogram dataset, Deep Learning and Machine Learning techniques are used for image classification of Walking, Sitting, Standing, Picking up Object, Drinking Water and Falling Radar spectograms. The Machine Learning algorithm used were Random Forest, K Nearest Neighbours and Support Vector Machine. The Deep Learning algorithms used in this article were Long Short Term Memory, Bi-directional Long Short-Term Memory and Convolutional Neural Network. In addition to using Machine Learning and Deep Learning on the spectograms, data processing techniques such as Principal Component Analysis and Data Augmentation is applied to the spectogram images. The work done in this article is divided into 4 experiments. The first experiment applies Machine and Deep Learning to the the Raw images data, the second experiment applies Principal Component Analysis to the Raw image Data, the third experiment applies Data Augmentation to the Raw image data and the fourth and final experiment applies Principal Component Analysis and Data Augmentation to the Raw image data. The results obtained in these experiments found that the best results were obtained using the CNN algorithm with Principal Component Analysis and Data Augmentation together to obtain a result of 95.30 % accuracy. Results also showed how Principal Component Analysis was most beneficial when the training data was expanded by augmentation of the available data

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy

Design of Portable Exoskeleton Forearm for Rehabilitation of Monoparesis Patients Using Tendon Flexion Sensing Mechanism for Health Care Applications

Technology plays a vital role in patient rehabilitation, improving the quality of life of an individual. The increase in functional independence of disabled individuals requires adaptive and commercially available solutions. The use of sensor-based technology helps patients and therapeutic practices beyond traditional therapy. Adapting skeletal tracking technology could automate exercise tracking, records, and feedback for patient motivation and clinical treatment interventions and planning. In this paper, an exoskeleton was designed and subsequently developed for patients who are suffering from monoparesis in the upper extremities. The exoskeleton was developed according to the dimensions of a patient using a 3D scanner, and then fabricated with a 3D printer; the mechanism for the movement of the hand is a tendon flexion mechanism with servo motor actuators controlled by an ATMega2560 microcontroller. The exoskeleton was used for force augmentation of the patient’s hand by taking the input from the hand via flex sensors, and assisted the patient in closing, opening, grasping, and picking up objects, and it was also able to perform certain exercises for the rehabilitation of the patient. The exoskeleton is portable, reliable, durable, intuitive, and easy to install and use at any time

Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications

Wireless communication using existing coding models poses several challenges for RF signals due tomultipath scattering, rapid fluctuations in signal strength and path loss effect. Unlike existing works, thisstudy presents a novel coding technique based on Analogue Network Coding (ANC) in conjunction withSpace Time Block Coding (STBC), termed as Space Time Analogue Network Coding (STANC). STANCachieves the transmitting diversity (virtual MIMO) and supports big data networks under low transmittingpower conditions. Furthermore, this study evaluates the impact of relay location on smart devices networkperformance in increasing interfering and scattering environments. The performance of STANC is analyzedfor Internet of Things (IoT) applications in terms of Symbol Error Rate (SER) and the outage probabilitythat are calculated using analytical derivation of expression for Moment Generating Function (MGF).In addition, the ergodic capacity is analyzed using mean and second moment. These expressions enableeffective evaluation of the performance and capacity under different relay location scenario. Differentfading models are used to evaluate the effect of multipath scattering and strong signal reflection. Undersuch unfavourable environments, the performance of STANC outperforms the conventional methods suchas physical layer network coding (PNC) and ANC adopted for two way transmission